From 42b4f59d4a141304b3cbed876254dbc4dbfc1072 Mon Sep 17 00:00:00 2001
From: Parth Danve <parth.danve@uconn.edu>
Date: Tue, 11 Aug 2026 16:19:23 -0400
Subject: [PATCH 1/7] AQC+Trotter Tutorial + Template

---
 .../function-template-aqc-trotter.ipynb       | 575 ++++++++++++
 ...on-scattering-with-a-qiskit-function.ipynb | 885 ++++++++++++++++++
 2 files changed, 1460 insertions(+)
 create mode 100644 docs/guides/function-template-aqc-trotter.ipynb
 create mode 100644 docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function.ipynb

diff --git a/docs/guides/function-template-aqc-trotter.ipynb b/docs/guides/function-template-aqc-trotter.ipynb
new file mode 100644
index 00000000000..8e9e3c77d83
--- /dev/null
+++ b/docs/guides/function-template-aqc-trotter.ipynb
@@ -0,0 +1,575 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "frontmatter",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "title: Deploy and run the AQC-Trotter-dynamics Qiskit Function\n",
+    "description: Deploy the AQC + Trotter Hamiltonian dynamics function template to IBM Quantum Serverless, then run it on a simulator and on a QPU.\n",
+    "---\n",
+    "\n",
+    "{/* cspell:ignore Trotter Trotterization quimb cotengra cotengrust Suzuki fidelities isa */}\n",
+    "\n",
+    "# Deploy and run the AQC-Trotter-dynamics Qiskit Function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "overview",
+   "metadata": {},
+   "source": [
+    "## Overview\n",
+    "\n",
+    "This is an experiment-agnostic Qiskit Function for Hamiltonian dynamics. Given a 1D nearest-neighbor Pauli Hamiltonian, a prepared initial state (optional), and a set of observables, it runs Trotter time-evolution, approximate quantum compilation (AQC) circuit compression, and mitigated execution, then returns each observable's time series. Swap the setup (PRE) and the analysis (POST) and the same core drives a different experiment:\n",
+    "\n",
+    "```\n",
+    "   PRE (your setup)            FUNCTION (deployed here)             POST (your analysis)\n",
+    " prepare a state         ->  Trotter -> AQC compress -> execute  ->  S(q, w) (neutron)\n",
+    " (circuit / product)        (statevector / fake / runtime)          magnetization, transport,\n",
+    " + optional local kick      -> <O>(t)                               quench dynamics, ...\n",
+    "```\n",
+    "\n",
+    "The template is published in the Qiskit function templates repository, alongside the other application templates. See: [AQC Dynamics Template](https://github.com/qiskit-community/qiskit-function-templates/tree/main/physics/aqc_trotter). This notebook deploys it to your own IBM Quantum&reg; Serverless account. Run it once, and any notebook can then call the function with `serverless.load(\"aqc-dynamics-function\")`.\n",
+    "\n",
+    "For a worked scientific example, see [Simulate neutron scattering with an AQC-dynamics Qiskit Function](neutron-scattering-with-qiskit-functions.ipynb), which calls this function to compute the dynamical structure factor of KCuF$_3$. This notebook covers deployment and the input contract instead."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "requirements",
+   "metadata": {},
+   "source": [
+    "## Requirements\n",
+    "\n",
+    "Before starting, be sure you have the following in this notebook's kernel environment:\n",
+    "\n",
+    "- Qiskit SDK v2.0 or later (`pip install qiskit`).\n",
+    "- The Qiskit IBM Catalog client (`pip install qiskit-ibm-catalog`), which uploads and runs Qiskit Functions.\n",
+    "\n",
+    "The function's own scientific dependencies (`qiskit-addon-aqc-tensor`, `cotengrust`, `qiskit-aer`) do not need to be installed locally."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "source-files",
+   "metadata": {},
+   "source": [
+    "## Get the template source files\n",
+    "\n",
+    "The function is a small Python package that Qiskit Serverless runs in the cloud, so its source has to exist as local files that are uploaded at deploy time. The package is published in the Qiskit function templates repository.\n",
+    "\n",
+    "Download **[`source_files`](https://download-directory.github.io/?url=https%3A%2F%2Fgithub.com%2Fqiskit-community%2Fqiskit-function-templates%2Ftree%2Fmain%2Fphysics%2Faqc_trotter%2Fsource_files)**\n",
+    "\n",
+    "The download is a single zip, named after the full path of the directory in the repository:\n",
+    "\n",
+    "`qiskit-community qiskit-function-templates main physics aqc_trotter source_files.zip`\n",
+    "\n",
+    "1. Unzip it into the directory that holds this notebook.\n",
+    "2. Rename the extracted folder from that long name to `source_files`.\n",
+    "\n",
+    "Your working directory then looks like this:\n",
+    "\n",
+    "```\n",
+    "your-working-directory/\n",
+    "├── deploy-aqc-dynamics-function.ipynb    <- this notebook\n",
+    "└── source_files/                         <- the renamed folder\n",
+    "    ├── __init__.py\n",
+    "    ├── program.py\n",
+    "    └── source/\n",
+    "        ├── __init__.py\n",
+    "        ├── _serverless.py\n",
+    "        ├── app_function.py\n",
+    "        ├── aqc.py\n",
+    "        ├── build.py\n",
+    "        ├── execute.py\n",
+    "        └── hamiltonian.py\n",
+    "```\n",
+    "\n",
+    "The name has to be exactly `source_files`, because that is the `working_dir` Step 3 uploads.\n",
+    "\n",
+    "`program.py` is the entry point the gateway invokes. Everything under `source/` is the implementation, split by stage: Hamiltonian and Trotter synthesis, AQC compression, and execution. None of it needs editing to run the examples below. Step 3 uploads the whole directory, so repeat that step whenever you change a file."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "auth-md",
+   "metadata": {},
+   "source": [
+    "## 1. Authentication\n",
+    "\n",
+    "Use `qiskit-ibm-catalog` to authenticate to `QiskitServerless` with your API key (token) and CRN (instance), which you can find on the [IBM Quantum Platform](https://quantum.cloud.ibm.com) dashboard. This will allow you to locally instantiate the serverless client to upload or run the selected function:\n",
+    "\n",
+    "```python\n",
+    "from qiskit_ibm_catalog import QiskitServerless\n",
+    "serverless = QiskitServerless(channel=\"ibm_quantum_platform\", token=\"MY_TOKEN\", instance=\"MY_CRN\")\n",
+    "```\n",
+    "\n",
+    "You can optionally use `save_account()` to save your credentials in your local environment (see the [Set up your IBM Cloud account](/docs/guides/cloud-setup#cloud-save) guide). Note that this writes your credentials to the same file as [`QiskitRuntimeService.save_account()`](/docs/api/qiskit-ibm-runtime/qiskit-runtime-service#save_account):\n",
+    "\n",
+    "```python\n",
+    "QiskitServerless.save_account(channel=\"ibm_quantum_platform\", token=\"MY_TOKEN\", instance=\"MY_CRN\")\n",
+    "```\n",
+    "\n",
+    "If the account is saved, there is no need to provide the token to authenticate:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "auth-code",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit_ibm_catalog import QiskitServerless\n",
+    "\n",
+    "# Authenticate to the remote cluster\n",
+    "# In this case, loading a saved account\n",
+    "serverless = QiskitServerless()\n",
+    "\n",
+    "# REPLACE WITH YOUR OWN CREDENTIALS or SAVED ACCOUNT\n",
+    "# serverless = QiskitServerless(channel=\"ibm_quantum_platform\", token=\"MY_TOKEN\", instance=\"MY_CRN\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "deps-md",
+   "metadata": {},
+   "source": [
+    "## 2. Declare dependencies\n",
+    "\n",
+    "Packages the function needs on top of the managed base serverless image.\n",
+    "\n",
+    "> The gateway only installs names on its allowlist ([`requirements-dynamic-dependencies.txt`](https://github.com/Qiskit/qiskit-serverless/blob/main/docker-images/requirements-dynamic-dependencies.txt)), matched by package name and pinned to the allowed version with `==`. Anything else must arrive transitively (as a dependency of an allowlisted package). `[extras]` *are* honored — `qiskit-addon-aqc-tensor[quimb-jax]` is what drags `quimb` / `jax` in here. `cotengrust` is needed for memory efficiency during tensor network simulation. `qiskit-aer` is listed separately for the `fake` backend (local noisy simulation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "deps-code",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DEPENDENCIES = [\n",
+    "    \"qiskit-addon-aqc-tensor[quimb-jax]==0.3.1\",\n",
+    "    \"qiskit-aer==0.17.2\",\n",
+    "    \"cotengrust==0.2.0\",\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "upload-md",
+   "metadata": {},
+   "source": [
+    "## 3. Define and upload the function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "upload-code",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit_ibm_catalog import QiskitFunction\n",
+    "\n",
+    "fn = QiskitFunction(\n",
+    "    title=\"aqc-dynamics-function\",\n",
+    "    entrypoint=\"program.py\",\n",
+    "    working_dir=\"source_files/\",\n",
+    "    dependencies=DEPENDENCIES,\n",
+    ")\n",
+    "serverless.upload(fn)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "verify-md",
+   "metadata": {},
+   "source": [
+    "## 4. Verify it registered"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "verify-code",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "QiskitFunction(aqc-dynamics-function)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(p for p in serverless.list() if p.title == \"aqc-dynamics-function\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "reference-md",
+   "metadata": {},
+   "source": [
+    "## Function reference\n",
+    "\n",
+    "This is a brief introduction. Every field is documented in full in the [AQC Dynamics Template README](https://github.com/qiskit-community/qiskit-function-templates/blob/main/physics/aqc_trotter/README.md): the complete inputs table with its validation rules, the output fields, the execution backends, and further worked examples. What follows is the short version, enough to read the examples below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "inputs-md",
+   "metadata": {},
+   "source": [
+    "### Inputs\n",
+    "\n",
+    "Every run is a single `fn.run(...)` call. Only the first three inputs below are required: `hamiltonian`, `t_steps`, and `aqc_segments`. Everything after them is optional and falls back to the default shown, so a minimal call is three arguments and the rest of the table is the functionality you can opt into. The Hamiltonian's `num_qubits` sets the chain length, so there is no separate size input.\n",
+    "\n",
+    "| Input | Default | Description |\n",
+    "|---|---|---|\n",
+    "| `hamiltonian` | required | 1D nearest-neighbor Pauli Hamiltonian as a `SparsePauliOp`. Strings are Pauli operators, so there is no implicit factor of one half. |\n",
+    "| `t_steps` | required | Total Trotter steps. Evolves to `T = t_steps * dt` and reports every observable at each `t_k = k * dt`. |\n",
+    "| `aqc_segments` | required | Compression plan: a list of `{\"n_steps\": k, \"ansatz_steps\": m}`. `sum(n_steps)` steps are compressed; the rest run as plain Trotter. |\n",
+    "| `dt` | `0.2` | Physical time advanced by one Trotter step. |\n",
+    "| `initial_state` | `\\|0...0>` | A prepared `QuantumCircuit` to evolve. Bake any local kick into this circuit. |\n",
+    "| `observables` | per-site `Z` | Anything `EstimatorV2` accepts as its `observables` argument. One observable per output column. |\n",
+    "| `trotter_options` | 2nd-order Suzuki | `{\"method\": ..., \"synthesis_settings\": {...}}`. `reps` and `time` are owned by the function. |\n",
+    "| `aqc_options` | see below | `max_bond` (`32`), `cutoff` (`1e-8`), `autodiff_backend` (`\"jax\"`), `fidelity_target` (`None`), `optimizer_settings` (L-BFGS-B, `jac=True`, `maxiter=300`). |\n",
+    "| `estimator_options` | DD, twirling, TREX | `EstimatorV2.options`, passed through as-is. A supplied dictionary replaces the defaults wholesale rather than merging into them. |\n",
+    "| `transpiler_options` | `{\"optimization_level\": 3}` | `generate_preset_pass_manager` keyword arguments. `backend` and `target` are rejected, since the execution path owns them. |\n",
+    "| `backend` | `\"runtime\"` | `\"statevector\"`, `\"fake\"`, or `\"runtime\"`. |\n",
+    "| `backend_name` | least busy | IBM backend name for `runtime`, or a named fake backend. |\n",
+    "| `batches` | `1` | Split the circuits across N runtime jobs. One batch submits a single job and creates no session. |\n",
+    "| `parallel_sim` | `False` | Fan the local simulator paths across all available cores with Ray. No effect on `runtime`. |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "backends-md",
+   "metadata": {},
+   "source": [
+    "### Execution backends\n",
+    "\n",
+    "All three paths share the same code and the same mitigation settings. They differ only in where the circuits run.\n",
+    "\n",
+    "| `backend` | What it is | Credentials | Notes |\n",
+    "|---|---|---|---|\n",
+    "| `\"statevector\"` | Exact `StatevectorEstimator` | Serverless account only | The exact reference path. No QPU time. |\n",
+    "| `\"fake\"` | Noisy local simulation on a Qiskit fake backend | Serverless account only | A faithful rehearsal of the mitigated `runtime` path. Needs `qiskit-aer`. Defaults to the 127-qubit `fake_sherbrooke`. |\n",
+    "| `\"runtime\"` (default) | The mitigated `EstimatorV2` against a real QPU | Yes | `backend_name` optional; omitting it selects the least busy device. |\n",
+    "\n",
+    "Both simulator paths still call the deployed function, so they need a saved Serverless account even though they use no QPU time. The two examples below run the same workload on `statevector` first, then on `runtime`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "output-md",
+   "metadata": {},
+   "source": [
+    "### Output\n",
+    "\n",
+    "`job.result()` returns a plain dictionary:\n",
+    "\n",
+    "```python\n",
+    "{\n",
+    "    \"times\": [...],                  # length t_steps + 1, t_k = k * dt (t=0 is the prepared state)\n",
+    "    \"expectation_values\": [[...]],   # shape (n_times, n_observables)\n",
+    "    \"observable_labels\": [...],      # e.g. [\"Z_0\", \"ZZ_0_1\"]\n",
+    "    \"metadata\": {\n",
+    "        \"n\", \"t_steps\", \"dt\", \"tier\",\n",
+    "        \"aqc_compressed_steps\": 5,   # total compressed steps (= sum of segment n_steps)\n",
+    "        \"aqc_segments\": [            # per segment: the plan plus its own results\n",
+    "            {\"n_steps\": 3, \"ansatz_steps\": 1, \"steps\": [1, 2, 3], \"n_params\": 133,\n",
+    "             \"fidelities\": {1: ..., 2: ..., 3: ...}},\n",
+    "            {\"n_steps\": 2, \"ansatz_steps\": 2, \"steps\": [4, 5], \"n_params\": 245,\n",
+    "             \"fidelities\": {4: ..., 5: ...}},\n",
+    "        ],\n",
+    "        \"execution_backend\",\n",
+    "        \"aqc_fidelities\": {1: ..., 2: ...},  # flat per-step fidelity, all compressed steps\n",
+    "        \"circuit_stats\": {           # per-step 2q depth and gate count, full Trotter vs AQC\n",
+    "            1: {\"full_trotter\": {\"depth_2q\": ..., \"num_2q_gates\": ...},\n",
+    "                \"aqc_trotter\":  {\"depth_2q\": ..., \"num_2q_gates\": ...}},\n",
+    "            2: {...},\n",
+    "        },\n",
+    "        \"warnings\": [...],           # non-fatal notices, e.g. a cotengrust fallback\n",
+    "        \"resource_usage\": {          # per stage; QPU_TIME is the charged QPU time\n",
+    "            \"RUNNING: OPTIMIZING_FOR_HARDWARE\": {\"CPU_TIME\": ...},\n",
+    "            \"RUNNING: WAITING_FOR_QPU\": {\"CPU_TIME\": ...},\n",
+    "            \"RUNNING: EXECUTING_QPU\": {\"QPU_TIME\": ...},\n",
+    "        },\n",
+    "    },\n",
+    "}\n",
+    "```\n",
+    "\n",
+    "`aqc_fidelities` and `circuit_stats` are the two to read first: together they tell you whether the compression stayed faithful and whether it actually saved depth. On `runtime`, `resource_usage` reports the queue wait separately from the QPU time you are charged for. A rejected input fails fast as a structured `ServerlessError` (code `4615`)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "sim-md",
+   "metadata": {},
+   "source": [
+    "## Simulator example\n",
+    "\n",
+    "Run the function on the exact `statevector` backend first. It spends no QPU time and validates the deployment end to end. The model here is an 8-qubit transverse-field Ising chain, and `observables` is omitted so the function measures the default per-site $Z$.\n",
+    "\n",
+    "The compression plan is the input worth understanding. Each segment `{\"n_steps\": k, \"ansatz_steps\": m}` compresses `k` consecutive Trotter steps into an ansatz built from an `m`-step Trotter target, and any steps beyond `sum(n_steps)` run as plain Trotter. Early, low-entanglement steps compress well into a shallow 1-layer ansatz; later, more-entangled steps need a deeper one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "sim-code",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "job id: ee1f3793-e995-427d-81d1-5924549beb38\n"
+     ]
+    }
+   ],
+   "source": [
+    "from qiskit.quantum_info import SparsePauliOp\n",
+    "\n",
+    "fn = serverless.load(\"aqc-dynamics-function\")\n",
+    "\n",
+    "n = 8\n",
+    "H = SparsePauliOp.from_sparse_list(\n",
+    "    [(\"ZZ\", [i, i + 1], 1.0) for i in range(n - 1)]\n",
+    "    + [(\"X\", [i], 0.8) for i in range(n)],\n",
+    "    num_qubits=n,\n",
+    ")\n",
+    "\n",
+    "job = fn.run(\n",
+    "    t_steps=8,\n",
+    "    aqc_segments=[\n",
+    "        {\"n_steps\": 4, \"ansatz_steps\": 1},  # early steps -> shallow 1-layer ansatz\n",
+    "        {\"n_steps\": 2, \"ansatz_steps\": 2},  # later steps -> deeper 2-layer ansatz\n",
+    "    ],\n",
+    "    hamiltonian=H,\n",
+    "    aqc_options={\"max_bond\": 32},\n",
+    "    backend=\"statevector\",\n",
+    ")\n",
+    "print(\"job id:\", job.job_id)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "follow-md",
+   "metadata": {},
+   "source": [
+    "### Follow the run and read the result\n",
+    "\n",
+    "`status()` reports both the coarse job lifecycle and the per-stage sub-status the function publishes as it runs. The same stages apply to the hardware run below:\n",
+    "\n",
+    "`QUEUED -> INITIALIZING -> RUNNING: OPTIMIZING_FOR_HARDWARE -> RUNNING: WAITING_FOR_QPU -> RUNNING: EXECUTING_QPU -> RUNNING: POST_PROCESSING -> DONE`\n",
+    "\n",
+    "| `status()` value | Stage |\n",
+    "|---|---|\n",
+    "| `RUNNING: OPTIMIZING_FOR_HARDWARE` | state prep, Trotter build, AQC compression |\n",
+    "| `RUNNING: WAITING_FOR_QPU` | queued on the QPU (`runtime` backend only) |\n",
+    "| `RUNNING: EXECUTING_QPU` | circuits executing (local sims mark this directly) |\n",
+    "| `RUNNING: POST_PROCESSING` | assembling the result dictionary |\n",
+    "\n",
+    "Terminal states are `DONE`, `ERROR`, and `CANCELED`. This `statevector` run has no QPU queue, so it skips `RUNNING: WAITING_FOR_QPU`. Use `job.logs()` at any point to see the per-stage logs, including the AQC fidelity reached at each step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "status-code",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DONE\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(job.status())  # re-run until this reports DONE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "result-code",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "observable labels: ['Z_0', 'Z_1', 'Z_2', 'Z_3', 'Z_4', 'Z_5', 'Z_6', 'Z_7']\n",
+      "times: [0.0, 0.2, 0.4, 0.6000000000000001, 0.8, 1.0, 1.2000000000000002, 1.4000000000000001, 1.6]\n",
+      "AQC fidelities: {'1': 1.0, '2': 1.0, '3': 1.0, '4': 1.0, '5': 1.0, '6': 0.9999}\n"
+     ]
+    }
+   ],
+   "source": [
+    "result = job.result()\n",
+    "print(\"observable labels:\", result[\"observable_labels\"])\n",
+    "print(\"times:\", result[\"times\"])\n",
+    "print(\n",
+    "    \"AQC fidelities:\",\n",
+    "    {k: round(v, 4) for k, v in result[\"metadata\"][\"aqc_fidelities\"].items()},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "hw-md",
+   "metadata": {},
+   "source": [
+    "## Hardware example\n",
+    "\n",
+    "A function call with `backend=\"runtime\"` transpiles and executes on a real IBM Quantum processor, with the function's built-in error mitigation: dynamical decoupling (XY4), gate twirling, and twirled readout error extinction (TREX). `backend_name` selects the device; omit it and the function takes the least busy one.\n",
+    "\n",
+    "Nothing about the science code changes. Only the chain length, the number of Trotter steps, and the backend differ from the simulator example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "hw-code",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit.quantum_info import SparsePauliOp\n",
+    "\n",
+    "fn = serverless.load(\"aqc-dynamics-function\")\n",
+    "\n",
+    "n = 10\n",
+    "H = SparsePauliOp.from_sparse_list(\n",
+    "    [(\"ZZ\", [i, i + 1], 1.0) for i in range(n - 1)]\n",
+    "    + [(\"X\", [i], 0.8) for i in range(n)],\n",
+    "    num_qubits=n,\n",
+    ")\n",
+    "\n",
+    "job = fn.run(\n",
+    "    t_steps=10,\n",
+    "    aqc_segments=[\n",
+    "        {\"n_steps\": 3, \"ansatz_steps\": 1},\n",
+    "        {\"n_steps\": 3, \"ansatz_steps\": 2},\n",
+    "    ],\n",
+    "    hamiltonian=H,\n",
+    "    backend=\"runtime\",\n",
+    "    backend_name=\"ibm_boston\",\n",
+    ")\n",
+    "print(\"job id (save this to reconnect later):\", job.job_id)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "hw-reconnect-md",
+   "metadata": {},
+   "source": [
+    "<Admonition type=\"note\" title=\"Reconnecting to a long-running job\">\n",
+    "\n",
+    "A hardware run is not quick, and most of the time is classical rather than on the QPU. The AQC compression runs inside the function before anything reaches the QPU, and the QPU queue is on top of that. You do not need to keep this notebook or kernel open while it runs.\n",
+    "\n",
+    "Copy the job id printed above and save it. The next three cells let you pick the run back up later:\n",
+    "\n",
+    "1. Reconnect, only needed in a new kernel session: re-run the [Authentication](#1-authentication) cell to recreate `serverless`, then rebuild the `job` handle from the id you saved. Skip this cell if you are still in the session where you submitted, because the handle is already live.\n",
+    "2. Check status: re-run until it reports `DONE`.\n",
+    "3. Fetch the result: run only once the status is `DONE`.\n",
+    "\n",
+    "Paste your saved id over the placeholder in the reconnect cell below.\n",
+    "\n",
+    "</Admonition>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "hw-reconnect",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Reconnect to a previously submitted job by its id. Only needed in a NEW kernel\n",
+    "# session; if you are still in the session where you submitted, the `job` handle\n",
+    "# above is already live, so skip this cell. Replace the id below with your own.\n",
+    "job = serverless.get_job_by_id(\"<your job id>\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "hw-status",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Re-run this until it reports DONE, then fetch the result below.\n",
+    "print(job.status())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "hw-result-code",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Run this only once the status cell above reports DONE. result() blocks until\n",
+    "# the job finishes, so calling it earlier just waits.\n",
+    "result = job.result()\n",
+    "print(\"execution backend:\", result[\"metadata\"][\"execution_backend\"])\n",
+    "print(\n",
+    "    \"AQC fidelities:\",\n",
+    "    {k: round(v, 4) for k, v in result[\"metadata\"][\"aqc_fidelities\"].items()},\n",
+    ")\n",
+    "print(\"resource usage:\", result[\"metadata\"][\"resource_usage\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "nextsteps",
+   "metadata": {},
+   "source": [
+    "## Next steps\n",
+    "\n",
+    "<Admonition type=\"tip\" title=\"Recommendations\">\n",
+    "\n",
+    "- Work through [Simulate neutron scattering with an AQC-dynamics Qiskit Function](neutron-scattering-with-qiskit-functions.ipynb), the companion example that calls this deployed function to compute the dynamical structure factor of KCuF$_3$.\n",
+    "- Read the [AQC Dynamics Function Template Github](https://github.com/qiskit-community/qiskit-function-templates/blob/main/physics/aqc_trotter/) for the complete input and output contract, further examples, and citation details.\n",
+    "- Browse the [Qiskit function templates repository](https://github.com/qiskit-community/qiskit-function-templates/tree/main/physics/aqc_trotter) for other application templates built the same way.\n",
+    "- Read the [Qiskit Serverless guide](https://quantum.cloud.ibm.com/docs/en/guides/serverless) for managing deployed functions.\n",
+    "- Go deeper on the AQC compression stage with the [Qiskit addon: AQC-Tensor](https://qiskit.github.io/qiskit-addon-aqc-tensor/) documentation.\n",
+    "\n",
+    "</Admonition>"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "neutron-scattering",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function.ipynb b/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function.ipynb
new file mode 100644
index 00000000000..f38200c2914
--- /dev/null
+++ b/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function.ipynb
@@ -0,0 +1,885 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "frontmatter",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "title: Simulate neutron scattering with an AQC-dynamics Qiskit Function\n",
+    "description: Compute the dynamical structure factor S(q, w) of the quantum magnet KCuF3 by running an AQC-compressed Trotter workflow as a Qiskit Function.\n",
+    "---\n",
+    "\n",
+    "{/* cspell:ignore Trotter Trotterization spinon spinons KCuF quimb DMRG magnon antiferromagnetic Suzuki fidelities isa COBYQA */}\n",
+    "\n",
+    "# Simulate neutron scattering with an AQC-dynamics Qiskit Function\n",
+    "*Usage estimate: 18 minutes on a Heron r3 processor (NOTE: This is an estimate only. Your runtime might vary.)*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "learning-outcomes",
+   "metadata": {},
+   "source": [
+    "## Learning outcomes\n",
+    "\n",
+    "After completing this tutorial, you can expect to understand:\n",
+    "\n",
+    "- How an inelastic neutron-scattering spectrum maps to the dynamical structure factor $S(q, \\omega)$ of a 1D quantum magnet.\n",
+    "- How to prepare the KCuF$_3$ (isotropic Heisenberg) ground state with the density matrix renormalization group (DMRG) and matrix product state (MPS) fidelity maximization.\n",
+    "- How to run Trotter time-evolution, approximate quantum compilation (AQC) circuit compression, and mitigated execution as a single Qiskit Function call.\n",
+    "- How to post-process the per-site $\\langle \\sigma_z \\rangle(t)$ time series into $S(q, \\omega)$ and identify the two-spinon continuum."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "prerequisites",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "\n",
+    "- Familiarity with [Qiskit Patterns](/docs/guides/intro-to-patterns), [`SparsePauliOp`](/docs/api/qiskit/qiskit.quantum_info.SparsePauliOp), and [Trotter time-evolution](/learning/courses/utility-scale-quantum-computing/quantum-simulation).\n",
+    "- Basic exposure to tensor-network methods (DMRG and MPS) is helpful but not required, as is familiarity with the [`qiskit-addon-aqc-tensor`](https://github.com/Qiskit/qiskit-addon-aqc-tensor) library that the function uses to compress Trotter circuits."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "background",
+   "metadata": {},
+   "source": [
+    "## Background\n",
+    "\n",
+    "Inelastic neutron scattering measures the dynamical structure factor $S(q, \\omega)$, the space-and-time Fourier transform of the spin-spin correlation function, so reproducing $S(q, \\omega)$ from a microscopic spin model is a direct, falsifiable test of a quantum simulation. This tutorial studies KCuF$_3$, a spin-$\\frac{1}{2}$ antiferromagnetic Heisenberg chain whose excitations are not single spin flips but pairs of fractionalized spinons: instead of a sharp magnon dispersion, $S(q, \\omega)$ shows a broad *two-spinon continuum*, bounded below by $\\tfrac{\\pi}{2}|\\sin q|$ and above by $\\pi|\\sin(q/2)|$. Those are the dashed curves on the plots below. The physics in full, and the comparison against measured neutron data, are covered in the [original tutorial](/docs/tutorials/simulate-neutron-scattering) and in Lee et al., [arXiv:2603.15608](https://arxiv.org/abs/2603.15608).\n",
+    "\n",
+    "The quantum workflow mirrors the scattering experiment:\n",
+    "\n",
+    "1. Prepare the chain's ground state $|\\psi_0\\rangle$.\n",
+    "2. Kick it with a local perturbation at the center site, a $\\pi/2$ $Z$-rotation, mimicking the momentum and energy transfer from the neutron.\n",
+    "3. Time-evolve under the Heisenberg Hamiltonian, $e^{-iHt}$, with a Trotter product formula.\n",
+    "4. Measure the per-site magnetization $\\langle \\sigma_z^j \\rangle(t)$; as a function of site $j$ and time $t$ this *is* the retarded Green's function $G^R(j, j_c, t)$.\n",
+    "5. Fourier transform $G^R$ into $S(q, \\omega)$.\n",
+    "\n",
+    "The bottleneck is step 3: exact Trotter circuits for long evolutions become too deep for hardware. Approximate quantum compilation with tensor networks (AQC) addresses this by compressing a block of Trotter steps into a fixed, shallow parameterized ansatz whose state fidelity to the exact evolution is maximized classically with an MPS simulator ([arXiv:2301.08609](https://arxiv.org/abs/2301.08609)). The [AQC Dynamics Function](/docs/guides/function-template-aqc-trotter) packages this whole quantum core (Trotter synthesis, AQC compression, and mitigated execution) behind one call:\n",
+    "\n",
+    "```\n",
+    "   PRE (this notebook)          FUNCTION (aqc-dynamics-function)     POST (this notebook)\n",
+    " ground state (DMRG + MPS    ->  Trotter -> AQC compress -> execute -> S(q, w): the dynamical\n",
+    " fidelity max) + neutron kick    (statevector / fake / runtime)        structure factor\n",
+    "                                 -> <sigma_z>(t) per site\n",
+    "```\n",
+    "\n",
+    "So the experiment-specific work stays here in the notebook: ground-state preparation (PRE) and the $S(q, \\omega)$ post-processing (POST). The two quantum-heavy steps, compression and execution, run inside the function.\n",
+    "\n",
+    "This tutorial is a companion to [Simulate neutron scattering in quantum materials with quantum circuits](/docs/tutorials/simulate-neutron-scattering), which builds the same experiment inline: the same KCuF$_3$ model, ground-state preparation, neutron kick, and post-processing, with the Trotter synthesis, AQC compression, and mitigated execution written out step by step. Read that tutorial to learn how AQC compression works. Read this one to run the same experiment through a deployed Qiskit Function: the quantum core becomes a single function call, and the multi-hour AQC compression runs inside the Serverless worker instead of on your machine, so you do not need an HPC system or an open kernel while it runs. Because the function is Hamiltonian-agnostic, the same call also drives other dynamics experiments."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "requirements",
+   "metadata": {},
+   "source": [
+    "## Requirements\n",
+    "\n",
+    "Before starting this tutorial, be sure you have the following:\n",
+    "\n",
+    "- The function deployed to your IBM Quantum&reg; Serverless account. Run the companion function template first: [Deploy and run the AQC dynamics Qiskit Function](/docs/guides/function-template-aqc-trotter). That guide walks through getting the source files and uploading the function to your account. This tutorial only calls the deployed function.\n",
+    "\n",
+    "- IBM Quantum credentials saved for `QiskitServerless` (see the function template). Both examples below call the deployed function, so both need them.\n",
+    "\n",
+    "- Qiskit SDK v2.0 or later (`pip install qiskit`).\n",
+    "\n",
+    "- The Qiskit IBM Catalog client (`pip install qiskit-ibm-catalog`).\n",
+    "\n",
+    "- NumPy, SciPy, and Matplotlib (`pip install numpy scipy matplotlib`). SciPy 1.14 or later is needed for the COBYQA optimizer used in ground-state preparation.\n",
+    "\n",
+    "- The AQC tensor-network stack, because the ground-state preparation in Step 1 runs locally in this notebook: `pip install 'qiskit-addon-aqc-tensor[quimb-jax]==0.3.1'`\n",
+    "\n",
+    "\n",
+    "The first call to a newly deployed function waits while the Serverless worker installs its dependencies, so expect extra latency on that run."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "setup-md",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "Import the libraries and define the experiment-specific helpers used below: `build_gs_ansatz` (the Hamiltonian variational ansatz, or HVA, for ground-state preparation), `prepare_ground_state` (DMRG plus MPS-fidelity maximization), and `get_spectrum`, `plot_green`, and `plot_spectrum` (the $S(q, \\omega)$ post-processing). These are adapted from the [original neutron-scattering tutorial](/docs/tutorials/simulate-neutron-scattering)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "setup-imports",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from functools import partial\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import scipy.optimize\n",
+    "\n",
+    "import quimb.tensor as qtn\n",
+    "from qiskit import QuantumCircuit\n",
+    "from qiskit.quantum_info import SparsePauliOp\n",
+    "from qiskit_addon_aqc_tensor.simulation import tensornetwork_from_circuit\n",
+    "from qiskit_addon_aqc_tensor.simulation.quimb import QuimbSimulator\n",
+    "from qiskit_ibm_catalog import QiskitServerless"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "setup-helpers",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Setup complete - helpers defined.\n"
+     ]
+    }
+   ],
+   "source": [
+    "#  Dynamical structure factor via discrete Fourier transform\n",
+    "\n",
+    "\n",
+    "def get_spectrum(n, Gjjc, dt, time_steps, q_steps, w_steps):\n",
+    "    \"\"\"Compute the dynamical structure factor from the retarded Green's function.\n",
+    "\n",
+    "    Uses the center-site approximation and a discrete Fourier transform.\n",
+    "    \"\"\"\n",
+    "    green = Gjjc / 4  # sigma -> S=1/2\n",
+    "    omega_max = np.pi / dt\n",
+    "    qpoints = np.arange(0, 2 * np.pi, 2 * np.pi / q_steps)\n",
+    "    omegas = np.arange(0, omega_max, omega_max / w_steps)\n",
+    "    green_map = np.zeros((omegas.shape[0], qpoints.shape[0]))\n",
+    "    center = n // 2 - 1\n",
+    "    for iw, w in enumerate(omegas):\n",
+    "        exponent = np.exp(1j * w * dt * np.arange(1, time_steps + 1))\n",
+    "        S_w = np.dot(green.T, exponent) * dt\n",
+    "        for iq, q in enumerate(qpoints):\n",
+    "            q_matrix = np.exp(-1j * q * np.arange(-center, center + 2, 1))\n",
+    "            green_map[iw, iq] = np.imag(np.dot(S_w, q_matrix))\n",
+    "    return green_map\n",
+    "\n",
+    "\n",
+    "#  Plotting helpers\n",
+    "\n",
+    "\n",
+    "def plot_spectrum(\n",
+    "    dsf, dt, q_steps, w_steps, lower_bound=False, upper_bound=False, title=None\n",
+    "):\n",
+    "    \"\"\"Heat-map of the dynamical structure factor.\"\"\"\n",
+    "    omega_max = np.pi / dt\n",
+    "    qpoints = np.arange(0, 2 * np.pi, 2 * np.pi / q_steps)\n",
+    "    omegas = np.arange(0, omega_max, omega_max / w_steps)\n",
+    "    x, y = np.meshgrid(qpoints, omegas)\n",
+    "    fig, ax = plt.subplots(figsize=(8, 5))\n",
+    "    c = ax.pcolormesh(x, y, dsf / np.max(dsf), cmap=\"viridis\", shading=\"auto\")\n",
+    "    fig.colorbar(c, ax=ax, label=\"Normalized intensity\")\n",
+    "    if lower_bound:\n",
+    "        ax.plot(\n",
+    "            qpoints,\n",
+    "            np.pi * np.abs(np.sin(qpoints)) / 2,\n",
+    "            \"--\",\n",
+    "            color=\"white\",\n",
+    "            lw=1.5,\n",
+    "            label=\"Lower bound\",\n",
+    "        )\n",
+    "    if upper_bound:\n",
+    "        ax.plot(\n",
+    "            qpoints,\n",
+    "            np.pi * np.abs(np.sin(qpoints / 2)),\n",
+    "            \"--\",\n",
+    "            color=\"red\",\n",
+    "            lw=1.5,\n",
+    "            label=\"Upper bound\",\n",
+    "        )\n",
+    "    ax.set_ylim(0, 3.6)\n",
+    "    ax.set_xlim(0, 2 * np.pi - 2 * np.pi / q_steps)\n",
+    "    ax.set_xlabel(r\"$q$\", fontsize=16)\n",
+    "    ax.set_ylabel(r\"$\\tilde{\\omega} = \\omega / J$\", fontsize=16)\n",
+    "    ax.set_xticks([0, np.pi / 2, np.pi, 3 * np.pi / 2, 2 * np.pi])\n",
+    "    ax.set_xticklabels([\"0\", r\"$\\pi/2$\", r\"$\\pi$\", r\"$3\\pi/2$\", r\"$2\\pi$\"])\n",
+    "    if lower_bound or upper_bound:\n",
+    "        ax.legend(loc=\"upper right\", fontsize=11)\n",
+    "    if title:\n",
+    "        ax.set_title(title, fontsize=14)\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "def plot_green(n, Gjjc, time_steps, dt, title=None):\n",
+    "    \"\"\"Heat-map of the retarded Green's function in real space and time.\"\"\"\n",
+    "    fig, ax = plt.subplots(figsize=(8, 6))\n",
+    "    t_axis = np.arange(1, time_steps + 1) * dt\n",
+    "    site_axis = np.arange(n)\n",
+    "    x, y = np.meshgrid(t_axis, site_axis)\n",
+    "    c = ax.pcolormesh(\n",
+    "        x, y, np.real(Gjjc).T, cmap=\"RdBu\", vmax=0.5, vmin=-0.5, shading=\"auto\"\n",
+    "    )\n",
+    "    fig.colorbar(c, ax=ax, label=r\"Re $G^R(j, j_c, t)$\")\n",
+    "    ax.set_xlabel(r\"Time  ($t / J^{-1}$)\", fontsize=16)\n",
+    "    ax.set_ylabel(\"Site index $j$\", fontsize=16)\n",
+    "    if title:\n",
+    "        ax.set_title(title, fontsize=14)\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "#  Variational ground-state ansatz (HVA)\n",
+    "\n",
+    "\n",
+    "def _apply_xxz_pair_gate(qc, q0, q1, theta):\n",
+    "    \"\"\"Apply the parameterized XXZ-type two-qubit gate used in the HVA.\"\"\"\n",
+    "    qc.cx(q0, q1)\n",
+    "    qc.rz(theta, q1)\n",
+    "    qc.h(q0)\n",
+    "    qc.rz(theta + np.pi / 2, q0)\n",
+    "    qc.cx(q0, q1)\n",
+    "    qc.rz(-theta, q1)\n",
+    "    qc.h(q1)\n",
+    "    qc.cx(q1, q0)\n",
+    "    qc.rz(np.pi / 2, q1)\n",
+    "    qc.rz(-np.pi / 2, q0)\n",
+    "    qc.h(q1)\n",
+    "    qc.h(q0)\n",
+    "\n",
+    "\n",
+    "def build_gs_ansatz(n, params, layers):\n",
+    "    \"\"\"Build the Hamiltonian variational ansatz (HVA) circuit for\n",
+    "    ground-state preparation of the 1D Heisenberg model.\n",
+    "\n",
+    "    Starts from a product of singlet pairs and applies alternating\n",
+    "    odd/even layers of parameterized XXZ gates. For layer r,\n",
+    "    params[2 * r] is the odd-layer (inter-pair) angle and\n",
+    "    params[2 * r + 1] is the even-layer (intra-pair) angle.\n",
+    "    \"\"\"\n",
+    "    qc = QuantumCircuit(n)\n",
+    "    # Initial singlet product state\n",
+    "    for i in range(n // 2):\n",
+    "        qc.x(2 * i)\n",
+    "        qc.x(2 * i + 1)\n",
+    "        qc.h(2 * i + 1)\n",
+    "        qc.cx(2 * i + 1, 2 * i)\n",
+    "    # Variational layers\n",
+    "    for r in range(layers):\n",
+    "        for i in range(1, (n + 1) // 2):  # odd layer\n",
+    "            _apply_xxz_pair_gate(qc, 2 * i - 1, 2 * i, params[2 * r])\n",
+    "        for i in range(n // 2):  # even layer\n",
+    "            _apply_xxz_pair_gate(qc, 2 * i, 2 * i + 1, params[2 * r + 1])\n",
+    "    return qc\n",
+    "\n",
+    "\n",
+    "def prepare_ground_state(n, gs_layers=5, max_bond=128, cutoff=1e-8):\n",
+    "    \"\"\"Prepare the KCuF3 (isotropic Heisenberg) ground state as a QuantumCircuit.\n",
+    "\n",
+    "    Runs DMRG (quimb MPO + DMRG2) to get the chain's ground state, then optimizes\n",
+    "    the HVA angles to maximize the MPS overlap |<psi_ansatz|psi_DMRG>|^2. No exact\n",
+    "    diagonalization, so it scales to larger n.\n",
+    "    \"\"\"\n",
+    "    J = Jz = 1.0\n",
+    "    builder = qtn.SpinHam1D(S=1 / 2)\n",
+    "    builder += J * 0.5, \"+\", \"-\"\n",
+    "    builder += J * 0.5, \"-\", \"+\"\n",
+    "    builder += Jz, \"Z\", \"Z\"\n",
+    "    H_mpo = builder.build_mpo(L=n)\n",
+    "    dmrg = qtn.DMRG2(H_mpo)\n",
+    "    dmrg.solve(tol=1e-8, verbosity=0)\n",
+    "\n",
+    "    gs_sim = QuimbSimulator(\n",
+    "        quimb_circuit_factory=partial(\n",
+    "            qtn.CircuitMPS, gate_opts=dict(cutoff=cutoff, max_bond=max_bond)\n",
+    "        ),\n",
+    "        autodiff_backend=\"jax\",\n",
+    "    )\n",
+    "\n",
+    "    def gs_infidelity(params):\n",
+    "        psi = tensornetwork_from_circuit(\n",
+    "            build_gs_ansatz(n, params, gs_layers), gs_sim\n",
+    "        ).psi\n",
+    "        return 1 - abs(psi.H @ dmrg.state) ** 2\n",
+    "\n",
+    "    # Seed and optimizer match the original tutorial. Each layer starts at\n",
+    "    # [0, pi/2]: an odd-layer angle of 0 makes the inter-pair gate the identity,\n",
+    "    # and an even-layer angle of pi/2 makes the intra-pair gate a SWAP (since\n",
+    "    # 0.5 * (XX + YY + ZZ) = SWAP - I/2). That puts the seed at the singlet-pair\n",
+    "    # product limit, which is already a decent approximation to the Heisenberg\n",
+    "    # ground state, so the optimizer only has to refine it. The small jitter\n",
+    "    # (fixed RNG seed, so runs are reproducible) breaks the exact symmetry\n",
+    "    # between layers; COBYQA then runs for up to 100 iterations.\n",
+    "    rng = np.random.default_rng(12345)\n",
+    "    x0 = np.tile([0.0, np.pi / 2], gs_layers) + rng.normal(\n",
+    "        scale=0.1, size=2 * gs_layers\n",
+    "    )\n",
+    "    result_gs = scipy.optimize.minimize(\n",
+    "        gs_infidelity, x0, method=\"COBYQA\", options={\"maxiter\": 100}\n",
+    "    )\n",
+    "    print(f\"DMRG ground-state energy: {dmrg.energy:.6f}\")\n",
+    "    print(f\"GS fidelity: {1 - result_gs.fun:.4f}\")\n",
+    "    return build_gs_ansatz(n, result_gs.x, gs_layers)\n",
+    "\n",
+    "\n",
+    "print(\"Setup complete - helpers defined.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "setup-load-md",
+   "metadata": {},
+   "source": [
+    "### Load the Qiskit Function\n",
+    "\n",
+    "Connect to IBM Quantum Serverless and load the deployed `aqc-dynamics-function`. Both examples below call the same `fn` handle, so the function is loaded once, here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "setup-connect",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Credentials are read from the account saved once via QiskitServerless.save_account(...)\n",
+    "serverless = QiskitServerless()\n",
+    "fn = serverless.load(\"aqc-dynamics-function\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "small-md",
+   "metadata": {},
+   "source": [
+    "## Small-scale simulator example\n",
+    "\n",
+    "We first run the full workflow on a small 10-site chain using the exact `statevector` backend. This validates the PRE &rarr; FUNCTION &rarr; POST pipeline before spending any QPU time."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "small-s1-md",
+   "metadata": {},
+   "source": [
+    "### Step 1: Map classical inputs to a quantum problem\n",
+    "\n",
+    "Build the KCuF$_3$ Hamiltonian as a `SparsePauliOp` (isotropic Heisenberg: $XX + YY + ZZ$ at coupling $\\tfrac14$ on each nearest-neighbor bond; the strings are Pauli operators, so $\\tfrac14$ gives the spin-$\\frac{1}{2}$ coupling). Prepare the ground state with DMRG plus MPS-fidelity maximization, then bake in the neutron kick: a $\\pi/2$ $Z$-rotation at the center site. The prepared circuit is what we hand to the function as `initial_state`. We leave `observables` at its default (per-site $Z$), which is exactly the $\\langle \\sigma_z^j \\rangle(t)$ readout the neutron workflow needs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "small-s1-code",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DMRG ground-state energy: -4.258035\n",
+      "GS fidelity: 0.9841\n",
+      "Prepared 10-qubit ground state with the neutron kick at site 4.\n"
+     ]
+    }
+   ],
+   "source": [
+    "n = 10\n",
+    "dt = 0.6  # physical time per Trotter step (also the omega-axis unit in POST)\n",
+    "time_steps = 10\n",
+    "center = n // 2 - 1\n",
+    "\n",
+    "# MPS-simulator settings, shared by the ground-state prep here and the AQC\n",
+    "# compression inside the function (matches the original tutorial).\n",
+    "mps_max_bond = 32\n",
+    "mps_cutoff = 1e-8\n",
+    "\n",
+    "# 1D isotropic Heisenberg (KCuF3) Hamiltonian on n qubits\n",
+    "H = SparsePauliOp.from_sparse_list(\n",
+    "    [(p, [i, i + 1], 0.25) for i in range(n - 1) for p in (\"XX\", \"YY\", \"ZZ\")],\n",
+    "    num_qubits=n,\n",
+    ")\n",
+    "\n",
+    "# Ground state (DMRG + fidelity max) + neutron kick baked into the same circuit\n",
+    "gs_circuit = prepare_ground_state(\n",
+    "    n, gs_layers=3, max_bond=mps_max_bond, cutoff=mps_cutoff\n",
+    ")\n",
+    "gs_circuit.rz(np.pi / 2, center)  # exp(-i (pi/2)/2 Z_center): the neutron perturbation\n",
+    "print(f\"Prepared {n}-qubit ground state with the neutron kick at site {center}.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "small-s3-md",
+   "metadata": {},
+   "source": [
+    "### Steps 2 and 3: Compress and execute with the Qiskit Function\n",
+    "\n",
+    "In a hand-written workflow these are two separate stages: optimize the circuits for hardware (Step 2) and execute them (Step 3). The Qiskit Function collapses both into one call. It performs Trotter synthesis, AQC compression, and hardware transpilation, then runs the circuits (here on the exact simulator, later with built-in error mitigation on hardware). The two tuning parameters are `aqc_segments` (the compression plan) and `aqc_options` (the MPS and optimizer settings). Each segment `{\"n_steps\": k, \"ansatz_steps\": m}` compresses `k` consecutive Trotter steps into an ansatz built from an `m`-step Trotter target, and any steps beyond `sum(n_steps)` run as plain Trotter. Early, low-entanglement steps compress well into a shallow (`ansatz_steps=1`) ansatz, so here we compress the first 3 steps into a 1-layer ansatz and the next 2 into a deeper 2-layer ansatz; the remaining 5 of the 10 Trotter steps run as plain Trotter. For `aqc_options` we mirror the original tutorial: MPS bond dimension `max_bond=32`, `cutoff=1e-8`, and an L-BFGS-B optimizer capped at 100 iterations.\n",
+    "\n",
+    "Call the function loaded in Setup. `backend=\"statevector\"` runs the exact reference path: no QPU time, with the circuits running on an exact statevector simulator inside the serverless worker (a saved Serverless account is still needed to call it). The `initial_state` carries the prepared ground state (including the kick); `observables` is omitted so the function measures the default per-site $Z$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "small-s3-run",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "job = fn.run(\n",
+    "    t_steps=time_steps,\n",
+    "    aqc_segments=[\n",
+    "        {\"n_steps\": 3, \"ansatz_steps\": 1},  # early steps -> shallow 1-layer ansatz\n",
+    "        {\"n_steps\": 2, \"ansatz_steps\": 2},  # later steps -> deeper 2-layer ansatz\n",
+    "    ],\n",
+    "    aqc_options={\n",
+    "        \"max_bond\": mps_max_bond,  # MPS bond dimension for AQC compression\n",
+    "        \"cutoff\": mps_cutoff,\n",
+    "        \"optimizer_settings\": {\n",
+    "            \"method\": \"L-BFGS-B\",\n",
+    "            \"jac\": True,\n",
+    "            \"options\": {\"maxiter\": 100},\n",
+    "        },\n",
+    "    },\n",
+    "    dt=dt,\n",
+    "    hamiltonian=H,\n",
+    "    initial_state=gs_circuit,  # prepared ground state including the neutron kick\n",
+    "    # observables omitted -> default per-site Z (the neutron sigma_z readout)\n",
+    "    backend=\"statevector\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "bf76d298",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DONE\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(job.status())  # rerun this cell until status says DONE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "small-s3-result",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AQC fidelities: {'1': 1.0, '2': 0.9999, '3': 0.9992, '4': 0.9998, '5': 0.9995}\n",
+      "Green's function shape: (10, 10)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# The per-site <sigma_z>(t) the function returns is the retarded Green's function\n",
+    "# G(j, j_c, t). The workflow samples t = 1..time_steps, so drop the t = 0 row (the\n",
+    "# prepared+kicked state before any evolution) before post-processing.\n",
+    "result = job.result()\n",
+    "print(\n",
+    "    \"AQC fidelities:\",\n",
+    "    {k: round(v, 4) for k, v in result[\"metadata\"][\"aqc_fidelities\"].items()},\n",
+    ")\n",
+    "\n",
+    "ev = np.array(result[\"expectation_values\"])\n",
+    "Gjjc = ev[1:]  # shape (time_steps, n)\n",
+    "print(\"Green's function shape:\", Gjjc.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "small-s4-md",
+   "metadata": {},
+   "source": [
+    "### Step 4: Post-process and return result in desired classical format\n",
+    "\n",
+    "Fourier-transform the Green's function into $S(q, \\omega)$, mirror-symmetrize, and clip negatives: the standard neutron post-processing. Mirroring is exact because $S(q, \\omega) = S(-q, \\omega)$ for this model, and the negative values that survive are artifacts of Fourier-transforming a finite, discretely sampled time series, so they are clipped to zero. On this small exact run the two-spinon continuum is only coarsely resolved, but the machinery is identical to the hardware run below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "small-s4-code",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwgAAAJOCAYAAAATcyDSAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjksIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvJkbTWQAAAAlwSFlzAAAPYQAAD2EBqD+naQAAYmtJREFUeJzt3QeYE+Xa//F7d+koTbqCgIUi0otgVwQ7NhRFKXo4x4ING3hUPDbEY0GRI4oF9YhdlIOvKKKIBaSJL1gQlSZIE6VK2+R//Z7zzvyzu9manSSbfD/XNbCZTCaTyWRm7ud+SkY4HA4bAAAAAJhZJnsBAAAAgIcAAQAAAICPAAEAAACAjwABAAAAgI8AAQAAAICPAAEAAACAjwABAAAAgI8AAQAAAICPAAEAAACAjwABzowZMywjI8PuvPPOwPZIkyZN3IT4Wbt2rQ0YMMAaNWpkWVlZ7jv+448/ytRXoGNS261jFMnpuOOOc99RMp1vCjJ69GirUKGCLV++PCHvDxTVxRdfbAceeKDt3LmTnYa4IkAogC4euohFTuXLl7f999/fzj//fJs3b17MX8DAgQPderlQ5e+TTz6x/v3728EHH2z77LOPVaxY0Q444AA7/fTTbdy4cbZ161ZLheMsiJslHV8vvviiHXPMMXbbbbfZiBEjrFKlSpZMEn2zGC///ve/7W9/+5t16tTJHcP6zBMmTCjwNVu2bLGhQ4e6GwS9RgH2TTfdZNu2bbNUFI9ChN9//93uvvtuu/TSSwt8rxNOOMF9R61bty7SeidNmmRnnnmmNWjQwAUfdevWtZ49e9oLL7xgoVCowNeuXr3ahg8fbh06dLAaNWq412s9p512mjtGdu/ebWVt/2u7i3KMlzXxLrC444473PGhoBaIp3Jxfbcy6qCDDnJRvGzfvt3mz59vr7/+ur399tv24YcfupsvlL4///zTBg8ebC+99JK7qT3++OPdBVg3Sr/++qt99tln9u6777ob3/Xr11tmJvFuJN1UTJs2zXr06OH2YVk1ZMgQ69u3rzVu3NjKMh2nK1assNq1a7ubP/1dEJ1rjj32WFu4cKG70bzwwgvtq6++sgcffNAFzTNnzky6YK+ounTpYt99953bF/H2yCOP2KZNm1yglZ+ff/7ZD1y/+eYb+/LLL61r1675fk8XXXSRTZ482WrWrOlu6pWx27Bhg/3P//yPy+A988wz7nqh53N7+eWX7bLLLnPnu44dO7prTfXq1V3276OPPrJBgwa5IH/69Omluh9QNhx66KHWu3dvu//+++3qq6+2qlWrJnqTkCYIEIpAJde5Szf1Y1WJz+233+4u1ih9KuF75ZVX3M3R888/b/Xr18+zjC7iN9xwgyuhI0DISTcY2i8NGzYs04enbiITcSNZ2p5++mk75JBDXDbAO38U5IEHHnDBwS233OKW9wwbNsxGjRrlbnQLW0eyqlKlirVo0SLu77t37173PRx55JGu4Cc/zz77rIXDYbvxxhtdQKYb/PwCBGXpFBwoMFCWSBkAj6qF6KZO79mnTx/74IMPcpynpk6d6gICveadd96xk046Kce6tQ0KLPR6pC8dI2+99Za7HiqYBOIijHwtW7YsrF3Uq1evPM+tX7/ePVe1atU8z+3atSv80EMPhdu3bx+uUqVKeJ999gkfddRR4XfeeSfHcgceeKBbR+7p2GOP9Zd56623wn379g0fdNBB4cqVK4erVavm1vXGG2/ku70DBgwIf/vtt+GzzjorXKtWLTdPz8mOHTvCt9xyS/iAAw4IV6xYMXzYYYeFn3rqqfDHH3/slhsxYkSe9f7888/hyy67LNyoUaNwhQoVwvXr13fvsXz58qj77e233w536tQpXKlSpXDdunXDf/nLX8KbNm1yn1dTUXz44Ydue1q0aOG2uSB79uzJ8Tjys3z++efhk046KVy9enU3zxMKhcLPPPNMuHv37uF9993X7duOHTu6edEUZ3m9r95L2/HSSy+F27Zt6/aF9ts111yT5/N431vuff/DDz+EBw4cGG7SpInb7zVr1gy3adMmfO2117rtKYiOoWjHlr430f+Rx0V+2x9tn86dOzfco0cPd1zreNRxFm098tNPP4UHDx7sf4Y6deq4bXvuuedyvFe0yVtntO3xTJ48OXzccce57dA+1v7Rby/3MRH521i6dKnb5ho1arjf54knnhheuHBhOJ5GjhzptsfbD7np+23YsKHbx9u2bcvxnB5rfrNmzYr8fsX53Ufuq2hyn6Mij7c///zTvY/OFXof/X4fe+yxPMdr7vf13jPaFLltOu8dc8wx7jjS+hs0aOC+v2jnw2j+85//uHWOHj0632X27t0b3n///cP77befO5cffPDB7jef+3uQadOmufUdcsgh+Z6n9Nl1ztZyL7/8co730Xeo+TrfFWTnzp3hWMyfPz987rnn+ufw2rVru3P0PffcU+T9r32h77Jnz57uOPJ+z2effXZ4wYIFOd7PO79EmyJt2bIlfMcdd4RbtWrlfr86T2v9n376aY7lTjjhhHBGRka+15yrr77arfuDDz7IMf+TTz4Jn3766e671Pbqu/z73/8e3r59e9T1aPnevXu765aW1+fU5/O2J7/zau7r2qJFi8J9+vRx+0fr0flP5+2NGzfmeU/vuvj777+Hr7rqKveeWVlZOc4N2vc6V+k4AuKFDEKMypXLuQt37dplJ598sivZbteunYv29+zZ46rCKE04ZswYV2VCrrvuOlc/8+uvv7Zrr73WL3mKrAOqEkLVRz3qqKNctQSlrVVadd5559ljjz3mSqdy+/HHH+2II46www8/3JVu/fbbb24dKk1WFR1Vi9JzSovrueuvv95V34lGqfVevXq5NLrq/KsEVHXmVWXlvffes1mzZlmzZs385VXfVin1atWq2SWXXOI+05QpU1w1F1V50XYUhUrwRCV4lStXLtZ34Pniiy/svvvuc5/tr3/9q61cudLN1z1Ov379XGpfn0f7Qdul6jj6vr799ltXaugp7vKexx9/3JUQ6ntXfWb9re9s48aNhVb5WbNmjauGof2ukskLLrjA/b106VL717/+5d4vv88t+t51/D366KPWtm1bO+uss9x8zYvF3LlzXcm29qnq06vKi0o4Fy1aZIsXL85R5UVVwLTtaiOiY0jVhFT/W6/Rdmkb1bhVx5MyRKpOo8eeyJLYaB5++GGXPapVq5b7TpR6129D8z799FNX4pa74azeS7+Nww47zGWofvrpJ1dyq8+jKi/16tWzZKDvWceA9lvuKgV6rBLw999/31atWuWqsxSkJL/7klLbLH2/5557rnv85ptv2jXXXOP2+0MPPZTv6/Rdq32MV89a50aPd0w88cQTduWVV7rz4Nlnn2377befy5LNmTPH1f/33rMgXjUdHQP50X5VnW+9l37nOo9p21StVMdspOeee879r2Muv/OUjsG///3vdsopp9j48ePd70A+/vhjV5Wpe/fuduKJJxa43apWWVLKQuk91EmBzkXKYKmjAp23nnrqKbdtRdn/qpal+UcffbSdeuqprrqUtl+/OV0LVOWtc+fOblmdb/Qe+m3pPaOdd7Q+Vc9VFS4dz5dffrlrc+P9HrW/vfOWvgNVt9J589Zbb82TFVLJujKlkftRx8tVV13lPtsZZ5zh2oSo3eC9997r9r2myOuRzkn6Teh71PGlKo06DnQee+ONN9w12Pv+VWtA1znvWh15rtLy+t3qeqfrtJbRdVLr17Vw9uzZeTKium/QNUJti/Rb1bk98lyk7VT1M61H1wGqGSEu4haKpFgG4b777nPPnXbaaTnm33rrrW7+7bffnqPUTCUlKrFRacLq1av9+QWV5HolsLlt3bo1fPjhh7vSlsiSkMhSIJXK5KYSCT138sknu9Irz//+7/+67cpdWrd7925X8qHSs9wlRCpRUSmHSmc8mzdvdiW5yqosWbIkx3pU6hetpCU/TZs2dctH+/yF8UonNT377LN5nlfJqZ4bNGiQ27bIUpozzjjDPTdv3rwSL++VeOv7+f777/35KmE89NBDw5mZmTmOgWhUUpdfSedvv/1WpP1QUElwSTMIml555ZUcy19yySV5SkdV4qlSWH3W9957L897rFq1Ks+6o2Wv8tueH3/8MVyuXDlX0rdy5coc7+uV1r7wwgt59oWm+++/P8f6b7vtNjdfpfrJkkGYMmWKe37IkCFRn9d8PT99+vRC36u4v/tYMgjNmzcP//HHH/58/a15Kv1V5qmw77ygLGOHDh3c9q5bty7Pc9FKZqPp3LmzOyYLKpE/55xz3LbNmjXLPdY5SNsfrfRW50ctq6xUQfTb1/GqzGN2drabd+edd7rX6vgL0tChQ937KLNb2H4raP9rn/3yyy955i9evNhltJRVjHbc5XeMX3TRRe758ePH55iv71eZDpW+KyPlXT+175RpyC8rdOONN/rzvvnmG7e/lb3N/Rm9396DDz7oz1MGUceFsna5z4m6jkeerwvKaOq7VbZfz0+dOjXHczfddJObf+mll0atSaD7jIKy5ddff71b7qOPPsp3GaA00aqzCFQirzYImtSwTZG+SjEU4f/zn//MUVKnUgvVbf3HP/6Ro/Ry3333db0RqFRBJZtFFVk671FPPirJ2Lx5syvRzU119VUqlJtK90UlKCpN8qhUUSU0uam0QyV/+szt27fP8ZxKU1QypEZ4KvURlSTrb5XMqmGVRz0/6T2LQyWDEq3+vN7H+z68SaVkualHEDXwi1ayrxKYsWPHum2LLKXxtlPZgpIu71FWqHnz5v5jlUypoamOEzV0L4popZIqMU8UlfgpmxFJ37dEHosqBVTpm+rOKqOWm3qhisXEiRNdyaFKbiNL0FXSqvr5Eq33lKZNm+ZpnOrV6Y32W0oU/bZFjVWjUYYucrmCFPd3Hwu1yYrcZv2txtmKK5QlipV+f5G/QY+yCUXxyy+/uNLe/ErklaH9z3/+485fXpZB52CVcKtkeMmSJVHPU4VlcfQ71jaqIbKyaJGvjfW3UFTRziVF3W+ifaYe/HJTNk4l/sogKFteFMqivvrqq+5a+pe//CXHcyrp129U34WyXt71U9kEZT0WLFiQY3k14BavIxF58skn3flBGfvcn/Hmm2+2OnXq5Dhna3mdl++55548vTjpOl7Udlyff/65y0oqW6QsQiRd/3Xu1rkrWq9UyswWlC33Mgo6hoF4oIpREegHrxv+3DfhqsagBsweXTx08tfJJPfyohOefP/990X+gtQ7jxooKoWrXk90gYmkagi5qUpJtKo8qsqkG13dOOemtLEa4kVSKtT7XNG6oPQawf7www+u60at31tXbt26dSuwSkxxKEDIfbOhk3ruNLaX7o60Y8cOVx1G35F3IxnJu8B531Fxl4+klHBu3s1AYWMRKCWu6mVKkatahG6yVQUnWsAYT0X9TKr2IWpgHgRVY5HIKkmRx5qqOkULGnWM5G7MXtTvRLROHX+5j73cVU+SSXF/97GI9tv35nnfWUmpao5u7tTtqKpJ6aZUBRVesFQUqlpV0A25ziv6TecOnNTNsgIEVX2Mdh4ojsK6PC1tqvalqkOqNqPgXg2hFehHu9kvyvGvG1ntC53/cwcEuvFXFbDCKBjPzs52VWuiXVtUxc47r6pqq+g70U29AgLvWFaBlAI6Bbu67uW+dqm6WLTenxRkRp6zS+t8VdB5SQV7uk6qobquqdpmj85XkY+j8QqGtI+BeCBAKAKVBKj+uHeTr4uIehZRXUGdWPTD9+pUiupUasqP6hAWhdanm1zVnVcJlurxq/RLpYA6UauUVifY3PKrR63SxvxKuqK9xvs8hdWX9z6PV5qpEqDctM3FKa3S9ih7oQAo902xSoa90uGCeoOJ9pkUwKk0U6Xb0YK43J+puMtHinbj4gVJujgWRDedusjp4qkszWuvvebmq+eXu+66y/WIkghF/UzesVCSm5Ci8LJW0b5jlfhpvr6z0vxORL+73MeBArfSDhC8Uvj8MgTe588vwxDL7z4W0dbnzStKtqMgao+kc4iytGrP4LXDUTsX9eik7FBhVEJb0IBTCpZ0/OQOEHSTrbYUysYoE+MdMyoo0nlKbUEiC4tyU8GOghOVwnv1z71e2aIdp6VJvS+pTZzaY6n02ms3oWuLgp2itkNRmy6V+Hs30mqPpWuf9peCZgWi0a5H0XjXFpW4ayrKeVXvqWNJ7Q303euaorYB2re5vy9v/UXNXOvY1OcoSnBT0vOSeOv3lvPomlnYQINe4aB6AAPigSpGxaTUpC5UqmKkRo1Kn+e++VBjOd1U5jd5J+jC6GKl4ECD+qjERulS/a2bxoIa2eV3otHNhJfFyG3dunV55nmfRyU0BX0e3SB56/eyHrnp5ksXyKJSozpRQ7KSirYfvM+kkvCCPpP3vsVdvjSppFQXQF3s1DhNKWqV2qkUsKCLalF4pehKw+cW641cZKO9oG5+vO8l2nGr70Pzi1OyXFQKBHJ/90EMmKSbr8iS1Ny8+d5yBSnu7z6WYyPa+rx5RQlmCvs9qzqbSp/1edQw+ZxzznEFJSplLkqAp/O3d/MY7QZYpcr6ThWgRw6QqeNZgYV+fwrYc5+nChujQI1atT8V4HvnJRX6FOW1pUFZHGWhVeChc5UG31NmVMGVGhoXhW62FQCo2o8aJitIU7Cs61G0LqgL4v02VUWwoPOqGk57FBCoiqa+A6/qkbIJOl6VUYq2ft2IF7R+j75fPdb4OkGdlyKrleU+NxVlFHLvuNUxDMQDAUIJKUBQtRP1KOONgtyyZUv3w1dPCUWti+nVCY52cVPVJlFd/9xUvam4lIJViUzuOpz5rc/r91s3p0Vdf37r0jqi3XDkx6vXrotQaQ4xr7qs+p4U3BWlSklxlw+C0uEKCHUxVi9IupCpfUgsvAGbot3Ax1oVRNQDkyidHstvID9em5hoN+fqeUvHTKw9NiWSbvx1flEgmDs7pcearxLzwuq+l+R3X1BwV9ixEW193rzc7ZjyOxaKchwok6A66V49dtVNV1uxwqgah44Nr0ezSF5VK9UfV7uU3JPXS1JklSzvPKUetfI7T+n3OnLkSL+qkkcl98qOKjAprIChqCXzRcmgqPqLzqu6hqlUWr2xFWX/63qkai6q1hVJ1TCjHVsF/a6VvdBNcVGvLR4vU6DxJpS1UeCl/Zg7U+ldu7yqRvE6XxV0XtJvUPcG+g4i26YVldf+pbCqSEBpIUAoIf3IVc1IgYBK9UVp5yuuuMK1FVCWIVqQoK4gI0vYvXqFOtnlpu7oRNmDSEoTR5ZiFffkqgbMkSc3lSR5Db0iKTBRV2+6+KkBWm76fJHbpuUVIKmertolRC4XmWkpCnVXpzrHujFX3Vmv5KU0SrtVVUAXNY3SHK1q0LJly/ygryTLlwY1Ys6dho4smYp1BF2vfUbuhrzKWJTGwH+qfqe63rqQqx5wbpE3nwX9BvKjEkP93nRsRrbDUeM//S4lmdsFFEY3T2q8qW4PvfOLR481X8djEL97/YZ1A6PfduRNt7qrLWxgNm1b5G9Sf6vhpz6PuoUsjI4F1bGOdrOtm67IUl/v3OKVrBblN+FlOxVERtL+VDU+tdXQ/xqYLPek+Tqmde71zkc6T6krS53vVA0p9/lIN/a6Juj8qfN5ZKcJutFUxwcqAddr1Y1nNMrg6j0ieRmOopx3dBMebX9GO5cUtP+1/cpARFaf1fGka120DFVBv2tlHPSZFRypo4/c36v3Hem8G0ltD1q1auWyR2pYrNdFa2ivLmp1flA34NGCQRX2RAa76mJV34c32nkkvUfkOaagz+UNwKdsjZfl8Oh3oCy6siBF7e479/5QFaWiZA2B0kAbhBiob33V4VS9VJXGeL0XqTRFJb0a+0CNwVS/UDdEuiCrnqZO2F49fZV+qT6l1qUSKl2gdCLWSU+T1q+TnEqYNF+vV0paqfXi9IYkukAruFB7CpV0qKRMF1c1/FL9ztyl0qovqxtGLacLq7ZVpRe6MOkkqpJBleR5jb1UhUCfWzdmugHVDb7mab0KqIpbv1OBhi6e2maVlqqkSKX5Ornq4qb2H7pYqU5vcUZlVf/9KllSWxKVxKpth0prtU59Fp2I9Z5ebxbFXb406MZNF0AdPzqudNOmUlLdnOgCFa13puJQMKf1KkDQhU7Hg4Ix3aSoj/OSBKC5jx3dUKlxtY4f/a+SbAU9qsevC793gdZ3p/2pusV6nW7CdIzpuM+vWoq2Xb8NVVFo06aNu9nQb0c3Uypp0+eL7NUkGegm0wuodS7w5nmljSqZjezRRQ1yVX1Gn1P7SjdHOreolFO/r8i+6kvzdy/arzonqcG32ruoYa1ueqI1/I+k3n9UNS5yHAT1uqIqLWqgWRidY1TKqm1UtRj91vUb0KSMgX4HyqbpXKjgQKXf+l3oBtorUCmIjgtti14X2Y5HmQgFCdpXXpuy3HQuUgZAdfm9dmii35BuqHXsKSOgajvK7OimWb8jnfu9kZK9zJ1Hvwv91vW9K9jQPtI+V+ZS5xcdGyq51zknWkPnonT8oONH1w/tQ51HFRDoONJ1RNurApii7H/9HnXs6TjV703r0fbp8ykrkbvUXJ9D5301kFZg4VWN8QqLlH3Xb1XHufaBltd+0vlI26BqdKryk7vOva6LClTVWFrPRRv/Qseg1q/gTMGuzmk6ZyjIVZUqFYLoOjVu3Di3vK5r2k4VBqlXJh1rOp4UCCq403fqjRGh65DOT7rm6/qjc5S2W+Mb6RjR8aB2i3pPHWNaj6752j/ahshR0YtKx4AKovR5gLgp1U5T02gcBM+YMWPcMuoL3qO+xp988snwkUce6cYF0IifjRs3dv2QP/HEE3lG5HzggQfcSJzly5fP08e4+mfWyJIaRVfjEeg5jboZrY/pwvovF42bcPPNN7s+6rVd6le6sJGU1fe1RoHUNuo1+kwtW7Z0IyRH64d90qRJbpRhLVvSkZQjadu0fzXqqPrCVl/o6q/6lFNOCf/rX/9y4y/kXr6gfvU9r776quu7W/tW+177RKPyaiTeDRs2lHj5gvrJLqxvcM/s2bPDf/vb38KtW7d2I/7qc2v/q//7FStWFGGvFX486HmNKKzjSmNXaERa9VVf2EjKxXkfjVegUbg1Oqj2mY4H7bPIMQq8z6tjW9tSnJGUNTq59zodbxofpLCRlIvat39pK2h02fy2TeMIXHfdda5feO0/nUduuOEG1y98cZTkdz927Fj/vKT31dgqGgeksJGU9T7eiL0aA6EoIylHjvGikbc1QrLGWYlcRr/1M888051DNOquRsft0qWLO6dGjk9SGJ039BuOHAuhW7du+R5juUc313IazyS3N998040LU69ePX/bNWne2rVrC1yvzrEagbp9+/bu/Ko+/LUeXTN0roj8fBpxV3326/pSFOqPv3///u670O9EYxbo+9eYPbnPcwXtf9GI1RqPQqP6ajTm888/340Tkd+4Ku+++64be0Lnr2gjKavff13/dL3QOUjLaQwcnZd0jsj9OxaNe6LPr3VdeOGFBX72OXPmhPv27euuFzqOtc3a/mHDhoW/++67PMvr+9f3VatWLX8kZY1A/fnnn+dYbsKECe5co99StPF9NMbIeeed595P76vndQ2Ndl0pynXRGzMj3iO+I71l6J/4hSMAgGShUk2ViKoxaLTuJlORSs5VIq/qbxohPSjKNioroFJ6lULnHj23pJTxUTfIylCrlBqpTW33VK1I2Z/8qqEBQaANAgAgbeimXVV7VCc8yDEJVB9dVchUdU9VTqK1KSoJVe1UA3yCg/Sg6myq0quqyEA8ESAAANLKo48+6roLDnoMArWDURs1lfirQW5p8NqkID2ovcP48eOjDnQIBIlGygCAtKLG1PGqUhWtlx2gqLyudIF4ow0CAAAAAB9VjAAAAAD4CBAAAAAApFcbBPVUoZEQNfiMGvwAAACkA/Vmr0HiNCClBnNLBhpcUCPfB0kD/RVlhHWkcYCg4EAjWwIAAKQjjVKtkeqTITiovG8ts71/Bvo+9evXdyNQEySUTFoECMocyNIff/T/TlXZofQY927b7uD6L08WP27aaeng5QW/WDqYOXOZpbp1iz+1dFC5Rh1LBy2O7W7p4KZTW1oq27Ftq11yXPukuf9xmYO9f1q5VuebZZUP5k2y99jab19z70WAUDJpESB41Yr046hWrZqlsnQJEDLSIECouiegE2eSqVBlH0sHmRWqWKrLyKpg6SCjXHpUWyhXqaqlg6r7JMeNc9CSrYp1RvlKgZ0zwplZgaw3nSRHZTQAAAAASSEtMggAAABIHhmZWW4KRJgMQqzIIAAAAADwkUEAAABAXJFBSG5kEAAAAAD4yCAAAAAgrjIyAmyDEKINQqzIIAAAAADwkUEAAABAXGVkZVpGVlC9GFH+HSv2IAAAAAAfGQQAAADEVWaA4yAwknLsyCAAAAAA8JFBAAAAQOqMgxDUetMIGQQAAAAAPjIIAAAAiCsyCMmNAAEAAABxlZGZ6aZABLXeNMIeBAAAAOAjgwAAAIC4oopRciODAAAAAMBHBgEAAAAJaIMQVDenlH/Hij0IAAAAwEcGAQAAAHGVkRHgQGkZDJQWKzIIAAAAAHxkEAAAABBfWVmWkRVMSX84RAYhVmQQAAAAAPjIIAAAACBlxkEIrG1DGiGDAAAAAMBHBgEAAABxRQYhuZFBAAAAAOAjgwAAAIC4yszMclMwK6cNQqzIIAAAAADwkUEAAABAXGVkZgbYixHl37FiDwIAAADwkUEAAABAXNGLUXIjgwAAAADARwYBAAAAcUUGIbkRIAAAACCuCBCSG1WMAAAAAPjIIAAAACCuMjKyguvmNIOB0mJFBgEAAACAjwwCAAAA4iojK8tNQa0bsSGDAAAAAMBHBgEAAABxlZGZGVwbhEzKv2PFHgQAAEBaGzt2rDVp0sQqVapkXbt2tTlz5hTpda+88oplZGTYWWedZamEAAEAAAAJGQchqKk4Xn31VRs6dKiNGDHCFixYYG3btrVevXrZ+vXrC3zd8uXL7cYbb7Sjjz7aUg0BAgAAANLWww8/bIMHD7ZBgwZZq1atbNy4cValShV79tln831Ndna29evXz/7xj39Ys2bNLNUQIAAAACDlMghbtmzJMe3atSvPduzevdvmz59vPXr08OdlZma6x7Nmzcp3+++66y6rW7euXXbZZZaK0qqRckY47KZUVs5Clg5qZeb9kaeao8qttnTQufx0Swc/bf3IUt2GPzdaOmjQqr6lg4OPaWPpIHRAZUtlW7bssXTVqFGjHI9VhejOO+/MMW/jxo0uG1CvXr0c8+vVq2fff/991PV+9tln9swzz9jChQstVaVVgAAAAIDEy8zMcFMwK//veletWmXVqlXzZ1esWDHmVW/dutUuueQSGz9+vNWuXdtSFQECAAAAUo6Cg8gAIRrd5GdlZdm6detyzF+3bp3Vr583W/jTTz+5xslnnHGGPy8U+m/tjXLlytmSJUvsoIMOsrKONggAAACIq4zMjECnoqpQoYJ17NjRpk+fnuOGf/r06datW7c8y7do0cIWLVrkqhd505lnnmnHH3+8+zt3taayigwCAAAA0pa6OB0wYIB16tTJunTpYqNHj7bt27e7Xo2kf//+tv/++9vIkSPdOAmtW7fO8foaNWq4/3PPL8sIEAAAABBXGlxMU1DrLo4LLrjANmzYYHfccYetXbvW2rVrZ1OnTvUbLq9cudL1bJROCBAAAACQ1oYMGeKmaGbMmFHgaydMmGCphgABAAAAcZURYC9G4aB6R0oj6ZUvAQAAAFAgMggAAACIfxuEgEr6g2rbkE4IEAAAABBXxe2OtLjrRmyoYgQAAADARwYBAAAAcZWZkeGmIISpYhQzMggAAAAAfGQQAAAAEFe0QUhuZBAAAAAA+MggAAAAIK7IICQ3MggAAAAAfGQQAAAAEFeZmRluCkKYcRBiRgYBAAAAQNkKELKzs+3222+3pk2bWuXKle2ggw6yu+++28LhcKI3DQAAAMWUkRnshDSoYjRq1Ch74okn7Pnnn7fDDjvM5s2bZ4MGDbLq1avbNddck+jNAwAAAFJGmQgQvvjiC+vdu7eddtpp7nGTJk3s5Zdftjlz5iR60wAAAFBMGRkZbgpCUOtNJ2UiCdO9e3ebPn26/fDDD+7x119/bZ999pmdcsopid40AAAAIKWUiQzCsGHDbMuWLdaiRQvLyspybRLuvfde69evX9Tld+3a5SaPXgsAAIDkkJn5356MghAuE8Xfya1M7MLXXnvNXnrpJZs4caItWLDAtUV48MEH3f/RjBw50rVP8KZGjRrFfZsBAACAsqhMZBBuuukml0Xo27eve3z44YfbihUrXCAwYMCAPMsPHz7chg4dmiODQJAAAACQHBhJObmViQBhx44dlqlcVARVNQqFQlGXr1ixopsAAAAApGCAcMYZZ7g2B40bN3bdnH711Vf28MMP26WXXproTQMAAEBJejEKqA0CvRilSYAwZswYN1DalVdeaevXr7eGDRva3/72N7vjjjsSvWkAAABASikTAcK+++5ro0ePdhMAAADKtsyMDDcFIcw4COkRIAAAACCFZAZXxUjrRhp0cwoAAAAgPsggAAAAIK7o5jS5kUEAAAAA4CODAAAAgLjKzMxwU1DrRmzIIAAAAADwkUEAAABA/AdKC6g7UgZKix0ZBAAAAAA+MggAAACIq4zM/05BrRuxYRcCAAAA8JFBAAAAQFzRi1FyI4MAAAAAwEcGAQAAAHHFSMrJjQwCAAAAAB8ZBAAAAMQV4yAkNzIIAAAAAHxkEAAAABBX9GKU3MggAAAAAEjPDMLO7LBVyA5bKlu5Za+lg5e/WmupbuoXaywd/LGhiaWDGs2utlR30kWNLR1c0G5/SwdrqqTHLcKsHzdbKtuxbaslbRuEzIzA1o3YpMevHwAAAEkjKzPDTUEIB7TedEIVIwAAAAA+MggAAACIeyPloDIIITIIMSODAAAAAMBHBgEAAAAp0waBDELsyCAAAAAA8JFBAAAAQFyRQUhuZBAAAAAA+MggAAAAIK7IICQ3MggAAAAAfGQQAAAAEFflMjUFNZJyIKtNK+xCAAAAAD4yCAAAAIgr2iAkNzIIAAAAAHxkEAAAABBXmQGOpJwd0HrTCRkEAAAAAD4yCAAAAIirrIxMy8rMDGzdiA17EAAAAICPDAIAAABSphejoNabTsggAAAAAPCRQQAAAEBckUFIbgQIAAAAiCsChORGFSMAAAAAPjIIAAAAiKusjAw3BbVuxIYMAgAAAAAfGQQAAADEVWaA3Zxq3YgNGQQAAAAAPjIIAAAAiCt6MUpuZBAAAAAA+MggAAAAIK7KZWa4KQjZtEGIGRkEAAAAAD4yCAAAAIgr2iAkNzIIAAAAAHxkEAAAABBXZBCSGxkEAAAAAD4yCAAAAIirrIzgRlLWuhEbMggAAAAAfGQQAAAAEFeZmcFlELRuxIYMAgAAAAAfGQQAAADEFb0YJTcyCAAAAAB8ZBAAAAAQV2QQkhsBAgAAAOIqK/O/QUJQ60Zs2IUAAABIa2PHjrUmTZpYpUqVrGvXrjZnzpx8lx0/frwdffTRVrNmTTf16NGjwOXLIgIEAAAAJKSKUVBTcbz66qs2dOhQGzFihC1YsMDatm1rvXr1svXr10ddfsaMGXbhhRfaxx9/bLNmzbJGjRpZz549bfXq1ZYqCBAAAACQth5++GEbPHiwDRo0yFq1amXjxo2zKlWq2LPPPht1+ZdeesmuvPJKa9eunbVo0cKefvppC4VCNn36dEsVBAgAAABIywzC7t27bf78+a6akCczM9M9VnagKHbs2GF79uyxWrVqWaqgkTIAAABSzpYtW3I8rlixopsibdy40bKzs61evXo55terV8++//77Ir3PLbfcYg0bNswRZJR1aRUgVLY9bkplrWytpYM7sj+0VHfJqmmWDjb/kvMEnqqaHdzGUl2dVpdYOti7z05LB+U2rbR0UOWjly2Vbd25y5JRZgnaChRn3aK2AZHUxuDOO+8s1fe6//777ZVXXnHtEtTAOVWkVYAAAACA9LBq1SqrVq2a/zh39kBq165tWVlZtm7duhzz161bZ/Xr1y9w/Q8++KALED788ENr0ya1CoFogwAAAIC4ysrICHQSBQeRU7QAoUKFCtaxY8ccDYxD/9fguFu3bvlu/wMPPGB33323TZ061Tp16mSphgwCAAAA0pa6OB0wYIC70e/SpYuNHj3atm/f7no1kv79+9v+++9vI0eOdI9HjRpld9xxh02cONGNnbB27X+rd++zzz5uSgUECAAAAIirzIwMNwW17uK44IILbMOGDe6mXzf77dq1c5kBr+HyypUrXc9GnieeeML1fnTeeecF3sYhUQgQAAAAkNaGDBnipmhmzJiR4/Hy5cst1REgAAAAIK6yXDuE4NaN2NBIGQAAAICPDAIAAADiSmMVeOMVBLFuxIYMAgAAAAAfGQQAAADEVeR4BUGsG7EhgwAAAADARwYBAAAAaTsOAvIigwAAAADARwYBAAAAcaWOhoIaB4FOjGJHgAAAAIC4opvT5EYVIwAAAAA+MggAAACIKxopJzcyCAAAAAB8ZBAAAAAQV1kBNlIOar3ppMxkEFavXm0XX3yx7bfffla5cmU7/PDDbd68eYneLAAAACCllIkMwu+//25HHnmkHX/88fbee+9ZnTp1bOnSpVazZs1EbxoAAACKiTYIya1MBAijRo2yRo0a2XPPPefPa9q0aUK3CQAAAEhFZaKK0eTJk61Tp07Wp08fq1u3rrVv397Gjx+f7/K7du2yLVu25JgAAACQHLIyMwKdkAYBws8//2xPPPGEHXLIIfb+++/bFVdcYddcc409//zzUZcfOXKkVa9e3Z+UfQAAAACQIlWMQqGQyyDcd9997rEyCIsXL7Zx48bZgAED8iw/fPhwGzp0qP9YGQSCBAAAgORAG4TkViYyCA0aNLBWrVrlmNeyZUtbuXJl1OUrVqxo1apVyzEBAAAASJEMgnowWrJkSY55P/zwgx144IEJ2yYAAACUDOMgJLcykUG4/vrrbfbs2a6K0Y8//mgTJ060p556yq666qpEbxoAAACQUspEBqFz5842adIk17bgrrvucl2cjh492vr165foTQMAAEAxZWRkuHYIQa0baRAgyOmnn+4mAAAAAMEpMwECAAAAUkOQ4xUwDkKatEEAAAAAEB9kEAAAABD3EuqgBjym9Dt2BAgAAACIq6yMDDcFtW7EhiALAAAAQMkyCNu3b7eqVasW5yUAAABADpkBdnMa1HrTSbEyCDVq1LAOHTrYr7/+6h6vWLEiqO0CAAAAkOwZhMqVK9vXX39tW7dutQYNGrgBy+rUqeOChk6dOrkBzfR/w4YNg9tiAAAAlGlZmf+dglo34hggbN682RYtWmQHHHCAe9ykSRNbvny5vf/++27yRq6rX7++dezYMUfQoEACAAAAQAoFCAoA2rRp4z/++eefbdOmTTZv3jw3zZ071/2/evVqmzJlipu8oEFBRffu3e20006zPn36WMWKFUv/0wAAACDpqYvT4NogBLLatBJzN6e1atWynj17usmzbt06P1jwplWrVtmrr75qr732mt133332ySefkFUAAAAA0mEchHr16tnpp5/uJs8vv/xiM2fOtPHjx7vg4K677rIxY8YE8fYAAABIYsoeBDVeAb0YxS5uzThUxeiiiy6yjz76yJo3b27vvvtuvN4aAAAAQBHFvZ232iS0b9/e1qxZE++3BgAAQBKNgxDUhCSsYlSYESNG2NFHH52ItwYAAACQbAGCqhhpAgAAQPphHITkxlASAAAAABKbQQAAAED6CrKtAG0QYpdWAULHYR9aZoUqlsrWzH/f0kG91sdYqhvc/0FLB5e0b2jpoEalkKW67ZkVLB188NPvlg5GvpEen3P5gnqWykJ7diZ6E1AGpVWAAAAAgMRT8iCozoboxCjBbRB2795d5GVXrlwZy1sBAAAASPYAoXv37rZs2bJCl5s8ebJ16NAhlrcCAABAisi0jEAnJDBAWLBggXXs2NHeeuutqM9nZ2fbDTfcYGeffbb98ccfsbwVAAAAgGQPEIYPH26bN2+2Pn362HXXXWd79+71n1u1apUbDG306NFWs2ZNe/vtt0tjewEAAJAibRCCmpDAAOHee++1d99912rVqmVjxoyxo446ylasWGFTpkyx9u3b2+zZs+2II46wr776yk4//fQYNxUAAACpIDMj2AkJ7sXo5JNPdgFA37597YsvvrDDDz/ctm/f7p678cYbbeTIkZaVlRXr2wAAAABpb8+ePbZ27VrbsWOH1alTxxXUJ+VIygcccID9+9//turVq9u2bdvcvEGDBtkDDzxAcAAAAIAcqGJUPFu3brUnnnjCjj32WKtWrZo1adLEWrZs6QKEAw880AYPHmxz5861pAoQ3n//fevSpYtt2bLFWrVq5YKC5557zi699FL7888/S+MtAAAAgLTz8MMPu4BA99Y9evRw7XoXLlxoP/zwg82aNctGjBjh2gH37NnT1exZunRpYqsYhUIhu+2221ymQH8PHTrURo0a5doeqMrR888/b/PmzbPXX3/dmjdvHvPGAgAAoOwLsjvSVOvmdO7cuTZz5kw77LDDoj6vQnoVyivDMGHCBPv000/tkEMOSVwG4fjjj3cBgVIdimYefPBBlz048sgjXWSjSGbx4sXWqVMne/HFF2PaUAAAACDdvPzyy35woDHIVGMnmkqVKtnll1/ugoVYxRQgKELROAgaD+HMM8/M8dx+++1n7733nt1zzz22c+dO1yYBAAAAsCC7OE2tBEIOqqWj++rcFDTccsstVlpiChCGDBlin3/+uasXlZ9bb73Vpk+fbvXr14/lrQAAAIC0dN5559n9999vGRkZtn79+jzPqwdR1eQpLTG1QXjssceKtNwxxxzjqhwBAAAAQY5XkIrjIDRu3NiNMxYOh61t27aupo7+19SuXTtbsmSJNWjQIHnGQfB8++23bhyEDRs2uHpSXpUjNV5Wy+ratWuX1lsBAAAAadWTkVSoUMHV3lmzZo0bh0wF8JMmTXL32+o0KGkChFWrVrn2BR9//LE/b8CAAX6AMH78eLvyyivtgw8+sBNPPDHWtwMAAEAZF2RTgRRMIOSoSlS+fHn3d+/evS0oMbVB2LRpkxuw4aOPPnJZgyuuuMKlPiKdf/75lpmZaZMnT451WwEAAIC0Vf7/goOgxRQgqIvT5cuX24033mhff/21Pf7443mWqVmzph1++OH22WefxfJWAAAASBGZGRmBTkhggPDOO++4Hoy8VtX5adasmasrBQAAACCFA4QVK1ZYhw4dXBWigqhBhaojAQAAAK4NQlBjIbB7ExsgaMS2rVu3FrrcypUrrXr16rG8FQAAAIB8qMD+hBNOsPnz51tCA4QWLVq4UZTVojo/GzdudO0T2rRpE8tbAQAAIEVkBjylo2effdaNPXbVVVfFvK7MWEd1++2332zo0KGu/9VobrrpJtuxY4ddcMEFsbwVAAAAgHwMHDjQ7rzzTps9e7YlNEBQhNK6dWt7+umnrUuXLnbfffe5+T/99JMb0KFbt272wgsvuBHetNEAAACAOrcJckonv/zyi2VnZ5fqOsvF2gbh/ffftz59+rhRlDWim6hLU00aE6Fz58729ttvx63fVgAAACBdnHrqqfbpp5/67X1///13W7p0qSu8T9hIyg0aNHDBgAKFd999137++WdX3ahRo0Z2yimnuFHe0i2SAwAAQP4yM/47BSGo9SarcuXK5egMSH9r8OJYGivHHCB4evXq5SYAAACgIF6XpEFIt3LpAw44wGUQjj76aL83o927d8e0zlILEAAAAADE1+OPP+6qGantr6oVLVq0yBo3bhy/AEHjGcQi1o0FAABA2Rdkd6Tp1s1p48aNXTvgSZMmueDg0EMP9TsOikuA0KRJkxK3J9Dr9u7dW6LXAgAAAOmod+/eNnHiRKtatap9++23bhwyVSOKpM6Azj//fDeVhnLFjVCiBQgrVqzw//YaSWzevNn9r+XJHAAAAMATZHekqdY5TvPmzf1uTDW8gHoRPeyww9wwAm3btvWnatWqJSYLs3z5clu2bJk/abyDTp06We3ate2RRx6xTZs2ua6VvGn06NHuOS2jZQEAAAAU3QMPPODf/G/YsMGmTJliF110kWuIrLHIevToYTVr1rRmzZpZ3759bcKECTGPixBTI2UFBf/5z39s3rx5LqKJpEzCNddcYyeccIJ17NjRHnroITeqMgAAANIb3ZyWzH777efurTV59uzZY999950tXLjQ5s6da7feeqv7f+zYsYlpx6EI5dhjj80THETSc8cdd5w9//zzsbwVAAAAgFzU/qBNmzbWv39/GzNmjCu8f+WVVywWMQUIqjakKkRFiXY0gBoAAAAgGQFN6a5169Z28803Jy5AUGvqOXPmWDgczncZPac0h5YFAAAAEJyKFSvaLbfckrgAQVWHlBlQ24JojSE0TxGMMg1aFgAAAPDaIAQ1wRLXSPmuu+6yqVOnusbKb7zxhut7tWnTpn6PR6+99pobXE3Zg3/84x8xbioAAACAaDQ2ggrk//nPf7oOghIWILRs2dLee+8919WSAgH1VJS7etH+++9vL730krVq1SqmDQUAAEBqYByE0vfss8+6AvqrrrrKZs+enbgAQY466ihbunSpvfnmmzZjxgz75Zdf3HwFBurh6LzzznMDOgAAAAAIxsCBA93/d955Z8zrijlA8BpDKIugCQAAACgI4yAUXe/evW3ixImuyv63335rLVq0cNWJglQqAQIAAACA0te8eXO/MyB1YaqaOYcddpi1a9fO2rZt60/eaMulgQABAAAAcRXkmAWp1onRAw884P+9YcMG+/rrr92kkZOffvppN4ry3r177cADD7QuXbrYySefbJdccollZWUlPkBYvXq1m3bu3JnvMsccc0xpvR0AAABQKsaOHet6/1m7dq0rjdeIxLrZzs/rr79ut99+u2sUfMghh9ioUaPs1FNPDfzb0ODDJ5xwgps8e/bscUGCAgaNPXbrrbe6//WZEhYgvPPOOzZs2DD74YcfCm2trugGAAAA6S0zI8NNQa27OF599VUbOnSojRs3zrp27WqjR4+2Xr162ZIlS6xu3bp5lv/iiy/swgsvtJEjR9rpp5/u2gecddZZtmDBAlcFqLSpp9DGjRvn+3z58uWtTZs2burfv7/rIOicc85JXICgLk7PPfdcC4VCVr16dWvWrFmp1n8CAAAAgvTwww/b4MGDbdCgQe7xuHHj7N1333XdhqoQPLdHH33UVePRQMFy991327Rp0+zxxx93ry1tnTt3dgHIX/7yF/d3NJs3b3bjj2nbFCRooOJYxBQg3HvvvS44UHdK2oEVKlSIaWMAAACQ+lTIH1ACwV/vli1b8vS6qSnS7t27bf78+TZ8+HB/XmZmpvXo0cNmzZoVdf2ar4xDJGUc3n77bQuCei7SPfdJJ53kGihrELSGDRu6v3///Xf3/DfffGMdOnRw7RVKo6pTTAGC6jqpBfUdd9xhZUGPF0dYBQu2WyjEx6lr56T8rj71gUmWDtaorzukhAqzX7d00PatKZYOHl34q6WDUHbIUtn2vXvtFEs+GeGwm4JatzRq1CjH/BEjRuQZI2Djxo2uh6B69erlmF+vXj37/vvvo65f7RSiLa/5QbU7UJZDQYIyG5999pmtWLHC/vzzT6tdu7b169fPBSilWb0ppgBBraPVFysAAACQTFatWpWj6nvu7EFZU7lyZde+QFPQYgoQ1BjCGzkZAAAAKJJw6L9TEP5vvQoOCmsbqxJ4FXivW7cux/x169ZZ/fr1o75G84uzfFkUU32b6667zj7//HObN29e6W0RAAAAEAdqP6s6/dOnT/fnhUIh97hbt25RX6P5kcuLGinnt3xJffjhh3bEEUfYscce6/6WX3/91Z577jnXi1LSBgjqwUh9wKre07/+9S/XDRMAAABQkIxwKNCpONTgePz48fb888+78QSuuOIK2759u9+rkXoFimzEfO2119rUqVPtoYcecu0U1K5BheVDhgwp1S9d67vhhhtc+4NJkya57dGoygoWzjzzTAtSzG0QPFdffbWb8sM4CAAAAEg2F1xwgRuhWJ3uqKFxu3btXADgNURWAbh6NvJ0797djX1w2223uUHJNFCaejAq7TEQ1OagT58+7m9tU506dVyPRQcccIAFLaYAIVyM1ufFWRYAAAApLA5tEIpbWp9fBmDGjBl55unG3bt5D4qCFg3ipgDk0EMPtSZNmsQlOIg5QFAdLQAAAAClS9WLPvjgA1fFSFWfdu3aZb1793bZBE1nn312cgYIAAAAQLGpZklQtUtSpNbK9ddfn+PxsmXLbPHixW568803CRAAAACAdNa0aVM3nXHGGYG/V7EyCF4vRfvvv79roFzcXosaN25cvK0DAABA6kmyNgiIIUBQ4wi14lYLaq+xhHonKgp6MQIAAACKT1WK7r77blu4cKF7PGzYMHcv3rZtW9d7UmmPEl2uuBkA3eiXL18+x2MAAACgqDLC4WKPV1Ccdaea5557zgYOHOg/Hjt2rGVnZ9vOnTtdrZ6WLVvazJkzrUaNGvEPEJYvX17gYwAAAACl65tvvrEHHnggx7xFixa54ED/a7C2cePGucxCaaAXIwAAAMQXbRCK5ddff7Xq1av7jxUYqBaPqvtr0sjPY8aMKbUA4f8PCwcAAAAg6dSuXTtHzR2N+HzggQf6jzUugtoIlxYCBAAAACQmgxDUlGJOOOEEe+aZZ/zHlSpVclkEjzoR2rNnT6m9HwECAAAAkMRuuukmmzhxoj366KNRn//888+tWbNmpfZ+tEEAAABAfNEGoVgOP/xw+/e//20XXXSR/ec//7ErrrjCOnfubOXKlbPPPvvMhg8fnmfk5VgQIAAAACD+AUKIgdKK47zzzrODDjrIBQJ9+vTxhxoIh8NudOWhQ4da2lYxuv/++90Oue666xK9KQAAAEDctG/f3mbMmOEaLE+ePNleeukl+/rrr+2dd97xxylLuwzC3Llz7cknn7Q2bdokelMAAABQQhokLbiB0lKvkXJuGqxYU1BKLYOgrpWefvppGzlypItoPKFQyHbv3h3z+rdt22b9+vWz8ePHW82aNWNeHwAAAIAAAoRVq1ZZjx49XOOJv/3tb3bbbbfZ22+/7T+vG/rKlSvb9OnTY3qfq666yk477TT3XgAAACjD6OY0dQOETZs22bHHHmsfffSRHXbYYa5FtRpKRDr//PNd36yRWYXieuWVV2zBggUuO1EUu3btsi1btuSYAAAAAAQcIIwaNco1krjxxhtdA4nHH388zzKqDqTsgrpgKmmG4tprr3WNMDQoRFEokNBw1N7UqFGjEr03AAAAAqAC5SAnJC5AUIvpJk2a+D0L5UcDN6xZs6ZE7zF//nxbv369dejQwfX1qumTTz6xxx57zP2dnZ2d5zXqC3bz5s3+pCADAAAAKOs+/fRTu/jii61bt262evVqN+/FF18scWF8qQcIK1ascDfuqkJUkAoVKrjqSCVx4okn2qJFi2zhwoX+1KlTJ9dgWX9HDjPtqVixolWrVi3HBAAAgCRBG4QSefPNN61Xr16ufe9XX33lqtWLCsTvu+8+S4oAQVV+tm7dWuhyK1eudFV9SmLfffe11q1b55iqVq1q++23n/sbAAAASAf33HOPjRs3znUCFDnuwZFHHuna6ybFOAgtWrRwG7N9+3Z30x7Nxo0bXfuErl27xvJWAAAASBEZ4XCA4yCkbhuEJUuW2DHHHJNnvgri//jjj+TIIGjI599++80N7azxDqK56aabbMeOHXbBBRdYadEIcqNHjy619QEAAADJrn79+vbjjz/mma/2B2rzmxQBgsYmUDUfDZDWpUsXv+7TTz/9ZA8//LBrPPHCCy9Yu3btbODAgaW1zQAAACjLaINQIoMHD3a9e3755ZeugyB1AqSePtWjqIYbSIoqRmqD8P7771ufPn3siy++cI0lvChGk8ZE6Ny5sxs4LbKeFAAAAIDiGTZsmKu1o058VENH1Y3UOY8ChKuvvtqSIkCQBg0auGBAgcK7775rP//8s9twjT1wyimnWO/evQvsAhUAAABpmkEIat0pKiMjw/7+97+7KvyqarRt2zZr1aqV7bPPPvbnn3+63o2SIkDwqMslTQAAAACCoyEEFBiIujpV1f4HHnjA1q5dm/g2CDNnzrQffvih0OWWLl3qlgUAAABog1A8CgI0ELDGAuvevburvi/PPfecNW3a1B555BG7/vrrrbTEFCAcd9xxNmrUqEKXU0Rz/PHHx/JWAAAAQFq644477IknnrAmTZrY8uXLXfvfv/71ry4wUPZA82655ZZSe7+YqxipITIAAABQVBoDIbhxEFKvDcLrr7/uegY988wzbfHixdamTRvbu3evG2ssiLa+MWUQiur33393PR4BAAAAKJ5ffvnFOnbs6P7WEAPquUhVioLqCKjYGYSVK1fmeKzW07nneRTZfPPNN/bBBx/YQQcdVPKtBAAAQOrQALv5DLJbKutOMdnZ2a5hsqdcuXKu56KgFDtAUN2nyGjlzTffdFNh1ZAuvvjikm0hAAAAUouqqAdVTT0Fq7+Hw2E36LAyB7Jz5067/PLLrWrVqjmWe+uttxITIDRu3NgPEJQ5qFKlitWuXTvqsop0DjjgADv33HNLdXQ3AAAAIF0MGDAgx+OgC96LHSColbQnMzPTtaJ+9tlnS3u7AAAAkKoYKK1Y1J1pPJWLdWMPPvjg0tsaAAAAAAlVrjTTHQAAAEBh6OY0hQIEr7ei/fff37KysvLtvaig9gsAAAAAUiRAUA9Ganfw7bff2qGHHpqnR6OCaDl1ewoAAIA0RxuE1AkQvB6Mypcvn+MxAAAAgDQMECJ7MIr2GAAAACjaOAgBDWiWguMglKlGymVN6w/ftcpV97VUtk+F9PhKOzataaluQ5ok5z7+aZOlg0lfrbZUV6VCS0sHJ/Q/1tJB25urWTrYuGO3pbId27aadT4k0ZuBMiY97iYBAACQPMLZZqHs4NaNmGRaKVIj5IceesiOPvpoa9mypZ100kkMogYAAACkaoDw1ltvWd26de3vf/97nudCoZCddtppdvPNN9vnn39uS5YssenTp9vgwYNt4MCBpbnNAAAAKMPCoVCgE+IYIHz88cf222+/2XnnnZfnufHjx9u0adMsHA7bmWeeaY8//rgLFipXrmwvvviiffDBBzFuKgAAAICkaoPw5ZdfWoMGDax9+/Z5nnvyySddl6d9+/a1l156yZ/fpUsXF1AoSOjZs2fpbDUAAADKrlCAbRCCWm8aKVYG4ddff7V27drlmb9x40ZbuHCh+/umm27K8dw555zjBlRTcAEAAAAghQIEBQI1a+btXnLu3Lnu/zp16kQNIFq1amVr1qyJZTsBAACQahmEoCbEL0DIysqyDRs25Jm/YMEC93+HDh2ivq5GjRquhyMAAAAAKdQG4cADD3TBwO7du61ChQr+fPVWpPYHXbt2zTfzUK9evdi3FgAAAGVeODvbTUGtG3HMIBx//PGuF6Pbb789R89Gn3zyiftb3ZxG89VXX1nDhg1j3FQAAAAASRUgXHfddS5z8OCDD1qjRo1claJevXq555Q96NSpU57XzJo1y1VLyi+7AAAAgDSjsQqCnBC/AOHggw92XZhWrVrVVq9e7XouUtsCZQeef/75qK9R96dy4oknxralAAAASA3uRj6oRsoECHFtg+B1W3rUUUfZlClTbN26dda4cWM766yzXNAQjcZB0LgJJ5xwQswbCwAAACDJAgSpW7euXXrppUVa9sorryzJWwAAACBFhUPZbgpq3YhjFSMAAAAAqa1EGQQAAACgxMIBNibWuhETMggAAAAAfGQQAAAAEFe0QUhuZBAAAAAA+MggAAAAIL68MQuCWjdiQgYBAAAAgI8MAgAAABIwknJAvQ0xknLMyCAAAAAA8JFBAAAAQFyFs7PdFNS6ERsyCAAAAAB8ZBAAAACQgDYIQfVixEjKsSKDAAAAAMBHBgEAAADxxTgISY0MAgAAAAAfGQQAAADEVTgUclNQ60ZsyCAAAAAA8JFBAAAAQHzRBiGpESAAAAAgvsLZwXVzqnUjJlQxAgAAAOAjgwAAAIC4opFyciODAAAAAMBHBgEAAADxpa5Ig2qDQDenMSODAAAAAMBHBgEAAADxRTenSY0MAgAAAAAfGQQAAADEVTg7201BrRuxIYMAAAAAwEcGAQAAAAnoxSgU3LoREzIIAAAAAHwECAAAAEhML0ZBTQHZtGmT9evXz6pVq2Y1atSwyy67zLZt21bg8ldffbU1b97cKleubI0bN7ZrrrnGNm/ebMmMAAEAAAAogn79+tk333xj06ZNsylTptjMmTPtr3/9a77Lr1mzxk0PPvigLV682CZMmGBTp051gUUyow0CAAAA4iocynZTUOsOwnfffedu7ufOnWudOnVy88aMGWOnnnqqCwAaNmyY5zWtW7e2N99803980EEH2b333msXX3yx7d2718qVS85bcTIIAAAASDlbtmzJMe3atSum9c2aNctVK/KCA+nRo4dlZmbal19+WeT1qHqRqigla3AgybtlAbikZQ33haSyrK3rLR3s+fQtS3VLnn/X0sGBK7ZYOnj46KaW6hr2Os7SQWb2fpYOsheutnSwcc7Xlsq27tptySgcCrkpqHVLo0aNcswfMWKE3XnnnSVe79q1a61u3bo55ukmv1atWu65oti4caPdfffdBVZLSgZpFSAAAAAgPaxatSpHwXDFihWjLjds2DAbNWpUodWLYqUsxmmnnWatWrWKKVCJBwIEAAAAxFU4FLZwdlAZhLD7X8FBUWqO3HDDDTZw4MACl2nWrJnVr1/f1q/PWVND7QjUU5GeK8jWrVvt5JNPtn333dcmTZpk5cuXt2RGgAAAAIC4UnAQWIBQzPXWqVPHTYXp1q2b/fHHHzZ//nzr2LGjm/fRRx9ZKBSyrl27Fpg56NWrl8tgTJ482SpVqmTJjkbKAAAAQCFatmzpsgCDBw+2OXPm2Oeff25Dhgyxvn37+j0YrV692lq0aOGe94KDnj172vbt2+2ZZ55xj9VeQVN2dnDjNcSKDAIAAABSrpFyEF566SUXFJx44omu96Jzzz3XHnvsMf/5PXv22JIlS2zHjh3u8YIFC/wejg4++OAc61q2bJk1adLEkhEBAgAAAFAEtWrVsokTJ+b7vG74w+H/toGQ4447LsfjsoIAAQAAAGnbBgF50QYBAAAAgI8MAgAAAOKKDEJyI4MAAAAAwEcGAQAAAHEVzs62UEDdfGrdiA0ZBAAAAAA+MggAAACIq3A4wHEQwvRiFCsyCAAAAAB8ZBAAAAAQV/RilNzIIAAAAADwkUEAAABAXJFBSG5kEAAAAAD4yCAAAAAgrsKhcHC9GIXCgaw3nZSJDMLIkSOtc+fOtu+++1rdunXtrLPOsiVLliR6swAAAICUUyYChE8++cSuuuoqmz17tk2bNs327NljPXv2tO3btyd60wAAAFBMoexQoBPSoIrR1KlTczyeMGGCyyTMnz/fjjnmmIRtFwAAAJBqykSAkNvmzZvd/7Vq1Yr6/K5du9zk2bJlS9y2DQAAAAWjF6PkViaqGEUKhUJ23XXX2ZFHHmmtW7fOt81C9erV/alRo0Zx304AAAAUHCAENSHNAgS1RVi8eLG98sor+S4zfPhwl2XwplWrVsV1GwEAAICyqkxVMRoyZIhNmTLFZs6caQcccEC+y1WsWNFNAAAASD7hcCi4bk7DZBDSIkAIh8N29dVX26RJk2zGjBnWtGnTRG8SAAAAkJLKlZVqRRMnTrR33nnHjYWwdu1aN1/tCypXrpzozQMAAEAx0Eg5uZWJNghPPPGEa0tw3HHHWYMGDfzp1VdfTfSmAQAAACmlzFQxAgAAQGogg5DcykQGAQAAAEB8lIkMAgAAAFKHxrXSFNS6ERsyCAAAAAB8ZBAAAAAQV7RBSG5kEAAAAAD4yCAAAAAgARmE7MDWjdiQQQAAAADgI4MAAACAuAqHQm4Kat2IDRkEAAAAAD4yCAAAAIh/BiGgtgJkEGJHBgEAAACAjwwCAAAA4sv1YhRQWwF6MYoZGQQAAAAAPjIIAAAAiKtQdshNQa0bsSFAAAAAQFzRzWlyo4oRAAAAAB8ZBAAAAMSVGigH1s0pVYxiRgYBAAAAQHpmELKzKroplW3fZ39LB9uOutRS3T7dBlk6qJ5haWFXdthS3b9+2GDp4JnXv7V0sGlVepQhHnZsap9r9/653cz+bckmnB12U1DrRmzS49cPAAAAoEjSKoMAAACAxAuFAuzmNEQ3p7EigwAAAADARwYBAAAAcRUOhd0U1LoRGzIIAAAAAHxkEAAAABBXoWyzUGY4sHUjNmQQAAAAAPjIIAAAACD+IylnMpJysiKDAAAAAMBHBgEAAADxH0k5oDYIjKQcOzIIAAAAAHxkEAAAABBXoexwgL0YMQ5CrMggAAAAAPCRQQAAAEBc0YtRciODAAAAAMBHBgEAAABxFQqHLRQKqA1CmDYIsSJAAAAAQHypm9OMgG7kaaQcM6oYAQAAAPCRQQAAAEBchbJDFsoIBbZuxIYMAgAAAAAfGQQAAADEVTjANghaN2JDBgEAAACAjwwCAAAA4ooMQnIjgwAAAADARwYBAAAAcUUvRsmNDAIAAAAAHxkEAAAAxFU4HLZwKKBejML0YhQrMggAAAAAfGQQAAAAEFeh7LCFLBzYuhEbMggAAAAAfGQQAAAAEP9xECwU2LoRGzIIAAAAAHxkEAAAAJCADEJAvRiRQYgZGQQAAAAAPjIIAAAAiCt6MUpuBAgAAACIq3AoZOGMjMDWjdhQxQgAAACAjwwCAAAA4ooqRsmNDAIAAAAAHxkEAAAAxFU4FGA3pyEGSosVGQQAAAAAPjIIAAAAiK/skIXDwfRiZPRiFDMyCAAAAAB8BAgAAACIfy9GAU5B2bRpk/Xr18+qVatmNWrUsMsuu8y2bdtWpNeGw2E75ZRTLCMjw95++21LZgQIAAAAQBH069fPvvnmG5s2bZpNmTLFZs6caX/961+LtO9Gjx7tgoOygDYIAAAAiKtwdtiVqJelXoy+++47mzp1qs2dO9c6derk5o0ZM8ZOPfVUe/DBB61hw4b5vnbhwoX20EMP2bx586xBgwaW7MggAAAAAIWYNWuWq1bkBQfSo0cPy8zMtC+//DLf1+3YscMuuugiGzt2rNWvX79M7GcyCAAAAIirUDjspqDWLVu2bMkxv2LFim4qqbVr11rdunVzzCtXrpzVqlXLPZef66+/3rp37269e/e2siKtAoRMC7splVXJSu3P56lqey3VZe7+zdJBeNlCSwdr3p5sqa7W03MsHRy96c9EbwJK0aVbm6X0/ty2d6+daOmpUaNGOR6PGDHC7rzzzjzLDRs2zEaNGlVo9aKSmDx5sn300Uf21VdfWVmSVgECAAAAEi87HHZTUOuWVatWud6GPPllD2644QYbOHBggets1qyZqx60fv36HPP37t3rejbKr+qQgoOffvrJVU2KdO6559rRRx9tM2bMsGREgAAAAICUo+AgMkDIT506ddxUmG7dutkff/xh8+fPt44dO/oBQCgUsq5du0Z9jbITf/nLX3LMO/zww+2RRx6xM844w5IVAQIAAADiSkMVBDVcQVDrbdmypZ188sk2ePBgGzdunO3Zs8eGDBliffv29XswWr16tZ144on2wgsvWJcuXVxmIVp2oXHjxta0aVNLVvRiBAAAABTBSy+9ZC1atHBBgLo3Peqoo+ypp57yn1fQsGTJEtdzUVlGBgEAAAAp1wYhCLVq1bKJEyfm+3yTJk0KHd8hqPEfShMZBAAAAAA+MggAAACIq7LYBiGdECAAAAAgrjSYWVBVgYIagC2dUMUIAAAAgI8MAgAAAOIqO8CqQFo3YkMGAQAAAICPDAIAAADi382plb1uTtMFGQQAAAAAPjIIAAAAiH83pwGuG7EhgwAAAADARwYBAAAAcUUGIbmRQQAAAADgI4MAAACAuKIXo+RGBgEAAABA2QwQxo4da02aNLFKlSpZ165dbc6cOYneJAAAABRTSL0YBTRp3UiTAOHVV1+1oUOH2ogRI2zBggXWtm1b69Wrl61fvz7RmwYAAACkjDITIDz88MM2ePBgGzRokLVq1crGjRtnVapUsWeffTbRmwYAAIDitkEIcEIaBAi7d++2+fPnW48ePfx5mZmZ7vGsWbPyLL9r1y7bsmVLjgkAAABAigQIGzdutOzsbKtXr16O+Xq8du3aPMuPHDnSqlev7k+NGjWK49YCAACgIEG1P/AmpEGAUFzDhw+3zZs3+9OqVasSvUkAAABAmVAmxkGoXbu2ZWVl2bp163LM1+P69evnWb5ixYpuAgAAQLKOpBxMUT8ZhDTJIFSoUME6duxo06dP9+eFQiH3uFu3bgndNgAAACCVlIkMgqiL0wEDBlinTp2sS5cuNnr0aNu+fbvr1QgAAABlLYMQ3LqRJgHCBRdcYBs2bLA77rjDNUxu166dTZ06NU/DZQAAACQ31x1pYFWMiBDSJkCQIUOGuAkAAABAMMpUgAAAAICyT2X8oQDXjTRopAwAAAAgPsggAAAAIK5og5DcyCAAAAAA8JFBAAAAQFzRzWlyI4MAAAAAwEcGAQAAAHFFG4TkRgYBAAAAgI8MAgAAAOKKNgjJjQwCAAAAAB8ZBAAAAMQVbRCSGxkEAAAAAD4yCAAAAIirUNgsO8B1IzZkEAAAAAD4yCAAAAAgrmiDkNzIIAAAAADwkUEAAABAXGX/31gIQa0bsUmLACEc/u8RuHXrVkt54ZClg4zsPZbqMnekwfGqQ3b7DksHW3fttlT3Zzg9Lsu7LT3Os+li2969lsq2/9/n8+6F0uF3xG80dhnhZDtiAvDLL79Yo0aNEr0ZAAAACbFq1So74IADEr73d+7caU2bNrW1a9cG+j7169e3ZcuWWaVKlQJ9n1SVFgFCKBSyNWvW2L777msZGRmWzrZs2eKCJZ0oqlWrlujNSUt8B+z/dMbxz/5PZ4k4/nWbpxoUDRs2tMzM5Gh6qiBh9+5gs6oVKlQgOIhBWlQx0g8iGaLmZKITEwEC30E64zfA/k9nHP/ptf+rV69uyUSl+pTsJ7fkCCUBAAAAJAUCBAAAAAA+AoQ0U7FiRRsxYoT7H3wH6YjfAPs/nXH8s/+BokiLRsoAAAAAioYMAgAAAAAfAQIAAAAAHwECAAAAAB8BQhqZOXOmnXHGGW6wFA0Y9/bbbyd6k9LGyJEjrXPnzm6wvrp169pZZ51lS5YsSfRmpZUnnnjC2rRp4/c/3q1bN3vvvfcSvVlp6f7773fnoOuuuy7Rm5I27rzzTrfPI6cWLVokerPSyurVq+3iiy+2/fbbzypXrmyHH364zZs3L9GbBURFgJBGtm/fbm3btrWxY8cmelPSzieffGJXXXWVzZ4926ZNm2Z79uyxnj17uu8E8aHBEnVjOn/+fHdRPuGEE6x37972zTff8BXE0dy5c+3JJ590wRri67DDDrNff/3Vnz777DO+gjj5/fff7cgjj7Ty5cu7golvv/3WHnroIatZsybfAZJSWoykjP865ZRT3IT4mzp1ao7HEyZMcJkE3awec8wxfCVxoOxZpHvvvddlFRS06cYJwdu2bZv169fPxo8fb/fccw+7PM7KlStn9evXZ78nwKhRo6xRo0b23HPP+fOaNm3Kd4GkRQYBSIDNmze7/2vVqsX+T4Ds7Gx75ZVXXAZHVY0QH8qinXbaadajRw92eQIsXbrUVTFt1qyZC9RWrlzJ9xAnkydPtk6dOlmfPn1c4VD79u1doAwkKzIIQJyFQiFX91rp5tatW7P/42jRokUuINi5c6fts88+NmnSJGvVqhXfQRwoIFuwYIGrYoT469q1q8tcNm/e3FUv+sc//mFHH320LV682LWNQrB+/vlnl7EcOnSo3Xrrre53cM0111iFChVswIAB7H4kHQIEIAGlqLooU/83/nRztHDhQpfBeeONN9yFWe1DCBKCtWrVKrv22mtd+5tKlSoF/G6IJrJ6qdp/KGA48MAD7bXXXrPLLruMnRaHgiFlEO677z73WBkEXQfGjRtHgICkRBUjII6GDBliU6ZMsY8//tg1mkV8qbTu4IMPto4dO7qepdRo/9FHH+VrCJja2qxfv946dOjg6sFrUmD22GOPub9V5QvxVaNGDTv00EPtxx9/ZNfHQYMGDfIURLRs2ZJqXkhaZBCAOAiHw3b11Ve7Ki0zZsygcVoSlert2rUr0ZuR8k488URXvSvSoEGDXDebt9xyi2VlZSVs29K5wfhPP/1kl1xySaI3JS2oSmnurq1/+OEHl8UBkhEBQppdECJLi5YtW+aqW6ihbOPGjRO6belQrWjixIn2zjvvuPq+a9eudfOrV6/u+sNG8IYPH+6qWehY37p1q/s+FKy9//777P6A6ZjP3d6matWqrj942uHEx4033uh68tIN6Zo1a2zEiBEuMLvwwgvjtAXp7frrr7fu3bu7Kkbnn3++zZkzx5566ik3AcmIACGNqO/3448/3n+sxlKiethqvIbgqHGaHHfccTnmq8u7gQMHsuvjQFVc+vfv7xpoKjBTPWwFByeddBL7Hynvl19+ccHAb7/9ZnXq1LGjjjrKdfGrvxE8DZSpDLIKKu666y6XRR49erTrTQpIRhlh1X0AAAAAABopAwAAAIhEL0YAAAAAfAQIAAAAAHwECAAAAAB8BAgAAAAAfAQIAAAAAHwECAAAAAB8BAgAAAAAfAQIAAAAAHwECACAIvnxxx/t8ssvtw4dOlj58uWtSZMm7DkASEHlEr0BAICy4ZtvvrEpU6ZYly5dLBwO2++//57oTQIABCAjrLM8AACFCIVClpn538SzMglTp0615cuXs98AIMVQxQgAULQLxv8FBwCA1MbZHkBgMjIyij0dd9xx7rWq367HqV5C/dVXX1lWVpZdffXVMa1nxIgRbn+99tprOeY/++yzbn6bNm0sXjZv3mz77befde3a1VVFAgCULbRBABCYAQMG5Jm3du1ae//99/N9vkWLFmn1jSgwqFy5st1+++15ntu2bZvVqFHDPf/HH3+4QCI/b775plWqVMlOPfXUHPPnz5/v/u/UqZPFS/Xq1W348OF200032QsvvBD1ewYAJC8CBACBmTBhQp55M2bM8AOEaM97pk+fbnv27LH9998/Zb+hN954wz7//HN3I123bt08z8+ZM8eys7Ndo+CCgoMlS5a4BsS9e/e2ffbZp0gBgvb9oEGDCt3G119/3c477zwrriFDhtgDDzzgAoW+fftaxYoVi70OAEBiECAASEoHHXSQpbpHHnnE/X/ZZZdFfX7WrFnu/27duhW4HmUP5Jxzzskxf+/evfb111+7vzt27JjjubPPPtuOOOKIQrexpAGashkXXXSRPfroo/bqq69a//79S7QeAED80QYBQFLKrw2C11ZB/v3vf7vSdZWa16lTxy688EJbuXKle0513x9//HFr166dVa1a1WrXrm0DBw609evX5/uef/75pz300EPuxllVe3ST27x5c7v55pvtt99+K/W2B1988YV7L71HpBdffNF9xttuu809vvfee3O003j77bdzLP/WW2+5cQnOPPPMHPO//fZb27lzp5UrV87atm2bpxqQqnMVNu27774l/oza3zJ27NgSrwMAEH8ECADKJFVdURUZ3cCecsopVqVKFXvllVfsqKOOcv3zq1qLqu40aNDAevXq5aroPP/883bSSSfZ7t2786xvzZo1rlHtjTfeaEuXLrXOnTu7+vy7du2yf/7zn66KzooVK0pt+72b/B49euR5Tp9F9fYVoMgFF1zgHnuTgiKPAihVIzrhhBNcUBOtetFhhx3mryueFJwpcFNVqV9//TXu7w8AKBmqGAEok8aPH2/z5s3zS8ZV+t+zZ0/77LPP7Nhjj7UdO3bY999/bwceeKB7fuPGja6qzv/+7/+6evX9+vXz16Vsw/nnn2+LFi1y1X1U9ccrOVc1nWHDhrnMggKSjz76qFS231tPtOpD5557rgtk1MC3WrVq9vLLL/tZk9yUPfBek1tpN1DWPv2f//kf9/fPP//sHqsdhSig8vZ1JH2+yZMnuzYlF198calsBwAgWGQQAJRJd911V45qM+rpZ+jQoe5v3eg/9thjOW5YVcXoiiuucH/rZjWSGk2rsbBKvMeNG5ejWo2q56ixbevWre3jjz+2xYsXl1oVI2nZsmXU53Vzr8ClQ4cO+QYHXvsDjU+gBsrR1hGt/UFJqXpWnz593DRt2jTbsGGD/1j7JhplL2TBggWlsg0AgOCRQQBQJuXuzlMOOeQQ/6Ze2YT8nld1okjvvvuuXwqv1+amG/BjjjnGBQdqN6BgIRbbt293k2i8gGiKcnOvajtqyKxty90Lkno/8hool1YGQe1Cijuugff51q1bVyrbAAAIHhkEAGVS48aN88zzuvhUu4NoN/peZkANdyOpuoxoLIL8BnD717/+5ZZRqXlpDCSWe5tyU/Wpwm7uJ02a5G7Yo1UvUgNlVbtS4+V4DpKWm6pIidqFAADKBjIIAMokleqX5LloQqGQ+18NnAvrXtWrMhOLyMbEW7du9W+ii5tBUPUiBS+5uzeNDDCU7UjkGAReMFSzZs2EbQMAoHgIEACkvUaNGrl9oHr86sUoaOqlSF2vqpqRuk/NHSDopvqnn35yXZEefPDBUdeh133yySeuR6NoYxWUdvuDkvK6h61Xr15CtwMAUHRUMQKQ9tRNqqh3o+LWsS8pNT72qgLlplGRtR2qGpRfA+V33nnHtTOIVr0oiB6MSspr1J3oQAUAUHQECADSnjIH6qZT/fWrK9No7QxUh149HKnb09Jw/PHH5xgtOdKePXvc/+pGtLDRk6MFCJENlBN9Y+59Po3TAAAoGwgQAKQ9tVnQwGXq5lSDqTVt2tSOPPJINzKzbsDbt2/vBvxSN6mlFSCcddZZ7n91F5qbum9VnX1lAVSFqH///m5U4hkzZvhVkD788EO3vc2aNcu3gbLaHiSygbK6clUVI30GNRwHAJQNBAgAYGYNGza02bNnuyyBbmiXLFniBgHTwGty+eWXu/ESSmtEYgUd3bt3d1mL7777Lk8j5ilTptiJJ57oelh68cUXXeCi0aBFz2k06GiNk8Xb5iOOOMIqVKiQsO93woQJ7v+rrroqYdsAACi+jHC8KtwCAHJQAKJBxjTAm0ZqLiplNTSCstoqtGrVKs/zGhfh008/tccffzxhN+fqSlaNv9XN6rJlyxLakxIAoHjIIABAgpx33nmuKtOTTz5ZrIHElBkYNWpU1OBA4zUoOKhfv74NGDDAEmXMmDG2ceNGGzlyJMEBAJQxZBAAIIFUT189Dal9g0r8S0KjO+uGXBmFRYsWuW5UVQ3Jawgdb2ojobYR6qJV1bby64kJAJCcCBAAoIzT2A2PPfaYawis3oKGDRtmzZs3T/RmAQDKKAIEAAAAAD7aIAAAAADwESAAAAAA8BEgAAAAAPARIAAAAADwESAAAAAA8BEgAAAAAPARIAAAAADwESAAAAAA8BEgAAAAAPARIAAAAADwESAAAAAA8BEgAAAAAPARIAAAAAAwz/8DoXa4kimnlTMAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 800x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "q_res, w_res = 100, 100\n",
+    "spectrum = get_spectrum(n, Gjjc, dt, time_steps, q_res, w_res)\n",
+    "spectrum = -(spectrum + spectrum[:, ::-1]) / 2  # mirror symmetry\n",
+    "spectrum = np.clip(spectrum, a_min=0, a_max=None)  # clip negatives\n",
+    "\n",
+    "plot_green(\n",
+    "    n,\n",
+    "    Gjjc,\n",
+    "    time_steps,\n",
+    "    dt,\n",
+    "    title=f\"Retarded Green's function - {n} qubits (AQC, statevector)\",\n",
+    ")\n",
+    "plot_spectrum(\n",
+    "    spectrum,\n",
+    "    dt,\n",
+    "    q_res,\n",
+    "    w_res,\n",
+    "    lower_bound=True,\n",
+    "    upper_bound=True,\n",
+    "    title=f\"Dynamical structure factor - {n} qubits (AQC, statevector)\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "large-md",
+   "metadata": {},
+   "source": [
+    "## Large-scale hardware example\n",
+    "\n",
+    "The same workflow scales up without changing any of the science code: a 30-site chain, twice the Trotter depth (20 steps), a compression plan that varies the ansatz depth (a deeper ansatz for the later, more-entangled steps), and execution on a real IBM Quantum processor with the function's built-in error mitigation (dynamical decoupling, Pauli twirling, and twirled readout error extinction, or TREX). We walk through the same four steps as the simulator example, reusing the `fn` handle from Setup.\n",
+    "\n",
+    "|                                  | Small scale   | Large scale                           |\n",
+    "| -------------------------------- | ------------- | ------------------------------------- |\n",
+    "| Qubits                           | 10            | 30                                    |\n",
+    "| Trotter steps                    | 10            | 20                                    |\n",
+    "| AQC segments (1-layer + 2-layer) | 3 + 2 = 5     | 6 + 4 = 10                            |\n",
+    "| Ground-state ansatz layers       | 3             | 5                                     |\n",
+    "| MPS max bond dimension           | 32            | 128                                   |\n",
+    "| Backend                          | `statevector` | QPU with DD, Pauli twirling, and TREX |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "large-s1-md",
+   "metadata": {},
+   "source": [
+    "### Step 1: Map classical inputs to a quantum problem\n",
+    "\n",
+    "Build the same KCuF$_3$ Heisenberg `SparsePauliOp` and prepare the ground state, now with a deeper `gs_layers=5` ansatz for the longer chain, then bake in the $\\pi/2$ $Z$ neutron kick at the center site. This is identical to the small-scale mapping, just at $n = 30$.\n",
+    "\n",
+    "Expect a lower ground-state fidelity than the 10-site run: around 0.82 here against 0.98 above, because five HVA layers cannot fully capture a 30-site ground state. That is expected rather than a failure, and the original tutorial accepts roughly 0.65 at 50 sites for the same reason. Raising `gs_layers` or the COBYQA iteration cap improves it, at extra classical cost."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "large-code",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DMRG ground-state energy: -13.111355\n",
+      "GS fidelity: 0.8201\n",
+      "Prepared 30-qubit ground state with the neutron kick at site 14.\n"
+     ]
+    }
+   ],
+   "source": [
+    "n = 30\n",
+    "dt = 0.6\n",
+    "time_steps = 20\n",
+    "center = n // 2 - 1\n",
+    "\n",
+    "# Same MPS settings as the original large-scale run: a larger bond for the\n",
+    "# longer, more-entangled chain (shared by GS prep and AQC compression).\n",
+    "mps_max_bond = 128\n",
+    "mps_cutoff = 1e-8\n",
+    "\n",
+    "# Same KCuF3 Hamiltonian and ground-state prep, on a larger chain\n",
+    "H = SparsePauliOp.from_sparse_list(\n",
+    "    [(p, [i, i + 1], 0.25) for i in range(n - 1) for p in (\"XX\", \"YY\", \"ZZ\")],\n",
+    "    num_qubits=n,\n",
+    ")\n",
+    "gs_circuit = prepare_ground_state(\n",
+    "    n, gs_layers=5, max_bond=mps_max_bond, cutoff=mps_cutoff\n",
+    ")\n",
+    "gs_circuit.rz(np.pi / 2, center)  # neutron kick at the center site\n",
+    "print(f\"Prepared {n}-qubit ground state with the neutron kick at site {center}.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "large-s3-md",
+   "metadata": {},
+   "source": [
+    "### Steps 2 and 3: Compress and execute with the Qiskit Function\n",
+    "\n",
+    "The same single call as the simulator example, now with `backend_name` pointing at a real IBM Quantum processor, so the function transpiles and executes there. The compression plan varies the ansatz depth: the first 6 (low-entanglement) Trotter steps compress into a shallow 1-layer ansatz, the next 4 into a deeper 2-layer ansatz, and the remaining 10 of the 20 steps run as plain Trotter. `aqc_options` raises the MPS bond dimension to `max_bond=128` for the longer, more-entangled chain (matching the original), keeping the same L-BFGS-B optimizer capped at 100 iterations. The `estimator_options` turn on the built-in error mitigation: dynamical decoupling (XY4), gate twirling, and TREX measurement mitigation. The function's defaults already match the original tutorial for all of these except the TREX learning budget (`measure_noise_learning`), which is the only genuine difference. The whole block is still written out because a caller-supplied `estimator_options` replaces the function's defaults wholesale instead of merging into them, so omitting a key would fall back to the Qiskit Runtime default rather than the function's."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "large-s3-run",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "job id (save this to reconnect later): 43ed8d07-6d7d-4f33-b70a-7f31b765b310\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Steps 2 + 3: the function compresses (varied ansatz) and executes on hardware.\n",
+    "job = fn.run(\n",
+    "    t_steps=time_steps,\n",
+    "    aqc_segments=[\n",
+    "        {\"n_steps\": 6, \"ansatz_steps\": 1},  # early steps -> shallow 1-layer ansatz\n",
+    "        {\"n_steps\": 4, \"ansatz_steps\": 2},  # later steps -> deeper 2-layer ansatz\n",
+    "    ],\n",
+    "    aqc_options={\n",
+    "        \"max_bond\": mps_max_bond,  # 128 for the longer chain\n",
+    "        \"cutoff\": mps_cutoff,\n",
+    "        \"optimizer_settings\": {\n",
+    "            \"method\": \"L-BFGS-B\",\n",
+    "            \"jac\": True,\n",
+    "            \"options\": {\"maxiter\": 100},\n",
+    "        },\n",
+    "    },\n",
+    "    dt=dt,\n",
+    "    hamiltonian=H,\n",
+    "    initial_state=gs_circuit,\n",
+    "    backend_name=\"ibm_pittsburgh\",\n",
+    "    # Mitigation settings from the original tutorial. Only the two\n",
+    "    # measure_noise_learning values differ from the function's defaults; the rest\n",
+    "    # restates them, because a caller-supplied estimator_options dict replaces the\n",
+    "    # function's defaults wholesale rather than merging into them.\n",
+    "    estimator_options={\n",
+    "        \"environment\": {\"job_tags\": [\"TUT-SNS\"]},\n",
+    "        \"dynamical_decoupling\": {\"enable\": True, \"sequence_type\": \"XY4\"},\n",
+    "        \"twirling\": {\n",
+    "            \"enable_gates\": True,\n",
+    "            \"num_randomizations\": 1000,\n",
+    "            \"shots_per_randomization\": 128,\n",
+    "        },\n",
+    "        \"resilience\": {\n",
+    "            \"measure_mitigation\": True,\n",
+    "            \"measure_noise_learning\": {\n",
+    "                \"num_randomizations\": 32,\n",
+    "                \"shots_per_randomization\": 100,\n",
+    "            },\n",
+    "        },\n",
+    "    },\n",
+    ")\n",
+    "print(\"job id (save this to reconnect later):\", job.job_id)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "large-reconnect-md",
+   "metadata": {},
+   "source": [
+    "<Admonition type=\"note\" title=\"Reconnecting to a long-running job\">\n",
+    "\n",
+    "The large-scale run is not quick, and most of the time is classical rather than on the QPU. The AQC compression runs inside the function before anything reaches the QPU: at 30 sites with `max_bond=128` that took close to four hours in our run, against the roughly 18 minutes of QPU time quoted in the *Usage estimate* above. Queue wait is on top of both. You do not need to keep this notebook or kernel open while it runs.\n",
+    "\n",
+    "Copy the job id printed above and save it. The next three cells let you pick the run back up later:\n",
+    "\n",
+    "1. Reconnect, only needed in a new kernel session: re-run the [Setup](#setup) cells to recreate `serverless`, then rebuild the `job` handle from the id you saved. Skip this cell if you are still in the session where you submitted, because the handle is already live.\n",
+    "2. Check status: re-run until it reports `DONE`.\n",
+    "3. Fetch the result: run only once the status is `DONE`.\n",
+    "\n",
+    "The reconnect cell below carries the job id from our own run. Paste yours over there:\n",
+    "\n",
+    "</Admonition>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "large-reconnect",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Reconnect to a previously submitted job by its id. Only needed in a NEW kernel\n",
+    "# session; if you are still in the session where you submitted, the `job` handle\n",
+    "# above is already live, so skip this cell. Replace the id below with your own.\n",
+    "job = serverless.get_job_by_id(\"<your job id>\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "large-status",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DONE\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check where the job is. Re-run this until it reports DONE before fetching the\n",
+    "# result below: OPTIMIZING_FOR_HARDWARE -> WAITING_FOR_QPU -> EXECUTING_QPU ->\n",
+    "# POST_PROCESSING -> DONE.\n",
+    "print(job.status())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "large-s3-result",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AQC fidelities: {'1': 1.0, '2': 0.9994, '3': 0.9944, '4': 0.9853, '5': 0.9747, '6': 0.959, '7': 0.9495, '8': 0.9542, '9': 0.9533, '10': 0.9451}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Run this only once the status cell above reports DONE. result() blocks until\n",
+    "# the job finishes, so calling it earlier just waits (possibly for hours).\n",
+    "result = job.result()\n",
+    "print(\n",
+    "    \"AQC fidelities:\",\n",
+    "    {k: round(v, 4) for k, v in result[\"metadata\"][\"aqc_fidelities\"].items()},\n",
+    ")\n",
+    "\n",
+    "ev = np.array(result[\"expectation_values\"])\n",
+    "Gjjc = ev[1:]  # drop the t = 0 row -> shape (time_steps, n)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "large-s4-md",
+   "metadata": {},
+   "source": [
+    "### Step 4: Post-process and return result in desired classical format\n",
+    "\n",
+    "Identical post-processing to the simulator run: Fourier-transform the Green's function into $S(q, \\omega)$, mirror-symmetrize, and clip negatives. With the longer chain and evolution the two-spinon continuum is far better resolved. It should fill the band between the dashed bounds, brightest near $q = \\pi$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "large-result",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 800x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "n = job.result()[\"metadata\"][\"n\"]\n",
+    "q_res, w_res = 100, 100\n",
+    "spectrum = get_spectrum(n, Gjjc, dt, time_steps, q_res, w_res)\n",
+    "spectrum = -(spectrum + spectrum[:, ::-1]) / 2  # mirror symmetry\n",
+    "spectrum = np.clip(spectrum, a_min=0, a_max=None)  # clip negatives\n",
+    "\n",
+    "plot_green(\n",
+    "    n,\n",
+    "    Gjjc,\n",
+    "    time_steps,\n",
+    "    dt,\n",
+    "    title=f\"Retarded Green's function - {n} qubits (AQC, hardware)\",\n",
+    ")\n",
+    "plot_spectrum(\n",
+    "    spectrum,\n",
+    "    dt,\n",
+    "    q_res,\n",
+    "    w_res,\n",
+    "    lower_bound=True,\n",
+    "    upper_bound=True,\n",
+    "    title=f\"Dynamical structure factor - {n} qubits (AQC, hardware)\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "nextsteps",
+   "metadata": {},
+   "source": [
+    "## Next steps\n",
+    "\n",
+    "<Admonition type=\"tip\" title=\"Recommendations\">\n",
+    "\n",
+    "- Adapt this workflow to your own system: the function is Hamiltonian-agnostic, so a different `SparsePauliOp`, initial state, or set of observables runs the same PRE &rarr; FUNCTION &rarr; POST pipeline. See the full input/output contract in the [AQC Dynamics Template](https://github.com/qiskit-community/qiskit-function-templates/tree/main/physics/aqc_trotter).\n",
+    "- Read the paper this benchmark comes from: Lee et al., [*Benchmarking quantum simulation with neutron-scattering experiments*](https://arxiv.org/abs/2603.15608) (arXiv:2603.15608).\n",
+    "- Compare with the [original \"Simulate neutron scattering\" tutorial](/docs/tutorials/simulate-neutron-scattering), the inline workflow this one ports onto a Qiskit Function.\n",
+    "- Go deeper on the [error mitigation and suppression techniques](/docs/guides/error-mitigation-and-suppression-techniques) applied on the hardware run: dynamical decoupling, Pauli twirling, and TREX.\n",
+    "\n",
+    "</Admonition>"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 08088ce280db4eed998840185f2262648f3aee86 Mon Sep 17 00:00:00 2001
From: Parth Danve <parth.danve@uconn.edu>
Date: Tue, 11 Aug 2026 16:37:59 -0400
Subject: [PATCH 2/7] CI, Workflow etc

---
 docs/guides/_toc.json                         |   4 ++
 .../function-template-aqc-trotter.ipynb       |  30 +++++++--
 docs/tutorials/_toc.json                      |   4 ++
 docs/tutorials/index.mdx                      |   2 +
 ...on-scattering-with-a-qiskit-function.ipynb |  62 +++++++++++++-----
 .../extracted-outputs/large-result-0.avif     | Bin 0 -> 14938 bytes
 .../extracted-outputs/large-result-1.avif     | Bin 0 -> 24495 bytes
 .../extracted-outputs/small-s4-code-0.avif    | Bin 0 -> 10564 bytes
 .../extracted-outputs/small-s4-code-1.avif    | Bin 0 -> 21467 bytes
 qiskit_bot.yaml                               |   4 ++
 scripts/config/notebook-testing.toml          |   5 ++
 11 files changed, 86 insertions(+), 25 deletions(-)
 create mode 100644 public/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/large-result-0.avif
 create mode 100644 public/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/large-result-1.avif
 create mode 100644 public/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/small-s4-code-0.avif
 create mode 100644 public/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/small-s4-code-1.avif

diff --git a/docs/guides/_toc.json b/docs/guides/_toc.json
index c544199ef51..40392520d78 100644
--- a/docs/guides/_toc.json
+++ b/docs/guides/_toc.json
@@ -825,6 +825,10 @@
             {
               "title": "Template for Hamiltonian simulation",
               "url": "/docs/guides/function-template-hamiltonian-simulation"
+            },
+            {
+              "title": "Template for AQC + Trotter Hamiltonian dynamics",
+              "url": "/docs/guides/function-template-aqc-trotter"
             }
           ]
         },
diff --git a/docs/guides/function-template-aqc-trotter.ipynb b/docs/guides/function-template-aqc-trotter.ipynb
index 8e9e3c77d83..3f73a51b881 100644
--- a/docs/guides/function-template-aqc-trotter.ipynb
+++ b/docs/guides/function-template-aqc-trotter.ipynb
@@ -15,6 +15,16 @@
     "# Deploy and run the AQC-Trotter-dynamics Qiskit Function"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "version-info",
+   "metadata": {
+    "tags": [
+     "version-info"
+    ]
+   },
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "id": "overview",
@@ -33,7 +43,7 @@
     "\n",
     "The template is published in the Qiskit function templates repository, alongside the other application templates. See: [AQC Dynamics Template](https://github.com/qiskit-community/qiskit-function-templates/tree/main/physics/aqc_trotter). This notebook deploys it to your own IBM Quantum&reg; Serverless account. Run it once, and any notebook can then call the function with `serverless.load(\"aqc-dynamics-function\")`.\n",
     "\n",
-    "For a worked scientific example, see [Simulate neutron scattering with an AQC-dynamics Qiskit Function](neutron-scattering-with-qiskit-functions.ipynb), which calls this function to compute the dynamical structure factor of KCuF$_3$. This notebook covers deployment and the input contract instead."
+    "For a worked scientific example, see [Simulate neutron scattering with an AQC-dynamics Qiskit Function](/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function), which calls this function to compute the dynamical structure factor of KCuF$_3$. This notebook covers deployment and the input contract instead."
    ]
   },
   {
@@ -352,8 +362,14 @@
     "job = fn.run(\n",
     "    t_steps=8,\n",
     "    aqc_segments=[\n",
-    "        {\"n_steps\": 4, \"ansatz_steps\": 1},  # early steps -> shallow 1-layer ansatz\n",
-    "        {\"n_steps\": 2, \"ansatz_steps\": 2},  # later steps -> deeper 2-layer ansatz\n",
+    "        {\n",
+    "            \"n_steps\": 4,\n",
+    "            \"ansatz_steps\": 1,\n",
+    "        },  # early steps -> shallow 1-layer ansatz\n",
+    "        {\n",
+    "            \"n_steps\": 2,\n",
+    "            \"ansatz_steps\": 2,\n",
+    "        },  # later steps -> deeper 2-layer ansatz\n",
     "    ],\n",
     "    hamiltonian=H,\n",
     "    aqc_options={\"max_bond\": 32},\n",
@@ -541,10 +557,10 @@
     "\n",
     "<Admonition type=\"tip\" title=\"Recommendations\">\n",
     "\n",
-    "- Work through [Simulate neutron scattering with an AQC-dynamics Qiskit Function](neutron-scattering-with-qiskit-functions.ipynb), the companion example that calls this deployed function to compute the dynamical structure factor of KCuF$_3$.\n",
+    "- Work through [Simulate neutron scattering with an AQC-dynamics Qiskit Function](/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function), the companion example that calls this deployed function to compute the dynamical structure factor of KCuF$_3$.\n",
     "- Read the [AQC Dynamics Function Template Github](https://github.com/qiskit-community/qiskit-function-templates/blob/main/physics/aqc_trotter/) for the complete input and output contract, further examples, and citation details.\n",
     "- Browse the [Qiskit function templates repository](https://github.com/qiskit-community/qiskit-function-templates/tree/main/physics/aqc_trotter) for other application templates built the same way.\n",
-    "- Read the [Qiskit Serverless guide](https://quantum.cloud.ibm.com/docs/en/guides/serverless) for managing deployed functions.\n",
+    "- Read the [Qiskit Serverless guide](/docs/guides/serverless) for managing deployed functions.\n",
     "- Go deeper on the AQC compression stage with the [Qiskit addon: AQC-Tensor](https://qiskit.github.io/qiskit-addon-aqc-tensor/) documentation.\n",
     "\n",
     "</Admonition>"
@@ -553,7 +569,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "neutron-scattering",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -567,7 +583,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.13"
+   "version": "3"
   }
  },
  "nbformat": 4,
diff --git a/docs/tutorials/_toc.json b/docs/tutorials/_toc.json
index 4e7370c233d..3787f0a0638 100644
--- a/docs/tutorials/_toc.json
+++ b/docs/tutorials/_toc.json
@@ -61,6 +61,10 @@
               "title": "Simulate neutron scattering in quantum materials with quantum circuits",
               "url": "/docs/tutorials/simulate-neutron-scattering"
             },
+            {
+              "title": "Simulate neutron scattering with an AQC-dynamics Qiskit Function",
+              "url": "/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function"
+            },
             {
               "title": "Krylov quantum diagonalization of lattice Hamiltonians",
               "url": "/docs/tutorials/krylov-quantum-diagonalization"
diff --git a/docs/tutorials/index.mdx b/docs/tutorials/index.mdx
index b3792b5ddbf..636e7bd2968 100644
--- a/docs/tutorials/index.mdx
+++ b/docs/tutorials/index.mdx
@@ -51,6 +51,8 @@ These tutorials focus on estimating physically meaningful quantities, such as en
 
 * [Simulate neutron scattering in quantum materials with quantum circuits](/docs/tutorials/simulate-neutron-scattering)
 
+* [Simulate neutron scattering with an AQC-dynamics Qiskit Function](/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function)
+
 * [Krylov quantum diagonalization of lattice Hamiltonians](/docs/tutorials/krylov-quantum-diagonalization)
 
 * [Nishimori phase transition](/docs/tutorials/nishimori-phase-transition)
diff --git a/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function.ipynb b/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function.ipynb
index f38200c2914..5430b8f60b0 100644
--- a/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function.ipynb
+++ b/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function.ipynb
@@ -10,7 +10,7 @@
     "description: Compute the dynamical structure factor S(q, w) of the quantum magnet KCuF3 by running an AQC-compressed Trotter workflow as a Qiskit Function.\n",
     "---\n",
     "\n",
-    "{/* cspell:ignore Trotter Trotterization spinon spinons KCuF quimb DMRG magnon antiferromagnetic Suzuki fidelities isa COBYQA */}\n",
+    "{/* cspell:ignore Trotter Trotterization spinon spinons KCuF quimb DMRG magnon antiferromagnetic Suzuki fidelities isa COBYQA Gjjc qpoints viridis fontsize vmax vmin */}\n",
     "\n",
     "# Simulate neutron scattering with an AQC-dynamics Qiskit Function\n",
     "*Usage estimate: 18 minutes on a Heron r3 processor (NOTE: This is an estimate only. Your runtime might vary.)*"
@@ -171,7 +171,13 @@
     "\n",
     "\n",
     "def plot_spectrum(\n",
-    "    dsf, dt, q_steps, w_steps, lower_bound=False, upper_bound=False, title=None\n",
+    "    dsf,\n",
+    "    dt,\n",
+    "    q_steps,\n",
+    "    w_steps,\n",
+    "    lower_bound=False,\n",
+    "    upper_bound=False,\n",
+    "    title=None,\n",
     "):\n",
     "    \"\"\"Heat-map of the dynamical structure factor.\"\"\"\n",
     "    omega_max = np.pi / dt\n",
@@ -220,7 +226,13 @@
     "    site_axis = np.arange(n)\n",
     "    x, y = np.meshgrid(t_axis, site_axis)\n",
     "    c = ax.pcolormesh(\n",
-    "        x, y, np.real(Gjjc).T, cmap=\"RdBu\", vmax=0.5, vmin=-0.5, shading=\"auto\"\n",
+    "        x,\n",
+    "        y,\n",
+    "        np.real(Gjjc).T,\n",
+    "        cmap=\"RdBu\",\n",
+    "        vmax=0.5,\n",
+    "        vmin=-0.5,\n",
+    "        shading=\"auto\",\n",
     "    )\n",
     "    fig.colorbar(c, ax=ax, label=r\"Re $G^R(j, j_c, t)$\")\n",
     "    ax.set_xlabel(r\"Time  ($t / J^{-1}$)\", fontsize=16)\n",
@@ -406,8 +418,12 @@
     "gs_circuit = prepare_ground_state(\n",
     "    n, gs_layers=3, max_bond=mps_max_bond, cutoff=mps_cutoff\n",
     ")\n",
-    "gs_circuit.rz(np.pi / 2, center)  # exp(-i (pi/2)/2 Z_center): the neutron perturbation\n",
-    "print(f\"Prepared {n}-qubit ground state with the neutron kick at site {center}.\")"
+    "gs_circuit.rz(\n",
+    "    np.pi / 2, center\n",
+    ")  # exp(-i (pi/2)/2 Z_center): the neutron perturbation\n",
+    "print(\n",
+    "    f\"Prepared {n}-qubit ground state with the neutron kick at site {center}.\"\n",
+    ")"
    ]
   },
   {
@@ -432,8 +448,14 @@
     "job = fn.run(\n",
     "    t_steps=time_steps,\n",
     "    aqc_segments=[\n",
-    "        {\"n_steps\": 3, \"ansatz_steps\": 1},  # early steps -> shallow 1-layer ansatz\n",
-    "        {\"n_steps\": 2, \"ansatz_steps\": 2},  # later steps -> deeper 2-layer ansatz\n",
+    "        {\n",
+    "            \"n_steps\": 3,\n",
+    "            \"ansatz_steps\": 1,\n",
+    "        },  # early steps -> shallow 1-layer ansatz\n",
+    "        {\n",
+    "            \"n_steps\": 2,\n",
+    "            \"ansatz_steps\": 2,\n",
+    "        },  # later steps -> deeper 2-layer ansatz\n",
     "    ],\n",
     "    aqc_options={\n",
     "        \"max_bond\": mps_max_bond,  # MPS bond dimension for AQC compression\n",
@@ -518,9 +540,8 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
       "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
+       "<Image src=\"/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/small-s4-code-0.avif\" alt=\"Output of the previous code cell\" />"
       ]
      },
      "metadata": {},
@@ -528,9 +549,8 @@
     },
     {
      "data": {
-      "image/png": "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",
       "text/plain": [
-       "<Figure size 800x500 with 2 Axes>"
+       "<Image src=\"/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/small-s4-code-1.avif\" alt=\"Output of the previous code cell\" />"
       ]
      },
      "metadata": {},
@@ -628,7 +648,9 @@
     "    n, gs_layers=5, max_bond=mps_max_bond, cutoff=mps_cutoff\n",
     ")\n",
     "gs_circuit.rz(np.pi / 2, center)  # neutron kick at the center site\n",
-    "print(f\"Prepared {n}-qubit ground state with the neutron kick at site {center}.\")"
+    "print(\n",
+    "    f\"Prepared {n}-qubit ground state with the neutron kick at site {center}.\"\n",
+    ")"
    ]
   },
   {
@@ -660,8 +682,14 @@
     "job = fn.run(\n",
     "    t_steps=time_steps,\n",
     "    aqc_segments=[\n",
-    "        {\"n_steps\": 6, \"ansatz_steps\": 1},  # early steps -> shallow 1-layer ansatz\n",
-    "        {\"n_steps\": 4, \"ansatz_steps\": 2},  # later steps -> deeper 2-layer ansatz\n",
+    "        {\n",
+    "            \"n_steps\": 6,\n",
+    "            \"ansatz_steps\": 1,\n",
+    "        },  # early steps -> shallow 1-layer ansatz\n",
+    "        {\n",
+    "            \"n_steps\": 4,\n",
+    "            \"ansatz_steps\": 2,\n",
+    "        },  # later steps -> deeper 2-layer ansatz\n",
     "    ],\n",
     "    aqc_options={\n",
     "        \"max_bond\": mps_max_bond,  # 128 for the longer chain\n",
@@ -799,9 +827,8 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
       "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
+       "<Image src=\"/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/large-result-0.avif\" alt=\"Output of the previous code cell\" />"
       ]
      },
      "metadata": {},
@@ -809,9 +836,8 @@
     },
     {
      "data": {
-      "image/png": "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",
       "text/plain": [
-       "<Figure size 800x500 with 2 Axes>"
+       "<Image src=\"/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/large-result-1.avif\" alt=\"Output of the previous code cell\" />"
       ]
      },
      "metadata": {},
diff --git a/public/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/large-result-0.avif b/public/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/large-result-0.avif
new file mode 100644
index 0000000000000000000000000000000000000000..ab48d3cd3b1524df907f35432ed8d53e73e8dae3
GIT binary patch
literal 14938
zcmYMbQ;;aY&NVu=ZQHi@*tTuk+GE?cZQHhO+w<>pzI*ECp|i49lCI8ES64Lv0054u
zvxmKbtA#1RKeMqgW&97eu`u`-1KQh|I2-(j|7V2e#@3Gi2LS-=EsUK1hyUkUEUfK}
z{u2lb00031{s#dj{htQ_;0XA~{WC7q|1kg}|3VH63)}y0{9hIIUtsdzw13qGt_+O-
zsrdiNf6D(zIC~3coByc)?N)1H?`Z!IM_AY!+5Ph{2Cj^M0zhE?qoY_j*_-?a0000(
z0ssKX{sRd17VZ}RWgs9R{`vneALl>Rzs~=s0scqfFmQ4I^B>y9(U!}`-o)&`ix8KQ
zg^@j%lZTVDiH#H2zZ?r|3j;?FE(2?O^Z(8Z*TUZBe}nrs3*Z1CU;rSHU?9+tf4#6Q
z98Lc3(EqmkS8V;?as4|o$Bd1!fin^S6ci?zeK>0Y5Ed*1)1AFEkGs36;_P&(G};8I
z+>>L2Ly55gryUjnV8Yeb$+oI}GpNzJhi5I-m`o5P#z8cX8J_D;QLWobH9ILx1%=&r
zkL{|I1%74sw};)g7a0}g9bxYipF^QVSH&wtN&I#(K({pTk1M4T0rgO)KtResw|M`I
zWj4<)a?FxW<^@3RxM%1>!*}+ba5;>#cb+{PvL2+`eJj595DXJDo`GgWnI`fu36KY;
zQ+cY?KxqYf20KRKDe&(t6HWgHcGfA?pX1uiufTf9Z1gP9AG=Y2eYZg;p8lFs{(DY6
zj->VRsLIuS&|@?CKgJhxArHkT!;{PPKl(%Ms*x}$Q4chLN#!FIeSO<cx}}b2Q*@M$
z=bH5G_>ieV<UKCmJF^X^5@$&KxU5Modr~=qSf`;Xzu|AvrFwg@y+_uEu}bSdhaYt-
z-kG(YcWN*G{o(1HPJEZCFtO>4U@0yeUm(>*)lBA-RLK_cAplChR0U9g?{AIB@|}wn
z-)g;IyCS1lXnva#E)E(ZMRas@_j=;IDsVJ$@4w*(-}qR!SiBlbXL0oDCDTK--LVLM
zFi3z&$e;}i+>E<67)b<iVS%pz2OF5ebBCV5URpequIwMYS9S`rcX1b&r4Em$o>CoP
zBRdG4jn-lVpC^5OT0^b6t}!xh)MRnIT~Ry3EloKE)|MS;XC%%AdHd?nRU7r^=vqbS
zdh0h*Um1K=H_2{z!N^sBisrZhR<DuF`CwSw)EA{E=a$Y1>q05uAfc9@CRSC-Ac9*=
zv~^zT;}Pg%sq@kH>IOy5JhJu((Bzl%`fKQJI=dOM`~`Mgw1k4AvXx0<BXQdMKUUb)
z^;wOWD)!97ZcqL4kh(Am_RBWAKdtmC98ZVqZI%S;TG;p?9Tm2uX`Fatnp_}P^;Cgp
zY`Wy+RG3vY+bdQfb4UWX*t)VMG(A^bZk;{oVO2Yvo9MzD(TzI#ED^_nbDESA*D14y
zl|{a@*VE{6xn^F{oIiJVf)f6I-d%lwJJBIT(QCgeh_boywKtg8A4mDF@Di$u<&(XY
z3JBXNDpNBNe8){q&s+%Iu-jO04^ktX_f00f?<f6?OSJrsd6B7L-9>NW$A8rrJiq%J
zWtHo+1R?goK-!?Gt-MhBQP?;rhmj@{)gA};6y}PvW)eJyIP!VOV#uGoJW*D{bV^QN
z31EHsb*ZVN;?tVal<c9Xyuhu{qJqByW`KRp%AKpi1dgOy<l;QAZMEj_4biy&45q;=
z?Ox!~;I?K|xJYRF_bw@LYjcap!eu7yL}2^8I*mU16>r6JB$TS6K-cs|>hhMNKeF%#
zs&T_{fG(|gIM!gDgLcSopi)MV;yskQ;0#h$+?qZYC^F<)TkOFC3rgtbe7McpJ!m1g
z=K9bhIBB9}?tG;n5YHnDOW<`sP4Ns`k~NywW}?!zJea@B8vkkZ(2i)83X|;;$KN5;
z5nowO_M?By-7nEt?h!10n&*l=vn={4ZzM~duh>7Dqi47mf**_LD>H(#-rj?FI^%kE
zhnVwa*3v)c&}sQh0w1y+(esU`Ar6A{U{p(#y=U++nfV1lq)JBBly3}8=3Hg_$^k+H
z1{7lAX){^sLkuH0RkrDE)SDD)Cz5`JlL3CrVV4TBes1#RdaCZ8A`~*SNd`GVQwsvm
zDLN_!3GIp{@2+X$Q|f0dx6kxl_XEe*X_rw=yBwA_VJad%Fv^HD6g@v0D`QwG<UFZz
z-`|Tq_VDUrMvytylSzteB;8yR#Vv%)>I0xKBU1mqrw9txL%AonhE=W!=sScr#3WCM
zwJGX;cJ*HlW~Dq5(Bl~h89zLzzzGv)<`nDWuDr=gs;{+ess^3BTBphNy6-}@iyQNk
zWMSlnEY{ac_f*^RX@wEmOJDf8d(jG|gmpxxQ!p;;&s5^L6D(EG^lNcRR`}gWxpc-|
zd5OPUWRvsbRW!cfjgiBNhshw2?XbcjF|OJ>ofO~T9uTIgA;UTFv$qmlymouZ?_GS9
zv<knV9Ihj-$T-zVWv_2%t%>YQEExO)lgi`bjg-{Xf_}Ks7q;J&baFl=`)U?0YNho3
zi`<j=?iKXyisEa;g8g+yhR^`O-<}9BN5)+s@3lCsb8G)g(Ns)B5?i$ZKf1qY%!x4E
zhpyAp`2J%#Tn1WX;6MlG@wqPEslw~)1AU=%TVJtE^s&;vwrrh)K8dvM$K?Y#F10o6
zZDs4a$c$y?9C75CA~k{sP^JT(Z_}}@ZbTr7Ys>z2EIJlS$FmVcBETr!d>Gj$SH=Ey
zoZz&jG9laI`jkizIeuQVWd?LSeQA2%Aqklddh<QEl57y^2qSdx${~X_H*$_8g3g{y
z5uJa0HtW^2CmLG9UlxY7i=FJnj#JNbE697Qe$%W&a~$076PJ`e2Ys+faRYe?c9>zS
zbi=u;e`_g!fJVo5JZLX?#qSsi2ZKMsFw#hg5SH4DDq3`=N(NISAWfQk$JFy#jE?Fc
zxBW=7Fi*jELO9aOt-t9KaVt{Yk4Z&qq|4mDuwM-r-J!S4<&Js#tvR3yy68D02lD{x
z!FKd6Kh@ruRhBMcx>UNBp|VFv_@hCSv}@}upn@#aEy50l=p)B!37-Qx)bU-CfsWNV
z4T%Bwa)pj|LHm#xCtU;uEV`(#4~#mMiShtf;upT0oC$wN4U6sC<dPVYp_GhIeQoJQ
z%*Q-1*e)C6Y2kjJ-@z0dWZ773MujQD05-GX?Tczdn|XhPNDX<=YRuUCsa5IWxJI}h
zz{?RQl{oSt@f5-fA!JkK0)I3q&kE~Y9t_*^TWH&?a9^3|+Xp;TyuNabjlk|Fe=EuQ
zxFl<;FaoZ{ROY4aRGQ9tS2AxKfGWgETD5EYrG);+Lpd+hA%wnw#Ze9<v4~9cqu6a|
z9+x!fkE}Gahxcorki>?fNgbw|$1a@K4#yyX>}6eAbp8+7+omgoyA{R6-0<#;$Wrbv
zQI&35o1zXkUpsRdeiODdV!A%qK@W3E`>x}AE&SDthd%INe@BW2?4VoF@(Z`1oGr-q
z;MqDGCNy_Ptj=5i&d|XQ3m8fO?}+)wZn>H@^GvW9;s`X}FIh%zX)mG&U{I$I3fggo
zNr{lR5H?vq-QfxPkKaK3w0jD*K0)#lypAw-D8}H$@SK^!O2_&(NSPYgV=CCB+0=bq
zl!vV3TX}Ytv>)O|QI_X%)tEv<cZlqJSxFE=sv;xYeY{6~_XU^4##(Ye)a<>H;qV}b
zTw*)S^8!ANI_lP5@)Obg-bI6syA+kxK%y8MNJa(w8e))0J;wL+q}r^foc15?0Eyma
zj249UkS_ZD%oqR!QTn8H(G@%Y)DfIA2980B*|}X4byfO{Je)2JXPJm#8M_NIZ-Y%#
zch`-sUxm7@fbIccIwUlFk&-irVMt9IU#~~P^fD{Hia=)Ch2242aiJF8S?*fv3|AA_
zhL3S}fyNJUL^btM+GZ50GS34TO<nvbNvs##&pB#fZfR?Lj1=<*xypSYCqfDl!E`_F
zO)oO>Y;iR|7AhON@{Fm>_dkR7G=CU;%+~VEr(NShr~-fxr!T88_3H&?guKyy_%CLt
zfg5csl0IZCnqA48pk~^sI9lgC!x!Et5jjP&TFLx1)|rGP`6qk)jw5vmT)JsLS%{|u
zsR2RgF<MYd1R=ViPiElSydw3Z_=ma4#HlNyG!K}xp*7s>-ciC*$cc#gv%q4dv_LSH
zoLlVmOoUP<C}%nVuPV3;`ldx<q^ehrao;Hl5QeuixS2vhRWNnkiH>_j+YNx9Is&qr
z*aQF%)U7aGTIln-+cK>To#Grxw-b=b3r&Ka!)_AoAmZsL{e{SSGV>A9#84Po8gS@>
zQsR+e!Q`1kiFIGE>)wg#Zz6xBE&7TY)$mm^I3yDDZEKGX>2(A>aSS5V=O~02jg~z;
z?*}0cFB8czx60di7WuP=(eie$`f33nPI~y84|Vx@!$qW4*z|tg49!YXIYx6;V$6wZ
z_I??_N5I#i!+Pd=*M|;$e_rW_rDw=gm#U+^(SdaIRwc}P#KabONH~sFr$gf|r?j(%
zd##*cArf$RVTTF|VLWqoNbFY1j1g@qI?87LqK^ELFjtVvrR8)5;vJCKLwNmV@S0o9
z-7wfl(%=li2cjZ|Gv6E4P?b{Xp>7_^W1e%B0kH=MtqMoDMhD#G)BbQqqyl)Ov8=ti
zq*RizMKz;g|CC^?NTZ;<8O%ib4G8tG0JD2t%Z^mtJeq9d*<OALGvx7jGelJ9NGK3?
znFNd(OR>Z+Uq19)`y;PMmVx_u$-=#OkhtvnS;DpZ0vH4c@CNE@@a-gMa&yiPMVJO2
zrb|QC591gzXJD5Bxa{wCov$vgSs4MHCr>f=LoVB^^2McwUNbs==DmS)v<vtvqB<gh
zIH)SWq8MNzhA*&N4c`s*+o9;?os}oJXe67M$K_MR(dU5S$;wbRypZk2fmE$2z~Cp~
z41&{813+<g{ywpnr&qUc$AZ3p7)K<U{W&(+kzk-mpCXUZNPi&2zh}J}S-MD#Li^*^
zKKmS1Tx-M8j*NM1#}lepn8q6L==B!4?7;{D(K`W-NJQhgBo$6i+q)TJgwB%*!$gL(
z!#DV@&G)r*X?=3RB%Ev(6i~;=G<b{Oy$fQ^6N{rV<!Xo!6HI+%MrC5ZtHugC=5N-C
z8r+gVR7)V>ge>lb*!cl6Hy9y_J^1v1fTGBS<OscA9+PtKEdThs4r(}61@W`R_820S
zImz3v#AyfjCE#kzAWSyVs25>01x*m0CJS{_KB1Z)cI*X4(B9ZDSpoDwM|hAYUT0%n
zaE(uS1M7$)0N6rQGO3HCo0dmD8`GXR3bWSE7cpI-*yv$9`W<^NbM#gnN%QRcm85f_
z^iG6%jr90jqLIrT=h#X<^KeT!heQ0{v}-VS`u%&iTehlS4OE^8NL%bGvk1*w+FWx7
zlZtD+I(`i2emLzTp|{whcd~jU+H=JXt|qa2ie}#gL<R&L11e1K#}XARtP(S<)_jD{
z-ZlE345th$Kz(9vnvTdN$z)Di@ag#&K*YiZEvf>7DoPQN#cMSmyaEgdXqN~dNW2c$
z%5EQ&%qY)^hnx9HD`BVrk+Yv6R8}De4S<0_rs@ZZV5?7!g}g-w56WerIv5udqD+@w
z(TsJ~hco=QYwK#`=yADNBj?z{K!rWYE^*UCgBzt1qKh=@uMhQLan3N<Pl2ahNTho2
zumM?&m&4Z&9;9NxA7g~BgdUZylgJ*1@7*2BF+b(p=~3MMHKUK-toe~mlm5msG%8d!
zO*Vpz?7lymNPT06P59G~>mV4Y>xIlhCp-3MJ=TA(QNV{xuCS5<q|ZN7V=4Y3(JzS>
zj`q*4I=1iq%1fRYo>W}ORl{ctZLu`u)U;)v^vec`O*J9VAK#$Zguwd}eKM(1oA4VB
zKF}spONki#isQJDz3kY>C(E0pl}7=O-bJ3wrjiift^-016*5I3kaSlE`K)gda;tzJ
zBWBL%!?C4x*v|1i?ts<~zVI48&rz11w2Fnt5=k{E;@})H&zOJ5U-_6_mOp8QzRdPK
zH>y0j`v+&Qil1DV3+!$D-jOSZT%sNBx6O}XoYEnti^@UTVMoMuMz=XCZz{g}&PGuM
z>mo8?^>Yl25}0ohBw^_&QzTQznm)GM0z?1@JCo1;;#n-?hoTC5hEJ2NnvbZwd-YZV
zpQ-H=QFL{J=J*E7sMqY>8U$P_OEE=x5oE+3*eTE|@eRD$*?Hyt3Cq1aY+bSk+Tmu&
zkcy){$*@k)Oh%3Law==R!e%69%uUKp{QH@4kEo>Y-$%D4=dOcL5*z?RbrI!CsK{=t
z1Ph<U?M#8ru6~NgLl*$D<$xrWSk^gUF&F!Wkr_+El#m8Bv~}$>+oBFdy;Bd#`*`+|
z)IM2L-2+&dIJwJZn7SZj=S@88+}B!qPHW*%iAi%O99hS0P?2ygfC<7)ueP-AoQ((P
zx425asay&vH$rpUz`eu%Iuebd-*zNE-1bTJbfy~L2J_v$wpz~mlS)YFl!>RM)ZEAU
zfNigb2FB%MrfvOWwv$ERdu)RSL5-hNh2=Ks_OPI@s7_LDuo)Yiov%c9kQu+~79cXR
zHRF?dZuB#$T<6?Mu~8xb#V&TS`}7gI3b7>e-N){%Ht5*yw8gC}Je#7rSQ;wjooTky
zUFa0AvahxY3TRZB%X{@yzi0=+;W{z4M~NyzPo4&`#j?03S#$f%j)}Ak<!1Pey}@2R
z+wsAkMQs$#?i>__vH>{n2Zj6<T!P@eno-*VQbFnHj@k-hG}`o+l(t>ToQBK!&lP)q
zn*s5;dH<g~FOR%8^}p0{@ZjHYkxG5q3vVwcrxk;ZTvIGRw{IV{&olK<81iXOX`2O?
zkwl$XtZ%;;blS2eWxYtvOLKujSOAu6FcD4ruK1t_t1SaWVj7!6G#xVnrDEJ-q#Hq0
zM6@>SD5%Q|r3@-P8Sr+Z;*iUFQj2i>$W7?t&4oj|dF}x7em??N%}^JYecIv2S|(#+
zf5m~!6N$z|c<Pp7c3o!e>n;$G+0_73Rs`CJb(w>p6#4(61vb!PJuegGoTh}<L0X3E
zPv<>e+Zi5-XYoMLC_+uTZn2nje$3}nTvuz!f!<QDmKKszx48UDl!09CJ3cS4NxZLP
zdZMD?UP^Q9vQnE;(wOZ7u;dQ^yB^dFD|FwjNb3MYIY@X1kp}y`s<}(@z%y3obRftG
z?DkBt>HJwC*eye)f3Fuy4!@%%MCZ2wD`CnM>_&MU>=Xnuf;84x1Vth^6lH;g#i5Iy
zWKh7TD<6)1eRi7mj{hyY2Qq%*a44HNI#bIhj3Y&*IlwDHFb2aaOGaNnXYOxD8>PN|
z{~HLVNg@-7%$A`52%_p=R0b{Qdq5a2&MJA<v;Q7vZCw9Qd@X_KfMj&Ad6Ngap9d%h
zK8KOY!B@LI1$bl{Lj35KV_^D&$9FyaeuaQdbvoa`GP)_SMaMAL9418{9+y1S4L=V~
zedzh_(rSp!D}+Hjqg9E2Q?`)*HGYAP1g^k5H21#j-A^|Uf^HG68@o?}1)M*b0xSdI
zJrVDZ8J+*(=Fxt*uUFmS%vv3^-#kigaqto%Kmt;qQz$(OvvfG(R=uLx>aF{BXva;_
zbo6X`CE8Wx7`ydeoIXx4MpqlBB`+0=(yiVCojAb3oj=gNM8wZ8bszZZT;_=vUMnH)
zy!e*;<k785iiO4BbE+Tj#RXgG_L#SiGtS+37U6)F3SX)5kWRcOEnjd^T;_p;#C+#B
zW@j+94iXf&3kRE4S4$ZaC<#I11DAY_>K@n@D)XijDkF;-k-#P2I-&z)$4*PDqd%U=
zaV>TILHl`JFW*c}MO9IJ4i3(<HA5WK;_FKXNjgmXY&x#=`-m^fS%jyo_?-;>_`_5A
z4l_rDfW%{H7sV4C56dctttSd(ND%bKjvy*e3qF<jC{5BdijG{jLXcARq&T+a{TBJ|
zKwpR6ojA5EO<#{hYZ&y}<_rW5U-=FstM9!|ZQ*XqqT*P`5dM{8fgmVi77{utUzaqL
z>o=35-YV>#*Q0Cy&Y?iN5j}y!I_mN7>5s4DmE~1pK4Q?vhN?qVP5bo|lnd)_w9&;%
zcL#s^NP4-CptH~R%@~Q_(7oiMrWW911e52Zp;_vOyiMrAvA*g~8_hph1?9DDq}(CC
zUM-deLA^rlfMNn#{0%89t4>;FC^)q5CI?9K@biN)2T1-bZn7|hRZ8?)HS7huMfc!c
zjN>mx!03*GS-%<8j@l(Xl5$Jl8K0is2e_WBE`|qQybvqViT4!KH>8&h$#$Yyd%%v%
zVizJYyzng$bVpq(YY+!*^ItcHFzfD#3BMr{*eV}!KSGFsES&k<q}%<5>?g}@`RUr9
z%5v;fk~>RTW8X7ghuVRDJ=<uePP7;8O)ihKNePdk`$@sqmrs!LrDQ)W4>rB|C8y%M
zoSzymP<DefmH|Bwt%k|jqULcBR1w5{u%dyy+R#uYU-sR3xQrv{!I`9#vU&^bhSo9~
zIL2j>oaC7HQEI==Pk^*q(v&6%=qM-Yxu#z7?m?SnmFks~m#rY^az-(4<D)u|eSHp#
zu}gj?S<9N>H?{}O*vvM`CR=F?8s=Rs?ighvjo95yPn8_i2QH%uP%O-Lul~i_rQpgZ
zMLpqu*10Vu7*6}k=l8;x@B$o8&g+L2IP&Qj3u};R0Dk*mqxo!?YCRw56@!lStcc(y
zYNskOPi(Y0HePoX?RF}UROr24HyNtzF><<ee^qM`V%HSI--V?^W-UjOavZZBxSoYw
zHT}hMGw|h(G4g`hJj0>MWN6qzIy~F_BRMvBnEqc|T^{z=w&NAmE)JRLc7Hsy<|R9!
z*ROkmrMvQ?y?t;!*N}Q;lon50W|j1}pnwwW2vhuyhn);#X@G`)gbyU99`lrei`|b^
z&o^#wlXMAe3frOb-gXYQr-#Fu0_F%Tua=Nr42a->Qu|BSF_<%}41^A3W#e`{AgcNW
z@0%k)*4LjCK0dJbCQAWO>gQg4-Q&b7NRM0j5|VT9EZ{Z}1&M-i&|hqf2KAQhL3Rcw
zA##l1s6=Q=cdh5$F74xfu%iZZ!A~l<e!AxH6(MSmdVwad!aNA!TG#P$lRNn;1sr6j
zu{*WJRrfxH?vT%E^rrLOvh=D!1R2`u!${dVAx;kBiCsa8cQTZ|G}CSNRNicFdVdcM
z7+OdbtVE6}3MgcGOIM{3G)97&$QZDdqQQ6bjWf_it4YeZh0tM`stUFz@|6fRf3al(
zXYFhL<b)@ya1<&dbgZQO=4^1UtCGB^t^AcxM{Gjh9Ai2MTMgk{>{Ga-%d;^y5YADH
zP8dlb`1yk8xp~*7!7A=QYM3$n;DWjL@YM3Lk<jQq9!koF&INbd{V}wVr(LLvMaB^<
z_X_jUZGd6An?!D5HAmno*hR8?Z48eu#U#>8LiPrqp0iAed1yEb*k$g2lYz&*+%eMX
zcuvSG!uEjyK0mY=tN14=*&AWK7~kv0Y9&sJ3nFMr`ylTaVwBO$m#Nm(kp0%=RKV(I
zp~Tiy_tfm#*(Fvr@cmOQY4GBDpl?q|u)j0lS<|<(B9O}YjM@=vk<y086(2AT)X;o$
zH!|r+1JqEYie>8b7Vc=7J_~NUSh&RIvOlZ?4b}$74O|dqTTb(&gBxruN=5X)QtC8h
zAJkK&-!5gGj1e!U1wIy;$OB>K76+(qJP<5_3F!fbt(uwaBVzp}S`ujktiFG$gKh?%
zD!R6D;Oa6>3A!sogPWObjJq1<%!Y7D=-Zc=%$$1AvIm;qS-s8rV!(0{YY^oZ{h9K5
ziGSPHqTi136EZ;JJ)IbbRGOB533Uqoj%No-#8RcA*^sh-#A`|eRX%{x8Rd=I26FcI
zHMbSHjWx`_%}%P|1Epqo&elvuO!;G)yxltlCqcMH9s4a0cq6kuf!vIX6>uK?1Te)$
zorly6R-$opW5*B^KMv(OCAZrYhy`lc>Dex6RZYEf@E@`R6i+H;-AW-9a#1r60{x_o
z&!Mp**J>N>Zt}!@#i9lco75Ym{mHL;z?`Ky+S@pHuE8g~M6C98DCXn}{2*M4CE<JV
zzMZMbsUlH%{8ODpk0>K|z2?<H&Fwmd@GQ<bTynSj7y+6#Qa&&|!Lu+C-rJQIc@4RS
zXaEt{cKFc*KMVg>%(yu8mPtuAq<Hk2(NG??b$E#br~A$bygZ{!#=r7v%6lixXpw){
zoASWXJwe@Oo|RuVugoiQB}|ls2lF~P;Q4|%KYI9Z4x%+;Ea1f=ccwazA-$JcO+vdG
zQY~q;wi(yNrJY)do}_-LmGi-etinKTnCm1mb30$Qxq*C!j8>o3P+V6x^D_HuLs}yK
zQtS)r**1JlcV*FD?-XAAg_htYy@H{I(BHVD12F{@Jp;w1qQDuw?%W)HN7Jwt8<(Hl
znAM2Fqb2FK^B$F1OB62_0HjMk7slUxOl?)>V{a1n4$ph(d6L*Hpv<+PpShNk@W4DL
zk~Cu)6&Tioj_^$eIP-Aw#F8LStdqpwT`P!f-ZQKShQj*!7A1hdO1v~5qtK8^<-sam
zLrWdoI&Xm1yw3qkaDjg>jDkg7Z-HA)zCu2kx7t3G+?Nudj9k=oU<_}~RJn~KUqvNl
zL~fL<MNeUJ&Gy0pruRp1$-)QCXCbkSs}~g97Zr4ABzut**26=9G;-~kyF-FWAhfdd
zfnyn3+in}OgY6Ng(;trY+t?%7F_P<LF~lTt0VGgK%fUuG>B9l`uol2hMkSc0k0_#f
zFL<39xdjnv%ru`clEr!jlYzF|6eh|p&yvj&HJdoLksMKR<F{N%p)9xwqsP6xy!6D%
z!cxcoL6VNW*m9c?3Yx3mZ(2q#lihTzKs)-UH<0obqV+tj(>Pbjb5$VRA}Ctc?*rwK
zW}}$v1$X4}c$6b=fan47QyJRvP4rDY?=F25<Dy+{Z-?6MiWu7UBi_r&#6coE7vBm>
z8YWSl7o>npkhln$+Iyg!+>CbT91d)1L#NK2H`cO?|C}uw!EtwE#M2r-hM0LHn!R}`
z1biR-s{aU75EHzGKdq5gvr7BxW$%v@(a8aY#%S&lFyVE|bpe@|OW)|4igL4FNtPpG
z@5vR3uoTcsurYd{m9^<9U8gGZtSqUWMM=7abGwk4T7Wd_BsqKIBY6n6JGX+|`J>7}
zN?zJGpzg#eS+1Nc!Rz|FCK{3Pe!9=nXa%GIh7$QK6BFW$Bta^vj_`8)cQ`?LfzOmS
zdaYCK1BHXqNXz&)G!gQtd8LjlmfZeajrUL>aPCu+fMn<^K~dZ{oLlYqEJOe;Zd=zl
z|IqTw@y5mHd-JmVLC&#uaf=p1L|QTqo3Wj}`Sf)?vR2U;*Cm=4&{mW!Bw+2>uUZ95
z*+;TK)k88)*r8RR&TvrAkQH&?npSHu`cZ2B?B4yD1gDofF99Rc!rr1g80VW;XzXn{
zj+=%tJa6XCJMQ=OQ_99E_!PW;mT2x9w)!T&)vNT%Xj-zO$nvc){^Yxbx8>fzLCm6w
z;!|W;(wVpA?N1%bq)``w!H07S%td9F{^o`$jg(8#hb;*jf~`1W@KrMtr5!ce-p|0%
zvg=kI9AyVImJiD^A!}MnUEYF|tR$It=WfyHofstCU+yX(bs8_LC1Li@1HlsubPI#>
zpC#|(?VXt3L@zp=XGqugL3F*zGD!WY3iNL?@t$TQ(mWTfUPS%n8WJ6hM}H00ozgt|
zs7|97x-xp>ol)%|MCf0R_n{VO_qrGISJ7<D+XGNjpYGs;+7e&PbLPH27No3YO(@(S
z)5u7B(xgL6%bHdId<5{Rbo}Qw3%hj8Jr@YIwJ~{6mdWVqeBj|U9hGQY>A}Cgk=kx-
z0o?lc)skHGkyMcYC}kZY@pC#AVA;!zLm}fG6HG~)c6ig8J$)$!bfPPM=Iq`-P0~ig
z6TkgipruHw$myf18do8*kAGq6%lm>Q#NBc~4jn*9_?N>Mw8j*LIB2~Y(d7}hV0X2c
zF-1o2HFQD>BNg^qch9BEq3S(qm!B`rN0=hh<e0D}vnJT{ZOsW)9_4PS*Z{i!Y{k;5
zu)X)u9VckaRNxfDj-^=r8D*LcR62YmxX6^F9EiAF0ERf?_-o?o24mgBw?t$>BSD(z
z`Thov-p#&;PhlHr!CJ>&t_TFQm+b3wr6MD*e!4o4t*$`hKB#<3n$*+fCT%399cV)o
zNATBegBw6E&jn4@+lPJEZmM$t%;Z#BDYhg}+|s*#?M;4>d6b2@x6272mC2<PWSYw^
zb17RZss0BaCE$=*zO?qL@?s23hAP}Ps=SSRE1zNOl+amdv&4fkN<Bc7O1Dx$L*}D^
zdl^xes3uRr0xc_#Hmi6U_c2kKCaK;mh5o#4Zpx!twKNo&36rAq)&JHS7j~29=vI}6
zwJp}5Ct+Cdg7oWc=85k)@WJ83#Yt1mJwU4A)!W#9V05S<&;73s_?&qwmcj_8rcg-4
zcE6C@RTGP(aJ(+DiZ6>hct4cdC_m!E8YfU-egyac-LQqsJ(R48*%>nE1q&Yk6EYnr
z3RxrBj0AkKLf*k{`V+jWGLR=;Nt)fjLv+|jl(;5*H5sJ&rYcR6_F2H{vS!1we&|F*
zaj}*bvRAy==jnzFKzEFEnvet!D~_Ttg|<vmkUH%0Ko;ciA1}p7{J!0P?*ZiSc49KP
zB43XjuuHAGitA+2bP>><sYaw|yIy6S49Y^!Ro{uH^O1Xi3sPfpJ&XnZa4J}RtH%De
zLJ{7esQt)!+xwq$AlFV7deUjhve303&UslxeaV;sJz<COeHgAOY{6No3m&Jwnj{@P
zw^t^FQ$sW-&TL-okfltA?Mot&W~%`RtCfF+WXzB_XIX#g6ELJ#=`b;E(XnZpRV7JS
zV@TK^;mU^jL9e)+a@;8SVYcTRW-vyML2EpPWuIRRdi_;X&7v<6BD(zL-oLSIUiSkT
z{oCrAUC__+6OP5MK^LyNGdl{|cDHzCq5-+x_6CTbhg{;#3HBdxUCA_&F|#iB|6s`4
z{eY-5u?OT1WgR%cE%Cq*S|4GmF_*&b_?fU~jVb-%YnRs93eoKcr{zYdj?lB@E+^Hm
zu`}x0;zp-}=^!v6dD#L>g}kAUf^9QVbjVjQHb*NAClT*zGm3N+k<5uN(|E=8C;nkg
zhJ*w%zSK_;p;h_ei@vE_BzrDIBZuoJJq`<!bOQ#7XPUSY-hMWJpDsqan2Se&c+3Dy
z4>wO*_-voAa|N0|7#FFRxsV2SWErp|+3J2MG;+N{LOhOLbdMo(JWZk$q1+CU-WF!J
zN;dmpk%HaM9@vB@et_7_T$pb}4ewiIg(N**wf<Uqqlt%R(7{Tpi9j;+NSha%Rr!hF
zOtwAT#V$P3Ud&D)OJ_-+fdI*I9`k5|O%fk@J{VsF@4xTqs_8_IxBJC41Kd(V&MO=U
z<M#O}iln8l^ae!P-*z;5#}txEN3^;yt>0Q~4K?%v54px0V5gzQqt>m0XT;a()F3+^
zd1Emq9dHHz6W-T>Cv|z4w?8zeeY0-XlfBFmf#WdnGdJv$c@SnUK|_2xA_HkLyGVZz
zucYdhQ32vb1Pl?i@exykb$F^IjE^sdtCU7u>ucD#1_&<@xmJU#nblMZRb8#Ogrz;g
zn*3V{Otzg*TNx{i7%?=wsW^G5SG0#;W`oguBZ1Vr1dLx~qh96LD~C$jm-VQ{bZ(?R
zVxd4XXaBd2r=TIO>}P{P4or>@rs;(j&L8iU;12h~QNg}?J@}z(k2Xk1ICL4HaZDb^
zB$UhcY;$SVH|<f_!%fa6Y&*{)I$e`~*-1|=%Xw%8@ART%3dl{Wn6cHP9wlD2xz~sl
z#`t=U-<4I=b^uy(a}rKMKgSF|_(P!LU2}A|xu=wiTfy+<ZeXvd4rWHX93ZO18{~~@
z&lcLtTJXs?X59tLc{{nO5?+CU-1k|YlT(#66l@z@HD-k+d)64}2rpsY!h@uQt*WX4
zl~`wtEtBv1SWtT^C4>l4*NGgi$6#AfxRkj~%c7{dE;@#r1Qz&-`vRU8zQS~|IJ>Vv
z-U0+KcPH8<zr5Dy4}%zKa6o%#sKT;?5Py&m({G}5{7%<*(wP~7(QvmM4g;m}_9e@V
zp!H}Yx;Yzj--|T+aCb(UiJHUtqHJ3t1WNPpOf}eti1u)_)jhDX(+A}Z6vnR~_pC1%
zb$916B*3aIXEl)z)^W-XB2v@32yU&P<Q%2+V$Rd?A=5hV77mbP){hLbj9UO=c-V!A
zrFeh_&^o*P`}is0m0?Gk*NL3?_X#%6h8SgxZeq@`ZO*Gu_gFcS+$zV6q%VJ!$>@`6
zfRkv>3pr!(W1((cEIT%CDvS4s67AgH2QyPkNbHs6C@DHg&+*%YVlz#BXH+cXgU7uT
zDSvqTCaIC_8Fy-__?v3ZEb3r2DK%N5o*s1=8*wb?w8!ZsLeFQl5H=7-z*1JM4*#NJ
z;MB)VAH|;wa9!C%BYruke(oL-;P`UYg#IK&_9vc4bDG{*lTeF#{)8$m)lk{+EFAXq
zC%oO(g2cH!U~+Ay?j>;icu4x1O@!XN`>QPDyp=&&VogFCzxnrovSCeZ+Kon4znPiy
zvggUlm`GO8&`GNv=*lH+fG$11uakNuAe*Y6`@DoIt1kzTRJ2<pTXwV=N28ba1kTaU
z<tgaBo~6%Q3#nla^P-`QcTF({;R7ZHy8OLrXx$xLWHP{lm6q;GN+QdSkzGz_2_+1L
zN{TaE!*bX&mvPwD7+Y371v#hcM=&7EfR#p((qu(|_F7F<VqJeVh!!Q|)e|kruG4N4
z9917?;`9|=LN|_b&Wm+V!;-#g5)m{I3Jk5)T`A;%t;>QSe&ZUcM&!WIAMlwpAixmM
z^>>XEk#lV0`558J447#^(CzLI4SQ)m(kr=C*!M#gmrBS0D1(DAnApUGC|pZPVRN_{
zdm-sIaV{PzjV8><7WFY@G?oF(sib%W=Jz~ok!coCCL_Jwm3n&GCPQ93B7a!81b;xk
zDc?gA>9U|vrM}v_<}72LfrWi0>gprl4ir8rEy!pqP|q!OtovV^YoDpQXCBDq1-s0I
z+4t4ZL(}eoSG?{|0@f>~$`EwjOf0$lt<ozFt|FJKaH7|0JVXOERnZ)4pMHDHF6Kbn
zjHF&N@G$3lsJ*I$t{l_xM1&vrA}7ujaOeRvO==V1ANBymPKX;Ymy~N(E?#lLmJMg6
z4ai9kqH(6gegj`#>jOY~sTJ9{TT08<%a3#-!k??w=!@!^tsF$CO<QbT^k&*Byl82i
zT|$%UJ8qzg`l%AA(lM#O^qsDij>oF`P}I1avI2T%74&AcPpAXfAY~7#7SI|menpIH
zhw7eoT;aN%d_BRv+#orHygz<(ZO+=<b5|by1K%-K&Y_ZpNUz*jb;v&eY~&Wu!7rC2
z*{$)xCK19>yYXn4&}JE-5?<p8ulxxy+DSM2H1vK)f_UE{#i0n2ig*ziV4dIJLK2o9
zjq+3!?jQ6;BtBF`iSouc{j++4&2MAc_Adh5!`tcsXFB!o*<ScPOePRye8sABFh;E9
zlLZMN`95c~?uNK?yAM9$NCV=hV|dKvpQNtYHd#g60dbi!*qk^Seqv-G_P$V&>dJBf
z?lazbWn?0ay#td*PhNC@n%FOz&wmC@WV|muB<mEl&}?UW%dsqVGizl;B-p+S8kM>{
zxAN#KwO|?B@Z}5YFRiX8fN<w)mS*2p*&14iqBhix^Ug8;jDPigBxDDxSYD^WiAYHH
zM`<CX+P=`}^bi$5Z)<<L!C3{dxQ28Jq;pL5FFbiB`iRGLfN1b2YikgWHdgx^nn^0x
z4YJ{~tZjc&neBG8WJh!b@V=%jw6@Qhr`EX!JXc<TIRr(6@l*cYzjO<WSv8yC3;qzB
zeN>w-KzBCt1GLKFSBCqh8b<1*ggRheXH$t6Za--xHgB~ULfLiS_}lN112;q2^=sA1
z|C-Xa#eK^-d`{_2NP;1E_>t%yD`ghw+;`mZ(lv#{@+hg1SLo7%h9!MCq$W8(^N|3=
zhY;Z~MTBtcXIbzU*q`;WLmLpDhyF|+7&VblxLIwML`q{1ctNrH&>}dX(wf%=_?U?o
znSIWUl<0ZAwJAXDz;vzP>t5H?&4^FeMPw1F@2DpTsRu#+uj%aXb$&nH>#vw6;cq5S
z^$qKym1z2mC<9r)KnJ$!F?aPD$}lLcx5t;AL&=T;WL+7E(yT)=HRl$RwJcAl9Q*pr
z@?2g4W^FE9FazAFvIF+ofsjp~h{P1$>RV^`1(WutNAq8P-LM7Z(Ow%N8iGHK(SNs*
zI)4GIbcqpQNU%Um4Rv<1%&wn6U2arxB<0YrzppL2a#Z+)IIoX&Y<*lUt;zwtUrO2P
z|ME-3WN!`c`U}Q9)p^reRcVHPn25sAn;3M$wFfu+F>FdqX`(W{O|UtYI<=KqG8~~C
z6btf&5$zGzK6;#@iXcA)Q9w&#_(e;rI@rQc13^>8h8GK#EkfnbqDpb3$0G}aCoYJ!
zyFw6B(#B=--dzwb*>gUUKT-MVCwf6Ltg$h8<essbZsj6OgEgvrww7)rVu@q@Gn*($
z3S2>P1!d4BHZO700bmo;DUrQ#Dyp;A2QKzBuy&We%nlo$s=2jc&8Yiv<(hmsYkB(&
z(0-dLbZqJj^jVkvmJQU*jdLiX!3)S{(ej1i7c7s;q48zaB1{dRavC6brFQ~7Yv$du
z(jR<+CK}R~wCbRguL%KRO)#)JS=^^=ccQSC9JHFz5<iR}U6_9-f-*j%x`FfeFeA8I
zroeG=i&-dJ%P03^x_U`Wc4NJO8!Yl5Q$C8MRi}GS05K{P+dOl31DnAJcT`cS2^-<a
z%8c#rtjE+%Slg3WD31v;TKN}HtLEWTdB{cG<aHdl%oA}Dn-}Lst3tH0C8O+Jk9`(>
zbtK`icVtcOMWTWC;7}LYc%u3x&;31!u~N9mkcSh>?c;XI(4ehF&lcJx?73!x1$pM7
z|EBnwOc0?ttLPDUJ5bMS1D>h67=(`l)x*wvZKsD!qOmWyuot4&)Kbh#Z-OJ$g-MFJ
zg-qJ((TSxU+sbuz`I_P*8(C;VVvH9!ZSz3#l+T2|0|S+Pbm}O<Ts>!IYHO#13-{8V
zJuCzZwb~U#;ry@1MrzRTIsru3Mq7Z9Q(jVOpc_%9XfF_?E<K@Nz-Nz5EZZKeKJ6{t
z*vE0BOu!N{p$3O0M{eo+LsSbh>=($^;NBnsy=F!`UrL9Y$B6$QNc@gC$^L?bJZOhT
z4Dec$yReV}OO-_A;<z`<+4eUk@Ij4g<XFltUX}=BPD6dsN#Et$choPy8Cl$F6>AA-
zAl&$jK91Z^2ePmHaKb}a4^{ne2<P5C+C_(?pBn-o{s}=D1$#XxMd)faeLr1MH<)h>
z&iI%l$Z4=VBzwQCsfi#F8w|3;VxE<$Y_tybA%OQ=i^1kDZsQ?SK$xJ-rowDLg`ped
zYR;gfS4mpEsdzpt@kiSY9k`m5!i|r3?R+f!Wu6gkG+|l?z)n_`7+i(Oq!-E)nw{W0
zr%pV<jl*Gl#0eZ3mwXhG-ITO7t)2*Cy3hhQ0`c;}03;-H_rQIu$+)yt+G=+v*Kv@;
zWW&dq$<Y~sjT{>310mhb)B+J=)|~<n(7+=14-ZZZK;<!;Dt`rUsTvZ%zDY|aod(s=
zBMM3niegU=0N!I|Khm6y=F`4N%fcw+M$fX#!JjQ2?Z@TAypug^*>ZK&w0Zl9cT`q%
zedpVZVQ_sn(}`?iID9gu>~-bo>7>d%eSELpn>wV*r}^01ihzRI9Oq;;8Zi;u6Zctd
zhT18|GxAuM;Kz^%AKVOW#pr24iGxx<uU3ZEpPC?P((3RLgi36BFjua#{bqE(G}B<V
zbJqB%`LxSm*B$McOIFYsuC24)h;#jxo2{i1c`?1vMzwouN38}WMSOe$(%V*I-Wv^E
zSx$ukJqGe*r2{S{Z00%dI?&p5&%53um+2fnMjWy0kFyECiSi~0u6!45z0o8&3#}PM
zPFZ!ZU!~y1g#sY4RPrjyzr^9-m2rQda>Zrn`AL>#rQ-!s{AgZ;-pNm-Y9Jvv38aS5
zj0mU9;m2zPrJ(%1gPh2?vp6#LO7Sqd;$>{@G7oMU*ACtgMlIl$a{h`~8h#?3p*i!E
z7riyFK;?Gu4QZx7U}-3rzMMq($D#y%8~HK2ADoCqx+h|HK(aoxd3j<C#)|EY^*5+K
z5ySr#Lf~!0el*v{MlcHBcaN`MA7Z8{M7aaG)mgJ&q!6a<Zo#3*Q$5hfsJ5>DdMq1a
zn^f^sp~H%#>580-t{_Mo#eo<vg{pMD4iSkioTT1SzIuKLvUCbqu)PhX#%3-6uz^;s
zn@{yA5oua;q?o@m{k(sA&2MlrQN#yDEGZMRpN$$hV<3hq0^i}9aM^$E`lv^zy;-#~
z!sX_Ell4hlDayCtvcubJ><Ns~mf>Hk$gsK7SjDcF1QvFfXA@^Ukg#;lQ1`XQbmZTv
z&%WW^98_Y>tvfv&ZX&0AY2pBV3lb$*$6@fvU7*Q~@TM8`b1>Y^gbHz0E4lEo35_%L
z`B2jZHCCf$JTwM_DSR_ULe@qGh@c&S*}J5LGC0Zi5C*^W8tAv0PzW-}wOQU5Y2mWO
z7Y6qYPPo~p$Mnf`*tABP-`BQrWffCc46+;p!=28oqG&$u7e#@Sc+^!yRVy)mIV!M^
z;^=jTpPKqLe}IXxtGOuv!&saxvP<*Z-NNf{r=n#Tu=HzBGqkHtNSc9u{i)W{J}<DC
zdAFZ=At9{HKeViMD!lG&tvV|(2I2LJOXXQAPNMO9RsU92X(GGS)IjKS5`WRWL8dC$
zES@Lp8o}j2$@OsinC!oeHv`&_;lJejxw5)e9gHTgviF>RIf-j|O0y62MHpmAXM8NO
zZD3`UkZuezWprv~1(%X^oj0sRHqc34k-m$pikgNcWKl4;-BmqU+pwCS14i#Q=Djc*
z@odiQHGO-?=wJCJUJp^Ag_2@|v?w&Q=EhN>aa#*#tGrPQKeOEy+|W%0@R2c%Vl!lu
znXR8%aiBu_VttOQ#zRvXN=moMyS<#qqe6!gNjNY#M=@<I$Vy1Ajpc5IX+dGcxIc>l
z9eJ#;voaCf=LDnsfTE)L2#mW6<tj6Aaz1vq|DvuEio$5DCASjyZDm$f<&<;fUcm${
zT8BNy8~eT5UoUx43IZ`y;(X*69{=&ka5ACIlYQE(Z13D$q~|Yh?`|fD;{JlJnLt6C
zK+=zpzRqUmZQ7U7LxUx-|2&+Y8CNt>SV>;KG*gx~dLpY#6zr2~GLieN26~KS&?$z-
z9q6rQ7mbdezlN-pHBj`L=bYIrU=hm6EYOwnZruju08gEhD%pusGDixQMK_&rX7Wf^
z0F)Zs*f?*C+X2KWroC4xM2l`crI0j!o>lzgW{7@o_fhv^^bLWVYXX^$qXkB##}LJO
zM~evKqL<b)YF3KvgKUwkX=-#%9%=vOu~xX~Eahv~hIwU;dzWq^@b3d6_<ueivi}ue
z;y{=Kf)IG>dWI2)sGpZmodZ~{%;2E5C09Lg%rs@%dv4GLYXHsj>-4KhzY2clgYd~M
KX5P6R^#6Z58+J?p

literal 0
HcmV?d00001

diff --git a/public/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/large-result-1.avif b/public/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/large-result-1.avif
new file mode 100644
index 0000000000000000000000000000000000000000..600133f28ca83b812005fe278fcda9f25cac9c01
GIT binary patch
literal 24495
zcmYJZQ*<c6(li>|wr$(CZQHhO+sTe?+qRwT*mnMX&Ueqc{V-i!)!nnEA7=&t0076#
z#nZvi&C(3upV?ZPG5!bJS{nX~0Uc~jT@3%j|1&}h6C0=hg8%>ymc}ms!~gRvmNxdr
z{|Q6{0Du4h|APQi{?7vd=x+Vv{+Yi0{}=#)e<6pZrQLrw{;!JqFEIUY+P`W;HwMQ4
zRQ&(sKjnWUoP(u{?SIt&cB`{=aB}#Eqbwba?f-cgLpMg@KoFS!=qQ%X4yOMB002Ob
z|I#o10R#t256k~Bzz`4+|J?tVkMkeuU+4eR0RN+K7`l20|A%&Uvg5LKFg5@0BE)5E
zY3#t|?CI=cYU|APFUQix($LA1%h1Nb;=l94wREuk-{Ahu0yq!|7yt+)1PC<jUoR|6
zC)58s^uO)?72EuGT>p-&M{H|i=z;_Q1%*jQPcJP3!h(gsG|>ep!{gy$rZhKGCWAIf
zD*y5bs>Bm6Qb_{MU~EngAG$tZc##uVi%Fxv|AvZ&wC@GLz_2<;cFQqOj`b0>e#g(2
zt&gx{AH;L%=!KR8sNDO=@ED|ebI5F2(a)+V_jCb;Wlb&C-ScWDr|{A`P2lOs_&5{%
z$SRx-OT=!2LsHiwf6~=R31I#4T87oIVSwMc0$g}drl(1U6TD`goB}c>#^Y6EJ~|j{
zXnuOE(Vs_w;r}L<?&BKQVDP$?zeY*0Fdpr1{I0O;IakXGgn~5D?<IA16IAM4`>`vt
z@Q2{s{m|($Ak{lD=V)DIb&@<tANwcA0JGzM*{Kvld?9O};zy*vpZ6O{++2iOg^8O4
zk7C3y=8gC|qG%2yraK-(8#}TU3aMshq+7>IGk?}*MMDXOMrUimcMo{ff8Li#Ow42j
zWYJLH>Q#{^i&7dK@ra4b11*o9iX?n77=#PIk7aVMB=V)?XHVgDZHJljdZ>Iaa0*;n
z3HnZ?a1zdu)H;m#h@LDd%_faYq?X7zgal11e{KK^KmyIPgN8$9(cWVb-p$0z*^Eni
zZJM0S;vxRoMNAhTg@piZ1_vi=OBQ2{l&AkjyT5pUj=*OJsfRt=G~t4iHa&Ojd0AH;
z+7bxFkodaRb{gv*fcYs?P0LL}(*?a`y;Jw{TjmlDB$tC7NfBnN9oXRR*Ws>P3rD*(
zs*e;$c9wzOD$KT$lgP$B=>=7yam(WV6LaRH{g!-6mU>$PC>#jc{ty|B1B+jJpo@{y
z3W-0l2CGFZ`rhdsUl3pTrDe`YEK$%M2!(MXf)AOy@iM~$lQGUbMCF|Zw@*#8D?ZJ$
zAJ2*UMLD*DAv0U{tZt0W=+$&lFxeuJ?uz7dg@(r}{CK^8tU)Y_(b1J14Rv1d^N9R#
zFuSb$uPUAB_eZ*`cqY+=u#N~Y`$$IEqyTkRnBzvnweRSw=SVNZ5EZSs;Jw$tbZiun
z;Zv90i22l*Z3IK3M+p=bKYT3`xzou$Llz2$<4WdS55~1ex9(qeKm_RL;$3QmbHG2x
z9TKjOUt^arLj(`4BK*0WU%5WW0e9rzSw>eT3mKZZPbsyx^U7rsmLKwAMXD$gukRD;
zgggNK>|G^j__?`zWMc!{6i+*JuEg0;Y4Ic*6VzF_3k^0sXN4xiocP9-v{LIa+&=;U
zY|{ipp**`o!AZ4-;_p320hyb`606;`DjeBBq2v}%Pw{P8f+(zbP{o{h5aIs5nj*by
z?z=_gH?ud=%bfc$>LT3K;8bgk)&(s&I^2b47tIF=Pj4l8o^a`r79KadRXQj;xYC$>
z_Y|fr7ZTX*!_jwudAgwh%MDS+Vyy#0Pkp_=DjsM-^P7<Y^Ay5->p>|;XZCbTaIku3
z)n(->zKFO+Jzni$c+xIlh<XuH>(-nn1D@M&A;YA3_xx&$c7MT>Br?FsN>|S^A;~iU
z3;9X+v=n~|C03cS<(?8?a{^oSJ}J5#*AMhkxw*{a#PA)qOFpHWq+Tp{9Mt9KTQ{^N
zaP(rhw=07M(0%Wj|4xW@5bl)BSb$#r0Qi>-1UKSms7Q1fDe!7SPKpgiFu@tr+IW$u
z<}atHA><H#-_7Jzha{&EbWEin41zzH3papAj34Jq6`agNg@gVS@o5w^5<BF#pk4bD
z>YxddbP!fliSFj!%<jhKi~5SVr1f?bOA@SaUr<Jw(z?^b)q7&Mc=1{L286R*I@FxF
zO!Iqq!{L&@s6&67Z?Cjg56FDHd5&*p7s)yGf{6R4!I+_#W^R2SSRl<icWYS(ijN3p
zv-zH<I)<gu{TR|Bha2*#z^ncgJ>ZscY4hRoSU;)A!0~qN(jmOrD*=3_=pou(;@rO@
z7tpQkRRtOe%=f%d34NF8kv7?Pns(PR+s;HyC{S5j(|<$m6j%W>atm!*f1VC?@w8Q1
zXO9E=f*JoHdAEqBFXVAyBB0&7m7*t!8-?cOgmr5+6w6^B+4A&k_>5a)@YuT9m={bD
zCq}E2Sd8U85kX>%9OL6%B>w1Ea1aA8lR-9dpC`#0LDKaSoUK^DbyW!u5}(uEzuuMP
z!tiTFinClKAMw2iEBi4XBp!Gyw;0+AM8q?{)npIo^iXhk44sPKY^DAwoxhSfY+B*9
zoKsCTgXK*EW6&G^w9&6PlRPS_&;}95$NoHB35ag-(?A;;#R3lQ?NMuc^s(k{hDnW8
ztdwE9goFIymA0>c4#%iOQK!kee@%GJyqWe+x)O({#CAbMfO2dvIoDuuchewYWJXRc
z*b53B6-TabGc-0s*z!bV?(_e8h9#21HY&RXsHWWN_gcca21x3g3A`0FIoB$3>FeIa
z8ZGlw!e!d2sucrf*Ypz83Q&{%$S+5u4J3vs{2K6m%X86u2ZaUc#&#%U3w?J~o(3Me
zlm`^nwoi6~{pre48wmS2xtwOtDm#*I=T`N{*VRJWO}agS>z#e8=G9MQP_!xn$+yoq
za~iOr0H3TWsv#Z5SQ$|6S)Q+=qe#D#D?Wk8Xo}~X?89Qd^I%vzR{5BTqQO99=2Gv>
zgm1>-R?B*U8OJ*oV+N>jvIvFygN&{YT4J}$s^hDFk3BHghlnZ2yT7TojB^;az9c#~
za*Sy21E`rfF(*d~)6ylF27EbU^Z8vn$l!@rV}`F^=*i0=y^&)R*lD<|!l$>Y&GdGB
zDieHOlrMY}TLi;f3H6kQDrD*l!!eRhHr?<#8Vhm-h|PbWYxRq-_{<9wXmFs(1~HCN
z44BYYP=pa0Eif9+(E$(W#|XGH-MEz&pl-l=OviL?g}A!`o0PmQbsn5nu>8bf&(T@e
zT&D-NKHa-OG?PuE0>j)<bauMi6sl8g^p|-Cg-Mxht{~nAQvgkR(dd@vIO!_Z6DC(1
z;dybA8WH)y=M$s(@}?$!jDn^QebRw<1s5F+K^iL5Dt5Lfw6>_Fwd4v1I^HMJbV*C}
zak99deCKS|NA%WCqvM1>nBU%-LMwMQwp=Lz>AU;dQ%LF<&Ox5qgXnFNcM&f@4jci8
zRL$uWyV{+qtKzaQi6!EGn?2F<f)lgnz7OrLon3`Z5B!GQqg|W>wSH=f=;QV#a_qI(
zZXs-Ao5N4$hU_tYmt&kdtz~a(#Z`XVx)R`hAI<X7;kL$8ss{yS7s!=A-21nmS_#Uh
zfdlWF8Tf87Lb~2<x}e8&Oq;Ofu>>u}Prd?5YMTS*e7R!OixK<HOHT<D5ddC|SymsI
z)1)c%T$kA)?!}`YUL77Or8lJ=MG1)tHuCWep1=}$SyjHyJL_SyVeD7=^3>4=Qnsnf
zAFr!wA#F_>jV6wfHu|bP`au1~)O#cap}(9IPd2=#z)c`m8i)3es`FTHTbr?rco$Fv
zSVb+acEVh>dX;{ZG!MNq%ohS}k%ungn<sxsK)BkF>jHUb38&;vh%&*=;QK(Jbvq~R
zh9%T`fTyqVYR>k~@IjspIb&3~KrjBl1pIyq`cp|RiSnpN%+z<-8bAj7W^Ba!Spu^~
z>`oDXI*Z}ZIc);Qt(q}Qr|8tf{zQ?!RvoLEBM^L;zoQsFXrt$;Po0{pQ03_ZV&|IZ
zw@iqu#H5gGMD77e_oiU>mjt+!=;4iX4y=>Dll5<ygZ^dymFAnxQ|A_}viJ)tgX(Pm
z)Rg-A#f?3VFc7m(>PwD_@?JQeax`?sVq_h+XO+>7#A8!X3OqNF2sC6F?h<!!;S7Nl
z%wFK^tvE<8*VB%kv1EUW-9eKZ%|<-ciS8>fpNHps3Gs4gdn@@JsT+o7h)WI)&)oCY
zVL3aW#4NbVVxfJe=}7I#2M&&#EG$px`ua$Z$OD&y0!~r^H2Dl7NGLM;U$`4dLr-5Z
ztoV0?W@Z%Gjih%f%qfVQ5wCD<r~%%0w1IdeMXDA%u3*NNf^A!FYJoSBm98d^s?Q$T
zvAeU}%GJv5ij%^$^Xq-L5ybj4f455%8=r3&sg%OPD!NQ2kqIQt1Gr7d^1n}&OIXCu
z!sy!Q2Up#4HN!!w)NmXR>ICz7v#dOw@(YgIgo1$;#bIL&NHlv@*e=UI5Xt!G)_02n
zV*;%GuTfRKlF87%6^6%6XgY37Yma&XjIkddnwF2<1ym@o6{%W7ITFf0iFDDw4wTYW
zy5BM|A`k()Ua#W;jx)_6&mzOgC{u}+lT;F3y=(o3oe{i&-m8rtZ*u44w>Sb2c*Fq&
z6Rq!zPak>S^RpX8C{=jRlyN4Rd&we0k1WJcR6@|Dq{z{)q?iDSV!<kX(mlQRC?(b2
zTDS$kyK9K*KjJB%kML9`&xXR^>|ROiH4V+PX44oVGNW?x0adI9#x-+gs7~6s{_VpW
zzddoIG%NAEv?OzJzaztp8X*X|io?Xaas$o^EUm)pAEKU$qh$bZ#LjV=@rJ$uWh6h!
z#J&QpIDG1kaaHD9q86b`<K}6cAF~d`Qz9RQy7)@JKs&xq2pPb{QfOxQMS;BZRQQLr
zoR^Fo5(}-#E5|o!Mt&}|-nI)9B^cO+6ip;ey>tj+VRk_hfV1I(7hoh-Qu{4+7528q
z{b8@5woNYev0xX^et$VY*QQ+Afy9G%zz~+RvWo|Q97mXsfB)JhM?3o3!ttvpIt0V>
zonUR?cPp0&0b#=ekizxzkR3H^zYpku`&U;ReZ1qctHsG-XkYJLLzg00-$Pco!+0^`
z3&;<);@CaWfGhy`FJFdYtm|ThzC4PI#%QJ6vf68FX|>u;yFP#Xz3D;^D#q-l;r<c{
zaB6)RRx#NU`TiM6!EzfpMBjQ}NSKJ}eLy3;(>k|Q{BV$xoYY_odDcW3gph$|bVz8(
z)5GX1hVW3KdB})Qq~>4~dxgjdA8};O@2NX?>138hF710=&xGf<Vj{s$Kk?t+S4Lc&
z#F?({A%Cxz%t0-h99@LgZ$h}kjkFO3`ON0G5F0}<D3i_2W+SWj_O_PAP|Hr08_fE;
zfn>224=&hxMJE+@j0prfG#Nrl*X)v!ky%4H$KiK(t*Fa%oAuR2u;gb1K$$#w4~zb>
z5&?~r^8*E~+o%QR?t`Ac&k>ZOSiw$%Ctlu(;@2^AYPj`K-5k+(<E5}zcq69`z$$(X
zZ`z?fT~1o8&i<Y7Uwr#r{_7{9UT1l`zKniY!hSeFyX5S%ao>ye(tyV03pbz7+MX$W
zP$CP<I(BDQj#b*7B5OpO(u$Y=7a4c-h+MixRNs~{P#h(E2CO6-QIB+QTWzSL-7PO%
zF)aybKMNQhJoMIp730PxkFYK@L~*!S94013%Io;=&6(+Qf2(pO3su;&pZ%)nGLB8Z
zyPj$bSK>JVpGEnlcu71t;8^J1a%e6*Ntj5Ywq<TtCp^c>3sb96u>+khPZHzq&&p0w
ztd>vo1-3UV<#WOlY92?Hu?j75e@lzIKKRIKFMy501HvwJkbQ|g`S$4#glb(^8~qd1
z0LGUv<G2i{$0Qt?Fmh<QM;7Gdu~*s;o~Kw$5c!2Ee}Y$5C6|edJ6*02?;&?tW|%;B
z<TAa>g~Il>y#}Q!E0~02l&yYBNU;E)gjo3!!8@5|#JUeF7X5LltdajO%PfE?<MR=P
zzj=LPN!dx-_;(_hygO~R5s5y7O%?k&_)e3)M9xNrf4%P=zzkG8YL<dZveg6^j{$T)
zi1EsIB2i1*TdUHV1zA&6xi$-mN19^P=qjs5gaE=0T4gf(O=mObw(2xo+3_7-iPF*@
zwK%d$G72vzmkSW0=dZl?(Rj$$F{);}UoaB)BDqeY3rCN6jUgd)7gfIs6wogl<iH(n
zS$7uhewoJ~z5rFy26%!A<`($gUx@a=;LUq?Bm&S<WXhQNoS}I6^8ogXFwAc!D#wOC
zDHc}9k5%HtVG+2W1Du(F@LlJg{6=4j@+qYYY*4Y~mCU6%be-@`dcNbaVv1wdS5Hj|
z_a@!2lQ|9}({(#)YgvTr;^Z!9yXjpp9Y^qhk?J1^?QARA=^p=C2*N%-C!Ribm!kXQ
z1o`iSZ=Y5)H?Ou~qht5nIdD1tx#z%IaOk5)sr--&$I}dd8YLw}FcOjF`1`V8Im^eI
z1__g4nTz6v2oh~Syc7(_++-`W`xSuC6Hh^1F*mW5GZA-WYn>$ByUnC&=?rVq#e4$y
zZPwc`FldGytYGJ0CMVqUOmQReiJ@;<lr1u1X0Ag^$XrX>k@sAg<wk@?mrYIf++||w
zW|m0i6SX)tQQT)(xn{|zq9BgEtIz%8woe6p7D!R%pUvxxAd?;r>~<>>9@e;LHig)q
zG`&<JVdk0L8PF*bD6iu2aZtrAy8KQE8{7^<D>JU)=k#6@cm)<%o>L<=M#5o??o=aS
z`yc&A1_n$TVq@jH+dW6_2gO(Y5^7VU4L_{Ujb|(MeBIiCpx^-AR4M<pZcEUO0Hb&g
zu|%&`6L;iSkQprH(jKxScfc#(KbY~=*6ywuGd@}14RKt8o;Q94ihwqeWp`|&WcO)U
zV;!0-p+MiImXI0g9x=idYa-ew)<HU&eh4;Joi916LXm)$pQ*)g?A75r^snBA4!Gtr
z16iqA>ddH|h+*|7jc-12JO?0c;*X$v0W?snMJC-G*F-}NhTo!!I@Dtk#Qi6`YL8(H
zOqnuA{jF%$f?xqW?NZi5RsAZr0WNNR%#O4r4Mwdlm%M6Uh?S^JDH%@q-yLm~<-84e
z*^r4l(IH!uY9>wnJ|CEN{IVBxp8^QY_A41ehxR!ZkMeUdpgaJk4E}<)Nk4g?=h@II
zlb^UnuUS~E1bRi#TFgZy6s+EKCeT}x(6#F1o2)g@u%h>patNSaaxQZIjt8i?9y%G&
zPO}K$OT%wGc<6*kCI%H7-gR~=TZ_6$0wbs<)1j3YOtQb+0vJ;wU&dhOaXQ0HaXmzt
zdd85#?+5PRI9l-=m&`mVV*mh@v&-z4F;8xGEgvt-PB*vt(kd+3ee|LBBKJ}v#HhD>
zmZckbF~#jo6Z9qD9SS;ptC7U@lII8MSkNiwTV_!YO_3<JWq~EvZuNJuEB@k+6$CP5
zVXRyIjZ|bZa|!sj$^1?mTrPkLS&fB4jyN7VmsD4vBUEJADPnOiB}8ppBWDcfaXtm?
zb>cuMbKY6J>(AVLHh0ix@IC9{Cp2(gP$Nud6dOKhusR)pZf^zoE<$UtXv9HQtKPnJ
znN+dSW{8LhlQ7eO6X4Ksvp|1A&dV%Gc5g${qz051V+3)<=Rw~r?~82(jwz7Yhuyc}
z7TJqXGpbME5Z&$jBLD%6V`>FOL@=GM>&7+wVQN)}<5Az_#=8rDRA|X_PbZb^=~DQE
zS4#^NTmU}LHDd>5Q4;;Taq#Nm{!%VB_GK+s-qJ^P!qK84?;lgXv$Bhil#n4Lr2a-g
zNnEXb<?D2u*I^ohRYe%gM>!xK;kMHiGUn#_QzR;Dpd&QshPrN&&|9;SPDl3-DQXI&
z%?CTHH4?H6&Xm|wOTY0Q&`#nUiY_`Mgt}@$AYf!^RS)j`5rD1^4LX$#?|S4)s3}F3
z)ndQu0ZE}97`F~im24W1VT7FXtw}J;4EmH;1R*$PMA(CikN}3(M}|#@2XU2P9ZJKl
zw)x6iqt@W>20!RmidR)Mc!=-U%G#_^vTxVGseX(HiWd1DbP*6F*#P*<lO4`~-KR-)
zjIjEahA8*!5bpW7D!!Md#Q%)(eYI&PR=gxrb}RQV3)o(>_^jW#SPIy0u4B@sxoG}W
zT$>WgnAc#*o&%UyK^Ks%d182&Y91!usb2Ul<sGt_IEv*fANI)UH;vr*`58qTzh%g<
zN3Ep^FiqG}OSVN~ALc#Jo^~LY+j;6L8Oo4xH#?T(ypVz9r!|u<t0GU3Jy_`o+Gp2U
zq+p>eu<Km7KVXZd9@4n?HvqMFVAMpZo*Kf7mB6xZFNi0YWJbNB)*1-od!A_cJuTB6
z>9<3ao}4gbfRuS(>naQaM{5P+Q&s;DgKw&fNfq>UaP8a>_{<_Fj^GV`#7}@nR&z%6
zjw`OSWWfu0L<x#wt2-mHq(<`!j%xPLEN(A~d@ZG^kNZcXEdSl{H^&x>Wm}qhG6h3{
z;D>^%z45G5DnLm@Oyoea2vGf;c1ZqaGl>vI5-6T|TC}8j<4>{IloF}h<l)avs`&N{
zp=s3djt?M6uJ?mJk@e+=mvriY2ZLs<Bqp)2%#aXoi@!DyM7}zxCI@<m>q4%<<&nXa
z&!FoQ7q3#iSl*@;S^A2Wf$(yctcCF;*F{(CEG#RDGz53R$Fo$-tA3*Z0oxo`7ul4P
zX2w2<+l50E;4Ug4e4Q$LXB0SUAdw)egaR6v`d?aCGxVuJRy}i;UH-2$4rcs(ZSa1O
ziCf;~Qi|aG{fHIRR?-nC<{=j3B=$NLE$uSS4elu(xnGXsc?lwpD=4!fJ_7-FtZ^&W
zF7=Z4zLoQ#Zp5l11LLnOkWOC%StKTb>aE-$Q}p;<<*;rEj3%RBGP9aCiYQ$@kV(Ol
z*1vY1;iDTJ#o0mdBD$iQSX_V|dhBH<!;~)xno<|q7H>nwYsD|}5)s`H_TYJlGO&+<
zDX=btEXMqkj?S&JtP0Q-VpIJvC1O9yA9JKOO){oAPZPbV6B>Og*4NH>#oPs8E8mNx
zN^-5<r|je*O>8I(?*}GKh<;<(%YqO*S?5XmzZe8Jh+da|YSJFHW9zHo*NC(a^(O;N
zKz&h6!&+jZBo`0NSJAg!$+n<H(4f0n4qn?>D*TR0{K$sV-xMug#)vMQ+94P`YA-nB
z-^~6zG0OL!+^FT$nOWd;27M<RmADuV>-?s8g6HlHs4>FH1uIBc6Wy)y*q=LpI&o^-
zBPJTbb1Bgp;51VZw`x3PvurT+dHVduk!z~bh!x|7<px3Mz>~&B<HTv5E<9fHi=FWh
zz4-<6@#dFjBkzcAMC7#medSeJ9t@oVzu5D8&}Xg<d!5IyYPnBAb46>7=UV)FqHtS8
z^zG?=a{LpL>5`H&1${630)};i2MpOAp{!~>UPKuP8YX=COFPC<N}Xm(dWHpA)L@hX
zy*vPAZA&XL0m*}ux$#V2n=l0ozeW~P=gT5GXJWb+l~kpp$nc%Zmj7y|Xe7KjzA8T(
zw5gr`QLx5iAdCML$6z$77hR^8Hc})mCAr>2IkO6ONo&a+iV-t^J(yPvtNWCC+RWyB
z6SD(|+`ReRgEMZ(p@@oz8`l~vG0S^|g&Up)v^uSZI%xg7=iE|esckuA-Sk2cDj$cX
z=Q{M)g;P4P2|<6#>jkO80tvhgR%Zf?h&&t7x7~F!S&E;D9Ju+_c-x_Q1xbmz!GODf
z#>2PmkxsbmAb8}O1~E92JI4=!`xBhb4}i}wYqYFR)n}ka>JXlmyz{xXnv=m7MeKRd
zUM?iATAs*<K@Ufc*t3sanrID1Bq2wg5cuz7>Rcv4xU>Ffm@)8CHg137K&Ua|u}&^q
z;Wf>MpZPs0m1{PN{AvDH=2(~pC8J~i!G{23YWqO>;(lTyuzPr><o&{1p|H}Sl9Ne*
zM=*)NHAl!T60|Cha1Z-i>1F`1H_0)EI}#@|n-%}`!DBX}!5W+j___?26*w}q1_&Gk
ziIIV$`tf(;V&YHaUyDU`SNlX(ApRTWoy&sFW>Rx1YFssv-b69{gs96|hV-Z(R}wUl
zsZ@Wb){;_#Yh1Sy^XyWX@)i4`H;}NijiXfl@pN49MJ{vJr<3*ApxFd3R{rnw9>78C
ztF1^gt-0hMcKyqH8)91&2N50cza%ACz2K5)hHQ#(_E@@zv-ct*m+_xWvxURex;ZR=
z3J25VL+FcyRaN%CDYW*;plZAFL{f!V*6zlqw9s+7kqozTD7D2G9x2woktOZs(->Rf
z3U=1cO_etwjKT`>{XXU~P+kR>n!Vo@hRuXczPPX}PMi0v_|(T_{Bwm;3~R$%839Tu
z(ciWqY?2_j3E?*Ktt#3ed-jLI*6?5@>hV7O38uCyd`MyQ{Nm(1^AN}TSa*+cf)?6~
zHx+pUUy|!s7NLrj)^H57$uV@TxG=l?h{(2U<+jVc%8@RXWS25*OM7;eDnF^ueaz()
z0Iq<hlaV=@bBd`-BzUHnwG?BIXDFuU!Bl{bZ6IIwlRC)Qxta%zYG~6(hb6iPw7hUa
zUGmMN1q*3v0~|aHf*yP>c_s?UW1*eB=OLU~qM>zW@JYlbsd)v4UrC+`L#yAs<3b>^
zdZ=0b@%Z=Kgrz3UOHC^eGYv!OUc@%DT@u&W^*0mhAPHl)$Z2%P*lAel^vH>pizj7)
zBc3<`x;Sm#FjNjJ4c5>IlP;Lan>)Nw2F)8UsUm!kJiw&pv9szO@*~lw++nN?n+%D}
zLu`7;7m~XCOsa5|g}sKZktYgjx%$r!J<CBZFJ`&QT&m)+5B55Az#}s@g-pC`A65K=
z9lJ>?XA;i*eBm_+ojmR2Kn?l+UBTJxU|*?E=9hTJdcpH++Xdp<_U%rv^Q1sRbTy@I
z^|T=`O7+rsN8rGG@M^h^9-c-(n}Te-dXz5FD?NtxyD17QksxES=P>lkE59n}n6wVJ
zJPwC~yl_nI=gaoEP3e~}eZUc7F#WkYQt2CCcQEHl9nPQ4izP(kuM^HF7!2FC$RowT
zTgeBrZH`Lv66kaxvtC{8U7+jITU(&y#RF!$O`+hbpab)HY|i;b?zP=5i0LZ0K}xo@
z<^Q?Khds1kn&j>tG|QEN*4c7u)2lQQGw4{)75%E`S`Iig`2O-|?{ko^_DI9|QJO9P
z=1P^=ka^PnIZ|RcY;x0dd=8<YsYK>&#}>m0v<S>=Z^vfDl_*!5Q7yWf2RUa1Nu|Xe
z?->p?(LKbID?eg!uDCUhk_3FS#NG8%v2E4x!I!$aHl-17%ez}{HVJdFn&e#c{ds$9
zlW?B%z_Up`J(*~#D%a8`S=WJ3r!^<tK5@B)L5`2n8Q<<3X}r|e6@g6XN%V>f*mARd
zb;@Xa4j4UbgCpMOwBQx!Bt^2EgoMVsqQTvlW|z3guRRgl7nsrR%kbjTRe7ivTZLb}
zlp;A%`HMDuomr(3yf_F!^(L{rW*e(LfN-<HN->2Lt%Q`RLxKnemVNC(jqKgEGa0lU
z8SF#pN>o;)X;k^yB+HUog2rQSN4b*0I64G=R-l!U4-qVxuMM5)U6q>4vcOdw=B^H2
zP5>iF$@UIxz!cTH(6Z`ifL-#TfnJczk*;=k@&S%;3uW0^4L$70VdjfKCYDcPCgJ+G
znF}hs-c>29lu8mRe41Nt)mNdQRID*(|B=Z;d$Mtlh$_GW2U%Mm+hS5E17?*2B`Sg(
zZOa~~&T(o{U)BkV$Erg-WZT2Lkv0XCz{Z(sL;BK>NYd<&{1~HbS6V~NRs*7AD7k`J
zzKi2)K5|a;^I}$G{#1(M1a#yQ=q{N87+rp7f?&KmndYf(MpK@C4$o>W>$y`SCuoiJ
zvP>wlOtK7)LR}5aJDl?0*hTpG_#{k<S;eP~z&vk6uiXGw!j+)IUM|fw{3y*rE<=}U
z2?n`vL>LrsjnWDuQ`9DEyer$XmVFUqIoRqAE4DbU6+B31O`0Vw5d6g@L;2IeBkxsq
z()svg5W%|S7z3mW-Fnx{1U1F+Txj5Dms!zkknAAiGSQ|&9tJ8xaCT-Fvo$CP!F<^y
zhm^DvUuP6gZeIVg37FXAKdxtv``_>bC+k@q=9%eZImW*Zpi0*f%<O&9jM96IQk5l8
z2NEhFqi--gkOC^iAe@1M2{m{!6G$l8G04x?b1n)bd`G7NO=~+aDXTkkRqFaY@9>!h
zs7FQ~TVi|@{Yk=bV{tY8EP(Am%!Pl?xaaOAMIF~9Fpg3z{Sd;MS^aEf;gjf!El_fL
z#cB>~F;fU63DTHtTdih<4~%xRbJ-mda=cH2T@@`WPFqd91=aABVdq&YCOD{6%v7VS
z&}Qc2;Q)&J1>Tz5<e1qeL<)B3p>E2KBD2(R`k~xT!MtPX2)x)NZ*)xbYdRahx1cJO
z>79Xy1C0$hLP^ouB4Tb=`Z!$Om+=pmNB5>Z`8JLo_605&`kYU4G^p7HTWW7YhJN8e
z#y$}NR6oz3&B@IgZGj8d;tjUH<p7Nj=}C})h8&{Y&8Uio6TAxD8hI{$z0HNlh@QL5
zRX+D%^P-3AA)COL7bB;gzxA*H@rpsjEvz3xT&;jLC?DyQ@wl21qD6Un8Hn?{$j-ll
z5wm{MD9<Pq6_#a5Fjz4{LLxzueWgF>n0NrO;HyAvuxE{*ueL~jF8Sj(hyP$8+GoT%
zGvVHd)E`<5OJ_a6+hGISfujr$tlf?o`}<sZ!LYT*qTrHzYH`=a2qzFL-Q<pGb@*g>
zLoB;5yjG@6_a~Cxj*`cYOAGuweMQR0p>!;_B+Di%d+xf*y}lr7H8K_ll%TB>{gLKf
z`~{CUEih-MWQg@LBQF$KAJ2bYv*LrkBzm_gm$zW*^IGRJAy;4etJK6Po|*YYQS&I@
zz2x$7SXb9yMz|Utf%b{K*Slg}Pyz)kfS&}=55rI<$Q>mw%mJ9N!y0W#YOV4yS5&j=
zDz$(idrhtaM%ry5$DkDRsBWIlrow-bLezUrk^Q~uLx>iD<5tj6ViR$8y|Yw4U@)gI
zS!+cKlr6>=LNLutaE~lS{G>l6@{YOZ6wILY2E7Q_Z-c=hYD6nE7n*8CenI@H6&U%D
zVV$W(z!r$D(RGd9q1ux`zTlA%ED$1;zd8z9<|86?k4@Fnl@HRro!*=|tw2no5;H^T
z)Z0ntJ(~vA_E=@{hF!`uW5`mHb=XdTm}<8UY!B>NM<9U~_Kw9;1EOmhfMWcv)+d7K
z({%YuPXXEY$Kai7=JEw{b-9ILfbT1P{J#A{v#0%h=MF?!3g`qLTj>5Ry)OgcYAGpe
zEV*ZJNP&mwHN=X2h+0#~&6xV&%{gWG9A+F90^f7|<o12J8J&^Vy?_MQGb@k5E!%sC
z3qy8;s|XAOlYj`<GH>l}y%+c(v^_dq-rX9B?bFL^E5oI}+Z+hvo)8;Kx1=3va%SHg
zKm$ilZEwBcF>ph4%pV<IKGVd`UAyY}5gkXx#(OG$A7N()m08sOnO9+vJ`PaDVZbp?
z^Q8C+snlwX6AiL%^yZU_Q$nQl4HkCVTc4e$`;QJxD1IHeav}6B#|jKsGL^jg*(=kp
zeI{QXZr;{1f%4e(rws@3(hm*;bWCrJYcD<M$}4EKw!RgU0ov5id)+JRB2c0tpHB8G
zd;u19Bpcql8VswwgCd&iH9-0lC}Q|c$-{da>U!}q)7r^uSlIjna`$fnpt!4z+yu)F
zJe<kxZzO-z39z-C&Ae=Yp+|eE#V0w?LKGr4{f6{h1u(_2W60s%nrLSe6;B)8bS^Y(
z7OyBen#evYZE9uuMOd}>k2>yRm9!RK5}qW`t%0)QO%aI@#+YMAdP7N`)(o39@-4pN
z$az`#IU53wQ&e$CVZ+pC>hoU2K{Jnb!vJ1jNb#2xTzsZb4KGjC%wCogW$j{pQDMiO
zo9ohN7;CU%25#0BfrQcnhlR#doNJH!wz?A4UiqGo9f?0_yk<2>UVc)jy&}m^7pjYh
zKIcqvLbv2#RI|poj2*@h`S!~rh7vfwu&pl6)JpDHJZt-!)tkQ4j<PU<TFBwNQ#<4b
zh8nL*uCV{u?qQ*3&+k{2anu8}v@9U_82f@UMd?)SJLjk&N?=c7|D^+ux#_aN7v_=3
z2+)W0AauRz1s@R*NbFMWL+gn4T1@^y1thBf3Yu7nIs2>)y-K%Gkn?P6K!k~KQ?q{6
z#6_BdPN&<b)LuC7!_AE>J7k&v+<v+a?*T%7B{=*A#8fZ#?7QM+yTASg57(D_4rvOm
z$FE)vPBgi*LqCc5WSuwm_=IhkR~8q>M`TB8+SRC!bvnt*;388=o?_=Q0}S*z1%Y;B
zWXV%UjI*n2WUYtf6C}{s`Tlk;JXW?hM;(y!{LMnJG7(}<J0BzKY<{;JJ`MHHc?}(d
z1`b4fRCL|mx^&w47J5-E)y2W(cqf5KGM^(KB+{y9-T`7!7PU4a9LbHH?2coe(nNv5
zNSEfq?DF!f^lGriq2yCK${!jcl5mVV^dKpnqMLA%3RXXdpGm;_qY^@hbzTIdU}gL!
zS|M4`@FtSb69>yHysx_V&NiC72qP9X5z8OKFdd9`<d8xNeV0+3k$6g#T<BFNlSfYT
zNqcGgO*Ien%^DB*IXXSIZ(KPkFD=UQWLg##rAS>rOCT4oNlu#~CLWse16bE;)jQ{O
z$zRJjk8~!I`E2BhL%BgwyB*E1D74$>b>93U{B2E?81DzzhwUx4I-7A9Rg@o$TH-k<
zp{XMKi7vM(FB`rMrpau|9_opNo5Ehg6X3Syt1#HQf(|<o4iQ+5{_p<y<8RAl_(Dh;
z?vCFWadlDq<=~6VSM`06fmddS{Hqa_MPC1i3rZL%mSu>Hm@WdYeHV#Sr)cu3j_1PM
zk(#R<p=2nV=1SMA=ZkkbDMj(MKMG23o{0Cnx|j2>SEPGKXc#1?Ma?v`irD(Fblwng
z{AA%G(=(sy;!5+&)n$bXm=ig!`7(*Ug4Q7!s=7KaF(mzc1K$u98TGKHY{heVw%L)i
zBlY4`_yV8&HP*ElNI%OOwLurwNV^HXf6M{(xq)I*fXz5BsYMnrY+!!{>l&~afT(EK
z$X07AK4e*Dl`c5b+<tS;B>d%64odjzGA)#BKBC+kyt}1dn3m(Cn)7pB(AwO~;&hRQ
z$G*XaZZX;t4Vm#iFIMb7RhE@V`&UT`#g)`K5=g~Gh-G-~?vxqxxyb8?Z<<(v8&VJH
z8zSsWloP`RUe2sY%vO!w+M>wKdq3S!%zNaUG1v%h8&W;ut{xpJl8=KQ2KkCxBjxEV
z!*0bOtJF0hf*zSi0mss;9H%9t^91AHG3g#7SWIgY?giOWJr_IVxD3CPLxJlf(qi!Q
z4q{fXaWaSOWi}88cn!t~Sf*raNgAn^aBr%As)M@%%FEv$;6dp62ZGMQ2bKBSv|$=?
z1<xK)OWc!g%J_gP^pbjbU{{MbhqK?L)2Iyn=U79|$BsL|m%x3FiZzIZ#QW9aBkiN4
zbfYP`gWJ+a9q;fDl5dW_@2r)0aNNE#xQpRy6`p1gs1uh+sulP%_1ee|dN?uG)yerj
zeQ&Cgs7wSh*yk8v*3-(sN&>I_e}<!h@mnVXC$=$x{Hh&w0SiJ>4q)^no!6H(=yG1I
zAX8GN_ok@3r{K!%g|R2ooo-C|?YVT1GqXGpg=8r05tiRAgc@!Ecy@bPYTJrQ2?;0m
zl$XdgtrnZmP6xR}VxaprJJedA%j4uMglRM&1jyLX9{CR*0Si|yY`kvMiO1J$D}R}p
ziwnTfe={sf3E-;c-KI!9#~jxSY_8b<92}C|J;Ux8!*=W-=uLleF&9pJ&(ONkbTnJ<
zn+zDUu-h<3fgM#U;`6Fy6|n(eu)i4qP~<KdG0*uLd9Y^dwMEAYEXQ{<R_qVLboav{
zPo$(INAw9o_)-N6LRmF>5m9|#<*MW(YzaVLiKcRexpEj}e{Rl^w}HK-Fb_S#bdyKK
z9TyXLfd(j@PmNVi_P6c4+`O2w=?1&Wr2N!arpjagaNfCVFImLQDy~s`SdWm8IaqM_
zG3w9T%lPh&LO{5kL@9#@6L%Aa#sDB6yr1mlMb<R}-YX$tT0LzJ^Z+>Ya?61344k;P
zDnE8&eMm;iNOGO-@RTpvVT3XXPvE8;b2xmHRQw<xaEv~GA}V7Bj{7_mTcOwSSq=a*
zHcR|Xmh}Ii`aFRWl$_^_P<59h?HvDmnV%G%@t&MPOtE4pcBwpEAUzHx9Th5=mM1$v
zHMKiXB3>`r`u+?N>(!|xH4zdkzoJAx^5o!Ep#}(XZ44^1zcTWJ6tYV#LjNZzY46;h
z;F&)s{Xviit^cR7$W&5G3>zTx(qdR*Jv{P=18ZaP;i1$>46L+|l>ym!=F!jwfwTWQ
z)cV}bE3J3BIF`QrwyVhJUAhd@l}+Ppk94Dt`l3itz6L@?%FUUxMJMXtEfS`)Ec`w+
zqy8Qb`}fS5a|^>b<7j8{^8t1YEI!xp{yfVObLZrS<BH-Lr7Da;u|%P}VO|&_3yPXQ
zB;^HxGyv|^)mXIL%7Ej|#C?am4g~vL1NECs0{g{~vI{U0i&o3%E;r9FUE&x$HFDLJ
zPUFMV6H^gx5@^O~W^3OM6B?W#-%8?-$Ugg0QJkq*n3@r_#QE@WO36aH?w$>(7l{yB
z`cLo10do+1YDnyRnLOzv_RMEYNbZVZ9dcSmw?X(l-ssA;^K&%)J|Q{rMyN9RKz(yB
z=;b>RrQb`+AD|&x(!;A|7*-@YH7+$C#t#l6|8AZiYKEpUL3BFOa9J|;m4O)lmsyo+
z!sGz`W!M$PnFza6tBVxwyef!1CFE)jMl>|l4mVJ535x;G0&$+Rn{`c;sjv1|2SXAs
zUoNY%baWaYbLw$!bwL|~WQM3pg$`1|H~8Uy4+c-tiPxHYx0QjO_HCu5`g^AdP%=u6
zY|wOmcXt^j+<QOHpT(IPoKT-0CRCx#m@QN#+TV!{dgX5FiuZob?K$tz30*q(@OAaj
z%jzy~nKrH;{1fQ2Hpw?wt<0&{x67(4rR-2eNfZXbEVV73R$)S+ohjjd$!?n19&3>L
zNI0;*F(=fMYD!e71~5Xm>rI7kxV+|XNqh%O<`2W@-vD&_sJZ(bb$pe4m~{LRh62a%
zqA^lTuU3kQbjAuKWT$U1>39h(vx<o{ovEsj2jufS0<@mnMTd*MBF~r^pvkUT9PVlj
z$0i9DUvrcM-?>?epDgC*i<G>zRc(;EJ2{-#GZK%)PWuZ8C*vKybh&;>fg%Noz?0uZ
zI+F#lVrDF6+{uV%Oq-116zc)mac=y+w8@TrEesiTq|m`p!w}qlB+u19R+m<0SzKUd
z9hs0XPVVS9PSTT(SMa24j?Z{Eh>gmdjd@rpEI8Z2<M$jLbP!Ws(EwfjwLPfeinwYn
zJE_109IAXLp?|-Qlo|5;;&0CqtVfcTgFfc4y~<t8Ti%rOWO2#3AH4`%Dhhas7Q%|C
zI`!0xeHjkjFa`|=&zR$E6rnU{N}gD>Lozb|>TuZ2YTM|jl#YT7jpD|m2jt-XoTpuJ
ztkmj^4j9X;h1aKdbS=BwRbSWxSsfq74JSE}iF5ngYPJ`r?}04}xa911CrxBb)3rxD
zlr`7`A&J}L9x~<7G#hn#D0_wURS@ZGKO176(Nq=Kb^hQPCoHjRG*iVntZhh(5X8-6
za@<Nk!PD_F;~e=Cf7lpjrLwp%pHg6`Ut-0xq0Vk%7+B)+6d>;hI&Mm<4WXv3wc{Lk
z*W*h6uB*<l%Cz~*N7?7)o@S~SXN$WO@)AY?H-&uf`8b6Ou_6oCTj60m)5g;Wd<KJ4
zHCxT)Y*A;^o}(97_q8>qck{>5@=?^AfyzRLcfqnB<z&F6Y;fX`%q9Z4V;INNF0M{2
zhA}TP$%ql8>^%T*`1zl(X}c^N!mNS0GoGn&xBSpu)G|~P3#B&LLVQUKMiOra8p-(q
zgl{3D^un)p3tef-b$2GDM_jM`C0#&a;c@Jmv5~hf=e1Zz5GA(j&$F@PAmIcoPOPFM
z4~3VyE(+hJy{(p?(^PLrd3yVRorf4z1IusMwbrx!L!<{mslWvl(iow}G68%(gO~9R
zF2NZo3ikYL0QYy@b3X^?BLB}<hr~oneOXCX4Sl!De&?}OnvY`=?k1-z&A+ecIYJaG
z$l?@??y^LF==v~Z38BgBGMsJ-Aml;|K&nq~EAc2Stzc}n@M-%E5tTEZ{Pl!iwcje_
zR-_4?UX4(vB_Ev1=6Qv$xFKKf#oIOak;DA2I8|#oD7}uWSYJ6<C?vVU&Q57XQFEl{
zMno%$H7eu?l!8W4%=S1`dIOyGUP&uwXG1d%r;ScFgq-6xp=6Ldal&U*nJqL}_()pU
z8swD4nr{{e*+sxn+1@5w<iqVlrbm^FP)2rvMK${ZMqBi^=zl`HbJX(gRhL<^$is42
zC;diVzIeq8*qOOe%OnB?9EI^dK7MGtAqe~*5m$^ck(}O6tt$QQx<2!r{|Xn#+(pO2
z0hi<`O3g5QZ%3lKH+Nd<*+xDV5>j<BFFKH(%HG@ak79nhsQoA@`g|BffBv<<X}QtT
zyqxcorvNU<kaTh~8Yoc-A|-59VlFnCle+Z1Ojv9#J5&ZEGnw1TdIPa!Y@Lcw&M`8u
zB7ESj^OC{QMgCLz;zNiq8XnlfKCRi4XPq~C8oO!AdGQ)Cqc^|XdO)@bQAT3HXjtl&
z_sG?QEjPFZUOezIK_|sC&qb($%bOH@5km{sh`n|dI~dcJg5LldZx$y8?!S0{a`yH^
z-;1=T#jY5E4v^Ud{$?_jJrlwMQ1&;V$eCdb4Yh?mgZhP~{Rm8q<209|W!W3K=mv2~
znG4yy(p`yR$6F!5&;QS^OZXoR($^JjWg5}Y135_N+NLXH`9B;#2m~cZj?N+5KB#RV
z*;m9du#krWeui}Z^|iCo5N5xBmcB(qHymK2r!^C!R9rbEp4G&-y0^oN7C0PRt~FVD
zITL>>G0EauYoc=_khOHFd6RQ#5#V=|kKG?&DRdZRAde}xyhsiBU89wmnq&zZtqyNH
zE2^>os*=S^@Ie)X+u+`i^ru|$sr_x#rQ`6>YdB6L^&7f`7Fy$-;I@VhkhlW0n?SvU
zNS~Bl`oiI_^NF23Lm-btr`@lhYs*$6J0<P4K;<Pg=F^Nzb)HH{esWI4n>>)9hKq!s
zb;*rG9Jv|&z4(L9LWS*rWij9-AXma0WN2{HTSeHd#p{2A;Te{S(?RG{Vq1#4{^JRW
zwtDm13R)4an4?U+miGDTdAoinF&dv-=OeR*O2zWt9oNMEfRDh74L`p7>qhW(d+vi1
zfWEhBW2P3zd=@W)R?@}4QUUUukY%a=yP&EiEMQJtEM_k;#(h7vOrJ|5)i|XmbB@VT
zrdI}Xl&wr60)Jm1Mi+n=^fE{Nkcir?8IqB}onPs=Y_tg}jIIDn`BcQ>|1$tmLz7_*
z-9_Pxqf!r8TPzu^lttd14?W7|&B7;B`tLpK+*b|e==phA5EWTMp6m=ln)~kflm6<0
z3%p^IPzH?{DvBx_0Lp{e6K#Wk0n)<3Bw?yUtrMK}o=-|?99f07DjPM9G#t!4LOL@)
zI{2ujt3caF;MalVBG>alX*i*nXFEQ&vtjy60Jff3*nEw#XB3WL-DIPI3)IluxCwMJ
z9yqK~LYQ3-o55c=N3VUF9+_?p`<za5;g6Pr<r!Csu!MSO3_j(Gcm!WYsfkE}Pz#wa
zVL{&(N?2-Sh=;-#T+(UNxI+$!oKb1re$lrGfzw$&u?pNFkgbqjbB=Zkx+G<H;MC%d
z4OY`%qvf_f@n2WDcEVM%i(^5mAZx$Ur<gkq-!w%f-OW>dC#DU%m2k{<3B0Me&^Qc7
z6RDzwAWq#l`-t8Qj4si?ti&U@YIJ0Acr)EBgK?MrnP96iei#<=q>Bf!`8F_h@x<W~
zg&?z>J;5%?Mz<71l|Zgm<zdD*sYNIyLYC<=S<{LkFIxU~_D(!JovM!v9xyu)q!C9r
zzG<B$LqBz7kC|?Hiz{c^8soPJRcP2i?AjO#1{1sj)~J`1W!N`raYL?)EgCVooMDr3
zFE*MU1K6bff#Y7QS+Ie(YV)c&_&T$Ea@kcG5`{rRx@nZk>@%mv#FBnw%$seLzCjC6
z&c9Y)!0QB#08+Y;v3Q;K&P|hDC0d;FF(DWfzOsJ$*$HypyM47Uj!b}jXoaLw$N=Ei
z&StDY@+4=j@gPN);o5lo%y(>QAFL8WlZ|yCgutJOpC~(#T`P5-O<_@Q25-hk>z~np
zrw30+NtAKjvQ^}%zOEgO!~ec~4tjU|{Co1!HHG@j<!>iQ3uy-8LtDjW|Gl|=3fnW=
zhaa-$_wHt@YfkL?md8#`3nq*7W($x7pQl?mY-dPJu~*ZvWdNp8xG=R`yzL~&pYOc~
zM%Z5BnTr=!TJ*JSmqA$pJXn2I>P=ybKf`uP@ZKP2yE5}-VM?$;XvZ7qftkGB)D{b!
zM*ZQt9o{8RG783Nv8r^vp>)`>uoOCCl_rGrG#>cA_DIBbmlCu%RsUc*2f$>c1i;#@
z(C>~p21gtz_Sh%bOy;L4>zkRuyh5o|^Q*isOZ{ZFTmDS-3RqhbHEsueq*<=8EegkI
zW9DhMRjf!sb^A+dcpeNiPo&`(y;m7-iRrPLc7V61hpB2av&*T_k0ZC6-U`AV56dmp
z?o(_D<=g5q$>QXS`cBs_b(8KCmB;5<<-Wy=-U;lh2B`3Kk?u&iC_HAY(->`{n*v7B
zO^L1DfszB-E3c}-J*<wLk!K8cJb&m4Smjv#jUSpB6CHkTZdXkR1_o>(S|F=EI5(Vk
zPtG*yHR%o*?;j7=cZRL!mR7~kGn@N#<Ie+?m7odPpt#6*#0{o+p0_pbx=N|FuKHTK
zZe7-!*^heh9skm|X#OTqjc+-znb?Qa4^}8Ah%qXiX2l#@dNHsr^W$xU5?#&Yt^G<6
zBZ;z~qSdjx4tL)fgDcoeWw#=bfi*t=T(eh|G1vT+xyj8Wc@D|k)jk}ns16=3*GYw@
zg;vdX_`?P-Uf@$7gyhonWR5PuNoWw_f>DZm3LVTm7W$yT74HM@FYyZT2s6Exw@#f4
zDiTD`>%!m9q})$xXnb`BC6dck4!56LEK!gIlt%%2xL-bo@sv#Lv+BX7F*4nxR^hd6
z;qTd{_+a34)uj*TSnvH2Qlb-KY;ItB!(Drb>N0x7Op62tVNH|6%oAme)ADFo?5jnv
zbK6I~={zPZ%G>B59#kQytrRjh)I<*|kC6z<n?|{l5z04=A@#D)9^x~^UE|>y&tq*d
zzdO`e8}9xbNj{$FrvwW6q59As9=YX7m@+BGH501b4)<IX<{9)RXPn_kr0p4^TYIo!
zc_!j;IAjfYgC6zAk^}?0{W{#?oo`d!Nt&Rf2@jO7*H1%oy?1kC@?>2$M!d#M&0FdM
z`1Zth-QJQec$<)PwkC$5AY)J+@iKzfiLkqQ&4@?N<d_|BGaPeF*H2<!Jr(zRj0INq
zxN0j6l`>?g1w1lOP1ON=Hk7exwHXWRpmO*?<z*efYZ<ybRH)L2^D&_8l5l87yw9@m
zFJx^wbZ-^Cp&Z*yIr>rQJehmRGj;g9=kW%gweIZf=3C4LFu$$15^4P2S>IiBm_`^C
zm$}b6wjAF?4stn)9x;zn`5DsG769M#(I-f5@#QxEzJ$+x^`C3eS=a48Z<tWj3@#Wi
z!6D++^)ionUjArtH3j~!L5=;z#B}PEfK#I`yO#eK@*WN04A3M!o(Sh)PE9J}v(dr&
zjs%LsYiPJ<^`7m_3lB{3RdOE#cQUXlOcXfc>C&c9EtwETrVP@_VrI>Jg->aX(Q2HH
zwoXsaB1$ry`KNRuhtj}XO0_-qsx^ZD!pc939>EB{XADPBmvYeeJ5E|>s<*H<)v=|p
zP0~en%QvC0UdCDa#@1IN%7IRoA353tcGCx)KhbEuv&66229qN>&Koko(K$fH747Oe
zVA{EWhSb#_C+FuDSbJG+iEjg7NU&*rxZ+h@U}Uwn{p%f&zx_)}!3fJb6GJcOcy4y2
z$lUC77Vp&*$v^EvQiX1p&<*_gOrds!Lxl`=i=^p95w#{j-c<R1AdqL{e`zr!HtKGG
z$dF+FPVaWCDlmHJ%Xh2h*u20TCLFj;aC90$$yXrg8&z?Ut_<&2*3qFUG|B`u$lNIZ
zhqO)&cX@u@$awq#7xrHjl<Lb#oO~60r%C|NBZ^7W65t}`Q;4#)T1}~uzVt#mv5hdb
z|7v;Zr!O_h&Msku)ybjPU|1N<fld9brrTr{0NDge+xFJ#^-Z<U9X5kQFC4Ygl8^i6
zao=J(SI#<URDIM(0Ji3OoR%K-a<#dueR8$geFT>l*beSl4n!VyzZcZBwW9_-oQBb7
zdrx!ow$gU2pZ7868D22mp6kN)(sQdfI2G9V<%R>UFybpDdxF^S7ba2_j=iGpf)5V@
zsLYNo!wz&}$|%06Kln5UBM``SUP#q$+YbfgsyfEZZXcbF2PH6%7g|G}T{AdqfO^p&
za~w&bchCWc7wAlMF%MbnDqR(icZtUiX*&9O3tVozHGf!qAdfC5yXOfyK;CXqS}i+O
zB^q;vb@DR!tiHb_u4wfb0!CoFF(h=M?gdpRoX$li%j{<!1lAbXdMsIWEy3UHhB+<v
zCmkfbzrjOr$|_0$f!ZTIF+{vpB@=`Zu3oU{4Gub{M<0I?RXiNJOb<X@(8^i%Wsm|t
zL`E_l(xZmo^BO7#HawE+_uaNKGM*V2=~v1Z6|+b+q8<S*2<IzQs@3Cm0`R_t@V=u8
z|GFD`lg4f%Y<<y5zn^Ja73hWfO{irxJ<1_P9~wi&SG+kl7Cz&)sKdJl#rBj%Ss~k`
zWqh{CDXTH{_7GsE0zFR&*q|0#oxKCpJRP5PSG&d6EVv}u3bRy&Kk>C>S|;>NL8Pqr
z!VnOWte?;D>?KGsX52xU`}hKH^_~ikim^1?M-2SbHI_PkPPi|+y(;VRj$&em5r^O;
zboo;&SQy+tK43RA_7k|*{3>AsE5EtT&NeD25>6HRSm|Pz4gaU_U^qy3mG&JzgP_;p
zj@79uDxDNM0v#X&``E;~8*(r{R$z2P4kW#NYk^`C$PMZ5nQhMOA2#Yia_GXE(#hPN
z+wsL+`p8FRi8n?BD9_l9^omNwVv=+5wG_)WDIu9gh15>td?GaOJ?p6}(zy59h~<W0
zS`Lzkj?Vflon}_k7Yk>DVO!vR&ug<H_}NO#tgeeJz@?}vjZkkf?>)V`p50|nPtt6u
zz$H#eQUk>`%LDcRa$hs^VQ+kl%9}APOrvnhxDBtH{x;Y<14FrUJ%8WzH<oI%8m*~s
z_A}+JOG~c5h)QBvvtgk;&fddDEEsI$Nx8py=-Vc7aR=I@NLk^(v}lRv15W7L$H7Wv
ztl=I&^rtNyo&iQlr4JZzC&k<*L4yx*obK`{&Quz~fKF+rl=h_eX0XAS!oZtyTfaDv
zFm7l*)rxYT`X-rp>1#kxH}@>N2YR^5zHlusHE3k#e#nAJrJ#xb*}Z>5UdK{ulYT=R
z*t$e`tywx>-%w`_9{lyIQq&M#HV^x4^9iGDljxmQ)#PxWUMWXsv45&8@kt_&=O9)S
z)|tGi)!Q#0jQbvQZS}xmpGC66JZS@(i(mfiht0Dj3X0<$!3Ds~CxE6@2ig*7i5CeH
zyOG%uC5H?gxtUqKk#+{nzepY8`Q#Uq|5S?2;>oC2Vj-d+v*&(P|0%(_HpmCYi+<B|
z|Fldce^J&h|Ddg7;bnXlUDJi$C^XWmGw<)2X7y!)yo|sHDAP|h_%s(D3IB1CDc0+s
zHz{FuCyV@kmBVaC>>wnNCXRMe+QDL>eAy@`XO8OPlgu!EHG34~gq%5Upy{MXz3V?3
z(;bY)n<V3idUglQqDg<XQq$?hHk<#ce<g-Z*LzPzasmGoGzInbgNL_p-0bHsL|#IG
z*?(hR3wxOXwXfwuD?HlStNZVQhWyvQC_XvbO6({X;&xL726j$f>VLj_bfC}XLdiUN
z+gJQs49}&-zhs`_dve#Fr<BG!iHk({4Fue0N&dh#QQ=>w1p*(s02B;b=^FY2o(s$%
zgot>A%3L9>q?}Ax8Hj{};Y&l@hG5r1uhKy14s~M!w(>`ge*Dd@EGY2ld>7Z*v&6n3
z{7O2e4AUc>*R!fNm^{|`4B;YMO*;c;k0h2YP5(Y=MP-3QLqM@~Ok$k+za66&$S!6%
zC_djRFTMW7e_{-r*gDVk>=<R5k`&(&Dz~Ht<M{B8JN-Ug4OU(Axmq*2PF@WGK{#jS
zN(FM1gc_yt`#{1#>SgD_BdZO;`~q)P-6ZX<XYR@*JG;~zzrw?##B98MC{ab14W~1@
zN_81FYommEH2RU9z(>?@q_KBxkaPY-@O$O{IaGx+S3hP_-KLU?=axLwV|D)iNaex}
zRlTL{fAIxqIRkSs^hIJz5NR7@CvJ_K@uj0WI*tJwZU}X)RNyd*WTvwqs|tvKb-+%J
z;OM}nOq}GE*3revqU|JFm%s4H>}ohd#tZzVwfY=~nbdeioV-0%fca8?fMZ<{Wm8~t
z|1XD#LdK9hSzMQ<l(|w)1LjpfACIPVM6c0oVI8%4AR7`GJ)|yK*LFC#OZG^bRIc$*
zoe<z49&-HM^hL6}*G*>FjO$!%AK_meJEg~64Z@_lE(5PLw_J^ObpcDbo=Q+XFc4K=
z@DbJRrcq|V+|jWzMR4gt@J}amLV$W_TJ|y1A&12<)MGbDHR?t6ZU%uqfZZ`gEq>5e
z`i1>^T_lfqG6v*ME@((cYB@lnb@<xiWa>|%#}-BqqW6c6D549Lr)_EYsDECydxf!@
z!oQ=E;a44IABsl?2^AFozk93^ilC*vDPxeo?o(mBsz>o!>0IqUQ}uu%HfV@@Q=Yz~
z(Ymlx6NQ7_yd`9?km3b?vx)|Ra-E=2A4Wk=^bw+Rt<<cYj$udeU5$u|WWx-^nnOt-
zGJAmhd#RWI_)&W&!>_)|2+|-m+EA%Sl*S0S&iKoHfNm!F1+=bv?Lg196Ifp2tZ%g*
zGJ40LMCYML=PaGUbSLLmk$#`N9!WScHcvpA)H2%~Pd5u298~Z*<*E2@qwvr){t{F7
z0K3@M4qniVks_Cq)&le5wzs4bi@N8nAlS*`Zbf7JSlrxes#()colX(yetL4$%L+0(
zj5?}=%Z*^rLUxo4ggDm`CDP(Nv>qp)cPzxNl9332@^qBECM1{>x`gU|a^nl{7X-8X
z{3Erte+FNe;7M1z*r4tkwTZT9Wd@yt*)`dYu?GeNJ`_X~)9+s{RUun%XXGH%?Yn)Z
z9@1dC`Fg)BS&sSsSuQBUQ}Y5u8aHp{YzCw_isF$XXOACEBRNhm2F%D;T%i054UGAh
zsKORU=YF%2O4X}*0}U+*n^;eqMx#v4txsd1Y~!@04jX9t+2WRRzTY!+eqg_LB6ski
zE%L{b@<2vhQ3YgiZ5w8yslNpD&csR;*C?CV5?zm}2O*x7z}!RSjBM_@A$G(^+0`9G
ziNVvw@t_OR=>fylg`)Wj-01^Y!Yj02e|B!~s8gqOYeYRXczI+oe#Tm@@;p)XTdoDa
z?9UWS?L6W4$NXJUNL&3x$um$}ceTpcDXA;*hm-?3*pbm%mFvj?`<<7TZGC&;Th|rx
zuPUZKByCHws=12q&9!6Q8Nf{~w~Y1vO)bWKUuv8>%#f(v^F@t!4^9S#uIqGP7^)4z
z5eF5inX{YEjt)+bnY{-lAf3diVrqKmGZKB?!RPbDt+#Cq!W?(+n;varAd~ejWM6gE
zuBEV`M9sT>$2S2;cGaU+@8VuZXL4NdarxgV-Kv97uJgYJwBzN2&x+7y(M>X0eXh1{
z2zFrStYi1|etBDh)b15lec7uM0$_go@W)o95^DgG@M61JyZ^%55VoikpufIpgP{35
z)vQ~;fIce<10qHW9`;=^@8k?0uK8W%sW&&w#P-xxn0;h`&28gM47X-_7hT{4Dhy7W
zzCsw_-jPa~it(s0=^YB`q$sKMWV@p>gL6+&3g&a%zJc=X$x*}<7;slU(Zn&}0(%&G
zb0<zA?!7+QG9XS()5FJB#Pq4LrOZG}&_Qji;n`luCKRG~0B7q|y;dtfrLJfC1wR`D
z?T+4n9jph4)#GSXLcOs+YQJ&$wRi5f*QdpX`DZ9^+kw34P7z_!soczYds%o?8(HQJ
zEW7WsnR}Z8_^K2v-p-QbNlMa9z49BD8*GrI(?@{Db6u<}fX@ayJTZT1UK(V(Jzc<H
z?;4TL-awYzP$_AFJ!B<CZ@SR2T194NalKj9>lyBHX6zd1Km?ojEGBc)gCU1^Y-wFU
z*#ORjR(&=+0GonOxqvnk4BLGppr2IUri0YgCxKMD9g!z18hDDc4@=ovW%oxst-uc~
znDJz|d*)vGjz~U9_wNw!i?adoK5LQLAFS-nedPjJ%dE!WdeJIPCQGR4+~-o~3PBYe
zz*S>Ru;^)l8-UzXA$##|-z`^u*x-;y{8`oXMoj|Qjahv}ek@gl;=e}9@<Bs=vJ<<6
z$TBTp_h2u<fcG#kn>;0*;o5f!@lGBlXT$M?ORLGyOOPR$p0I@}%G&Pc=HO*ua+FKE
zIh0JLmCv(4mD9A_cI%4O9+M8n1cxNQq<gjPx?%?ce3!adwS&sybnu(rQ}$A?owFjG
zp@NF&jSiX~nx0!qB;@j=+=i};X}UAJGHxC5b8qFJ=?a_R*ozO1HC{XoYT5t`ErXv(
z-|2NPsQ~I6Ktt3a%O4_WRqSC+Nx4Gnw~kVv_Tp&FfMYOqH`5`irYyLHdul-ST#m=i
zd>d;GXcOXMph-6|o@8}>dh;;2cGajWXG-y*ZO!rV3w3{j2oWp{GmVF5z%6!j95<~6
ziGDo*`&kTNt+Q_E5G~f)h;MM*AInjs1mYbB%2p`iMM)O5eNWqYul;pcjOrb^y}Qq2
z7xFjMc$sdCkSfA1iV#-h+@}xX1BTr#_PKM~JJlBTUvB$bj!=7VC3WJ;P&9LxDD5&C
z+IZGQ4_shYqfh-;PIw%ji`<jyjlC7s8aKJ|jRm+l(5k@r(FeWNR!H_)z}KlWYR@kH
zQhs10Sp+am<-It=(&t2o=Q7Yv9VRx}vRsFfW(|mg2<@7MyAAZ6a>!po^NKn$xSlCa
z`vA6i_S#O%_Gzj3eN;z(Z|RoQ4Y<LTTxuVFq6&G{=Ju6v)`DQ>$fNSgJ`1o^#5sFc
zY}$=l=d>%8%MKTJxEzBwG1USbGs_lFsKQ2S#5N(#rmTNwQAIgSjH!$DTLt%wwcDB}
zg(XP8+P!;+QZiLdI6;*?&4vMyjZFw`*TyevMEUFBfJmE@GApZk7ce$)J-1H}47vqJ
z;5z@2tyd*Py7s$}I$cUK8RSPZq`euiO!cyyw|5H;jiRF}rPBe9JkOC8jO@!SDwBWe
z;>4LOK%mFLOa~@wPuQwMHj>pXU%5GKm68NMc$x4w#Kk`lolmKPde`<1Jx$#vODP;F
zWz8P!n$S^H9g-mQAG^yA;<X`+jAh$U%5*WVLEu7@wSkTjg%j6IoSddik}y<T;}N%h
zIhIwHZD9?LtUSf^_9t6Jut0Dy=I=i~vrdaZIpdkJ40|R8c7;xxu}yNJnaB&>(+ll@
zLg9b)GooKgXN>`~H9=qI!LvcjhDqDoCunZzc8Up`_J0cfMfc;D6_aA`OED4tDKWY&
zW#}_)v^f!^h+WLVFn33HuDpt1`PTuk720LV>-AEs1?LmPzTPclz=a8w&Q8=&&nt`G
zH{jVil!?y>82m~g@~3mSc|!2a@Q#mXAY|zv6i<I~8P}x{qq!M&mtiA8@_WKbdCZM;
zuWI;G9~{$n*)=``xzlHVZ|gX{UhhT021*h<8iZ6#t`bH9m@DT2a^p9SQ=?f(K$Do@
zZPt*#6&WTkrZ%!P-I;pQ9r%;ty340hsz)|66ZtOXe?h_U^I?u8Jcs%5kxFnK&M*_0
zf~PtkR6~1B3s*gy4yu5FU(Ju^;cu`WUh?;7+GadvvfMS?@Sox+jldH4dOv(b7F>zM
z+<<Pi!$<>)n&xen1)fO}e$DU^wPZyVv}qVmlbx345ZL*dEs@2Kkuz&SxHy^Ev6Y_G
zTo;|2w<tyLqLs6K`!I*DK+(3#L#ng1$1l{{-NDY;gt5M=nArj$cU<Xc9=5akMIX!h
zO!tcpwtQGdHtH3acjH$Ib9SB*7wqR*rKh5&zIwIEn~B0wA3b*=Xxgx7-Y!dG{>Sm(
z>_Q<qf1<4#m0;Y&+>++5ullnH<M)rfU7OpkHJt~5sdk9*Ovyu?ez}#r*0s;qdQNl>
z%oF-fEv(hiDlq(+y)v#kohH)z^bX;Wt0?+&-^z1j3Q~=H*D7revoAod(_Kbh<6E-C
zFFBhd!`IhCD^&^L(HvHfFP^r1FHlb)Z=DqRly?D}oTWEBemSx<UdY$ht3Heq^JAEN
znHQwL6#nz@I!XLpPEXqd?(;)Bn+|Qh7ZxjR?V81L;}KZD`OdJw@9nU~hS=}0zq9Zd
z@Ov$N#596MHHQM~Z4f8Dq71Xe@y(UasL=YhD083sX-gz)QBi+rc6axSUM%E+tsue$
zGRi{5^n10DtJ+Zc=b7;f5$4ievV#r(GFeu8iAu)TytL`-a(2j}YB_CKe?rJ~-AbCy
zQ>F>i{%hcLVl*IFc}F<W9q|>WkNg4hf<+^)^glYMHWm=TF$G9l?ROezJv0ylWxt(Z
zqr(Do?aSk1WhG+J&BMRKIp$4$nsL&v0_d5&<bI%;)#-d!LtPjn_+YxeYOt-8Mr|V7
zaA2&x{;DsZJ~s-z<U&6Bj^e4dOmi^DtJbU0QxV6q(J(kX0BiEyhkyD><l(DVC8&s~
znfgSV)G^h5WG6y-g@PdxD%pRNt}x4R*#1FwI3{zq@~(I4NsYRiP^h6WZ*X&JIXnC$
zsy#<@K!k??!yGO<wrhmOjCZO1XdZr$Y$wBTCa}8N7%3QxBdpvvGH)*A(t<E?uCDo_
z_h~BfYXeaKMhY}*R)peW>zoe?I^~8@7yZbU`TbcTEw7)s<83^0|I&!-lkh0GJ*TRy
zgj>Je{(T<4+_d&O<G|IBWjSB?et_Ab)+l7vfVLVK#q=qi&padFP-jAGNhpTb;EPIc
zH5l5mrlR|TVf*$*_i%X*J!i7ji^-o-THAk39@5r0iZzq<{^ulKAJLT3wG+_spYUX~
z8mCaI5r@bsbHQ%?j-<~%BuHrZOL}CMH1`&dHW%xtTCle(xR>|4n8BMY_52b<=l1Jz
zzFdJ@Wyet+u})zS2EQVfLc#yRH(%EA118lP)W{3FX&Gq($4Kyt<R}&q;-aa(t;Z^W
zP|*(#^i7eohv<VNwMatF4Dzccq|&%R(xRZIYViHJ>LChhhYZwib!%v!npdsoty=wy
z<KAHD|Eq+=R2>@oN4u~v3RVdR`cbwv>k9WKtTQ<5W>Vd0v1;*lkNlPKbArgwk=BnS
z<?(`{BwHfbDY0#@qEWwZ5x4VQ1!F(DAnLW-yo=}2DyrdE&YmGcnZok$UKP(bZbnO;
z^Wj&C)_Q;y|EokWhR8`)AY_GpY@1y7fli&qA;Yih<PR6T>=cyD5I3UO2%KIi1-9`n
zV8SQ0ogQlD%9JtxX)kJjo=CdU#^=;JN{j|Os11_DmQ#!AfP87wcA)Z)Or)dk+d>Vn
z@o_<Q-Md`k{{bsY2bi~x>@N`AevoS$iASB(OZYO5%m@PT5gXHJ`HO-5Hnz!*&+KZ)
zDx*b;syAttgg$Ha^bEFT^ZU5YiVURU6HwojlZHO*r`eVaRV+3?Am&V})PJWGZMCR1
z!aC0tWDNC3nL>Vn>Mbvw<!q)?>@wRwu@<z4&|n3Je${=esgcu?u(AhZ#KWmPrZ0xO
zRC|{&VjdmTR588jZAyfL_YDgQCPG~QGf1g7W4>jn|3n*A#T|$QP;~#ru|9KBdYlLA
zb1C+5x#k2O6EZjMRjn#lD<-K9va$+|6m<fYPYY72B`2df!M^EoiYmeh?=M=I{y_jS
zA}nCFgSJ3U)+!ED%vVIrQ4rM(lVd*wL@o^Nuau2`L$qZdtZJ@=%@nc%y#O?ZGy%U-
zGJdlvZQw4=riW+vO*{?OS95N@Empv2YmU?iQG6feabqo)H$tY}?*E#osDF!C#5Aga
z1YvU&`s`KIn~mG#^h)+O{O99IXDzX%kYF>W{&1_k;z~zIo;bH=zwmr<8%00mhW;j}
zx}3=zd}$Mn<IZ-EW%ikkB^bxUxZ1FuH}q4J!AXGC0RBJz$2$)X#x{pp0zM!-Ie<Lf
zCC9|77!7TqW)$QFR2GtVxk3(Zhp`ELMRwRooj9rnMYkCpH_{Scr>^!fevxH1NlY(z
z{hpT*T}e-KO^N<8w71_2Fi?p`IlcVn_(A!yEX59W;hH!HJZ4kN*P@;Bt>F1<mDtF8
zF}PzG0(UG)>yQ}1pExAOyJ~+LB7`QN@~Nf{KWkzzd*`>`CJ*5Q=xZ1@H`Pa+X{GX*
zjeJzK26BJK75|0K5^6h8j~Q7~so7@pMyWjL{gPwu7Ew_gtXHdF#ryZ$C14U5hMc&F
z^{@GX@})a_uP68WTH6;Wz{RlfWr!b~iC-oM!OK|`_X~8!tT@a%^fDUnVdUIR_e3tT
zo_tIa&~NpVj4+VM+$Muz%Hp+g+*h95KIQA*0M1gG0#)&XjbwsxrQ}s-6>NNDvI@oe
zCS{krRtu9btu~jqyS)UZJb@xW8`cMJE$(}kYCy%K7jdIJ$_gFLSj%X~aV~CmY+HaV
z{E!3#oq=FkRmW{c3#W;yG|O5d+Ic1Vq?QI<(IX1GoKys8RQ;uY<moUJ?B$!3|J)bB
zs8U$Ort>Zp6mN#*L0-2dT9LLCOVgpgTaVKWTvtd^m86c!{f2@LF0BRe_TV6+1pmI|
zHA4=fl~^e8eqoP=&`0@abb!)ZHKMY+<Qr}yZX0nIB7%zY%ZL`e2liI~(?3zvQsUB`
zNJ$Kida^-o)rNdcmcS=q<W*PG`e3}nyCdOZ(y=<9ewh%QZgd!n?|O66e$n~Se<?ZS
zwFo8}zUoM!W%PqZ1i<nVOzkNFTS2U9rFQx8Bf~&s*ElkNM+Np}cryKg9-Ho-kqhJ^
z|IuWR>UjZo5J{`0Ma5Cwx8tP8q!K=9$|~Pk#Zq|^vgks~S!lwZkzf#B>>iY)``VZ^
z7U51qukOE36D<(V_TwUc><M_F8QbWj4N1OS_Lp7lOUd)?Lh+k9LHtkRHuaAp%QGz?
zATsU4?v-KN!1tqRkb5nT@q)X`r)o2s0A=R|tRI?53$v^NlnMekM*xquD(X?;84mdb
zG&!jqjRm1L6A8%%g-Ri={!Am^J9+fF<bZ{A&(Gr)KkUXm6%HijtcfygW1`526{H&y
zd)D-a%Wi@nwA;XSCYwp`Q^$yuLT{s19@WwB#Pvn4q%6cqE!dEw{E$DIeLeTaT{K62
zTKetaI;}Q4_0&b+It2ygJvZSa3G1=AvZ(Gt8Ka0>wtAFAY)k06V>bpIfY-ibRiWb;
z<V;1Po5p*G(D~-C8LIAXy1C+!6HnK1JU3<5gsJQ!X6gi|5?BDKp(%+jC`>z^hJ(01
zmG<_FARcuDRif;VeO^IVXI$<H+W%>EzSartOU|BoR&UyG!VF=S<Pd!0%V?Har0Njm
zE@5<DSTdR{9j)9iMojZcxI96d0`;z#oU;Q#>~+c$28A4)vO}?1SB0o}=^i2YSjfmq
z<^9SBpnBIxg=B^tO!w8>%=j}1f32gJ%tw+5XUu?$0L1Y5st$R7|L|`{)>MKLO9^?R
zs__71b9rJ16xdhOr?d<a4!7{B<x9BViX}#R6d~F53me-YF&+YZ^o^t>h9{A4oa*q>
zr(?oB*6!W6KWS3Y!ItG3?apMaKK&_GOr4^CKpZ?I-*e;yWe{ij(W^lzu#XOmlbv+0
zZ4mLmJ$!gJ?hgBxh9mTYtsMD1|2a?)-7W$D#?<erD$aQ<MfMXqXzA4x`PbwPuG`zz
zm^sc`2m~8Eo#JMu2PSJ<mUvWfBt)n>O8Fztj9Yfi$JqAD|F8$$4)+oO3I`b`FE39D
zfHE@>&;kfR^t|;9NC>gfNmkGR*Q=U1D{d!N;tWw!Ms-5rrbeD)y7IgYMXiTy#I9E8
F+)D9}`UU_1

literal 0
HcmV?d00001

diff --git a/public/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/small-s4-code-0.avif b/public/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/small-s4-code-0.avif
new file mode 100644
index 0000000000000000000000000000000000000000..0221680ba535d9f1a3e3d19f2c7bfc30ee3dbdfb
GIT binary patch
literal 10564
zcmYLPQ*<UuvyE-rww;MFv28n<*tTtJl6<l4WMbR4?c6!%UuWHV=-Rb+VRb$9Qv(43
z;h8ymI2gKGngRVaTT3(Mf3U5k;olh8!PeB-@E`uK5nGtpIQ|a;0XbM2JO79O^K6zj
z_QwANqJlsmK*0YX;N<^#ARtQXzuaG=`u>jr68jr+Sz6ltJNRD}{cm9UZ`$8#Lsur|
ze=7c;{Hy#&A~;w&+y0~e+pWgZ!O`I_j<j?zw*Twl3|*N;0>I$@(a|iO98CWKKtP~S
zKtLdJe*uz%rMu<-7^uJ8fBAU-P=7oBPXqo(;WBh_7x{;FakS&Hbucym_YmeWwlsF&
zaq@6-Hnnx)`I}>DV`=E<!DDFSVDaz1@GTu||6APOU4RCFfC7Pl1%rTx{_TZh>1g`D
zOaC4Bx7g<2b^X0EWvi`;p)(2)3=B4fg9m#d2o5|H%bJ`FpS!!6()8qS8H_P<`KKb^
z`$nR%tODwc?a94m!WD=GInn#l*))T94nM<O?A2nT!=f&8Om5KBP|puZFp_Rwmkd~Z
zoevS+4d_pK)QPs3m+22~pZ?dzG;w*0rC{dsRA(P=EU2`~BXGb}>>vIIIg*+=Ho92!
zd#iaikn&u<BmGx!?uG)QM@3S=i*FkGaf(WxHmPPyUBItid6u6;wk^6^r84{zxetb}
z1I}}hDuo(sLM>+`5_Yqx_Je+q1*^K}a2e^N!wGTz_N4CDW!ibBed~6Y(ECG?4q~C!
z8zExl7=X#BuS?Z|5Lv0k?xtqvkUR#NKWpI%oBZJFc_~YKc@B;;(HaC>$ZDP=&{ql=
zh5J%#uMR0FHj+2th_4|Wnf?Z(W(pyc3PI`_-S*}{lsE%)m|TuFYw3gS7UmOlzRe9V
zQG!;8*s=RjgXZqO{t=WGXFMsQN6Qq;(-5Kt@pNi631tvcglXCn?6jP0RnQgV7D7#T
zV<i_-Z%db}XnXK`o8)~O0l)d4kx%dP(0e`1Q#W&$kElc@dzdA|?{F=67vEO3Yh!pi
z+OIw$_EyJq`ok$0J}(kLW@{TDYPsEw4}D#8oi(#ssUy!|cYbxpOy!MGF!?-uV+QOP
zd8CuT!^Pu$#PVcX*TXTbn(2R8IhBGpHe|uM%Gw+y57c5am$#V&!W~2yvf|qn_AM`N
zv*0$v@b}qByiXV3dWo7jU)}@|3asP9`j{&OgwrT$CI1d@_8w}eRuQv@5LKHg$K|$Q
z=o49M;^#?JI1HDcIf)s3MoeJ8zL^T$i4_yI{{tJG4hPut{dSM_XgyUfp4m(&g}Y<|
z93s&632Fho+JB_iNQxK@yJ6V8++6_5H4+rP04}F7v{Ue5w;j(|4TU_rNKSAgN?*wR
z4hpwAI}%b}E~2>k8}l9|og}Uy>jy3mo!neZU`b7y8+ywH4*pOiGi?&CCw8<;Jh{bw
zo34Yd#3x8b*e`_%HiNX8Ul}fGNv*K1VeGLBi)~4DCgIeFSd?#RscLHSHOJ&zUt4g3
zQYv-l!(;n>h~egbVM6vw!ZnAoAlKw89K%B#!UyKJ%MU{J5%Z68{!%GLB7Bc>Zow7!
z1^##WCwXe9#TI?z0fGLHu4s%1mFhQBRaK3M(M>}yiA2%pkYzDxg3Ju(^!^hsw4$&;
zJ$(3FLsl72?3e+&t0r7E5b^nnt{4l3H^Ye@CVvpy%S&M^)mH3o3x$E5mwyl_DYsv*
z{`e}|h^ITJeyrPX-yukGrB&#7o)2xWLi=U7`i$5#au8eK6PJ>$f>RWo!u~*HewI?5
zSDjBx!w6|Pa=px?S%{U6z1(e^Wiu+Fl!?y9OUfyGq#c>WQ?b6r_H9K!r>B?gku25h
zR%g%5qfX|Pv6wXyI%ATEX#<qRQIt`GxvJC?G`Ut;N}x*e!<V9|9GiHJ1H=yIDFOX$
z#IZCJiLo$!DE{iXrI+{ccsroCbBU}WNsv01KM*4dFNebBP_>f#7_@Xei(LxnwQ|VQ
zsaylA(&THHaL9`QL6pB(LT;gJco-yOfU_<na`Rv}H$^}TfT-NQW&0pr5ku(*<r!iZ
zewCsD)MJ-a_bZ;v<ui=wsn#ULWCqeAu2CX{U9IN<qF{AGry3Tb{*Q+b1ol%q*z$5F
zyu6+ifzuFQ@VD`q12qx{=|bqZ<p;NoQW}m%AXl!P!#8pn%g@2ekCg)<MGY4G)9QeL
z+njrBO2GWuW}qanX3E~DnM7g0wC#eH#CjviHqMS^p#PR1!||~>tp3Cpg>m;_Kz>Z&
z(kFK6lo-L9i*tVGLzV7Y)!x7@4oMX1F9!?rbU7xC0F}1g)3@)KKHGb)ID&fQj%Z_$
zFw?0sr04RN5EY{tRh4oZ&jpfiJ88H0=P|*89xgvNs!laJvM!aW14Lh6ycgYj$>rwK
zoy^+=EgR6w%wyY2Ekq{#{6S9|7)F+zNqE!w6DIlt^ES-dKt(p^ZWFuW3{}Z=ZI?!6
za=8Pg8_<Y)Pfe~X)u?a>WQ{pBAy+(jGo7~FK?%wU7<`542JbHrm9_}KYwm(yeu%hK
zZZCnUcX`TtLg-C=7d##lj%V=qcMMs>dghOCQ*n|fBU11(d``__U%-t)JA4Kg1nLcz
zQNcm@$)^p_9(KZlyTvTnVao@u`o7=6;9G-A#*fY2)f2EC@C>Z(9QSaDJSSg6$G9OZ
z2!+Z%P8=GzbG?dc8AX9hxEHY<FO--I_iI?ie3`MSSfV8Ro54F``={0;%Y#l=9dX3B
zvF6jatxF7Q1KHl&O2S2~I?Z5|@r_2oij2Ova%h^U0%|e_-zY<pB#MT~UP~P~w3UZ^
zunUPVQ_AA8lJhkj1oTF=>(9~r!*Z6V<9Kg;*CH$4yF<`Bkvw2Jcv+v(Qk-`joP8S_
zRzd8+Dw`TrICG*Z_8DlX5_|Lv_FfJ<Lt8vNCIQD5bX^qAf<Rzb(Dy#GjHCi4Wfj%h
zvVLpt2z<sA1p=165p?&hJgqICtCz+Q*w9j;XqsBHGKBYjz8@`w_+2M@p8l&7mKKa&
zTIM?*-}<dZflZpfmuG@b&BYeCU(&|%>Swto+u)_y*Zf98kW5JDyO{XqrM9-YE+?^d
zG_qI1=w8)+%mMO2LFv)B1P|9ro=No7W2UkQ%xmlCHY&RKp3oy&aPtbIqW{Q>14}}7
z-#(wXRCc(0u0>su?WBfsXS5Wxag4ntT16tq2Mrn*TD=>vy;mzB(tQg9ej}uKy;2D1
z=66^btY~t!0dl*P07q*D3ZR@&lZZNs;`Ff25S4@>!{;o3!=L2lvg4iikX7{HV!z)S
zaSfLT+Jvk9!-&EsFxPh}y~$611zE=^K3NL2flSIx?-<nCQg3PicgO#<?OCFF!7!}3
z_&Ji{a<q!~1aas)QkGzF3w-i1)iU7%y%-oFVelE5_TBm-LNM`hn{4!lMejE`5U#-c
zWhCwh7OvBb3e6p12m$(zqR-rrlaMrH*3=~B@YDPUWTa?inid>(JS~l~B7-sZ+hTmT
zMui=4gRzXY@N1O|(p|+H1@!rI95FM`)eC@5sv#fa)dieEqEFoqT$TFh)wKmi-tx?G
zihCRx!mRL}FH#zRdd9K4MFx)knC*w8g2|`u1`EDy(-D&=zrct~sa>Afu(IU#9?~H1
ziRA=78rRK<A?bFfNB5Pw({CAR=R&|#2wXI7difkFohKMz$kvFRG*}w56-M`TagNzu
z5eXvP7QK0YSb&>?AW4G=U0EAHP*^x5t*UBK%Tr2X;HU(q$|LszU%Y$DHYeN*>)HxC
zEai9=`dWLWevcI*rjJ?Yg;XX1A&ltkZafQf?R-5isqTx84FM&-+TU!&?0I!5Osyuk
zcuTltU+h#xMW4kQwaPTyAYFl@+O;`I;t#cFqE<uwmrv^E%VO7HCMsgHe|DLI2Uu=@
z>oUX`@KnNUF_I{(Rl0h|9%sxY^6O8mT3q4x#YSN!8!|hl`whX-8K(HcjkVk}BlE4-
zlEY&~O0->H9*#jlZe<r+(cTiT1MuFYJ0_iW7rM<&Gor^SaCS*7mx6bshJrq$VG1m{
zEcs-M+7pf6>u~SADh+1zS~E9;=lg}y<y5*_#*b?UIpq6AW$i774ul(-4&7)9^LIxE
z3Od_}-Xs)wKr#3qZeREoa>09u?2kodCA(bh-*gq=*`keEU%?`8)ri5mtQ}glel4qw
zKjicK^+sYh#aC{xph}RrtIN%uTnZ1s!oIQAksm#UK#RY-P?Cwu49}=cwi-rJ12NfQ
zkB2|R0KDyJj`bLq;@!1Rlq<jxNcmMmdN&-xZxGms2rkd5ZW^JS!67$Vi>iS2fD~x1
zhBN6~@_rGVIw1YiXf!WVFWtecwMLKn3Jcg|S3-8VUZJN1E43iY%Tv!5qou-l6mmX@
zfSL;~A#397z1seU%8b11Hg%#m#UKn$)<KH_6W}*n3@~)W$r@n3&D{dN)?zfA=;V^l
zS$Q@jF#p=KZ+tARNlrhkRt$39kCft2wbJi-*w?cw72W7qhn!{H{TlIo(J<%HS2>WY
zPWB+4JSFYJbshM92PNOFKv%*uD-`R{g-M?Q=P`?v9W^Wh<4Uz{$RFm9^JrI*jC``j
zl<3DxsPob%DbvzZ;7%8vS7ul-{+Y_wRPxHBVkME2jYnzx*e6;--%wGgEsK1M(Rrix
zV>DToX?0UZ;~0UtyHcd<&CwACic^FhiL2FvBtK`}vucSN(yPo0PV|%q>^gR70y*bT
z|M=IWx$muey`|?mWbO`YR)ATt<2?CZqP~H(%%$VP>B+VCg3kmqye13lOQgc}ENN5&
zmZd=l>9a#h3?gxEKtij^vS5|@Gu(YwuTD8>GO}DsB#&p@i*kt_KQh!PLf6AKg=+DX
zz7EF@{Y<tF@N^ye(u1)*+hWsv%znlnj3Ju*8hY+`jQd)q5N<B9=nCumO=YLX;KjZ5
zsTrh`SVz=`>L>Gdkj#Yz>!OnwT>XewDGuJ%F8k@p{d32Uu-&cGs-i4qkaMxvpe3uR
zM$3Q+E3bJ<EYfM+^SqRuSYxxM^t96!u^C>!(maN3>#)(zx)SI<4X0jc80S*TKWa#b
zbqddWR$Dl9dx#@4UY2Z{+Grfb!Gz4l2g9r(e+AGnu_LpNN_Pz(s07}<>a1WOKN|SM
zSu>%A3@-be@cr%8`6S*=VoaCt>hHVQXq%O}tieQ6UG^0NJRqNS9=MKaE=P8-OVg$c
zH)V=kHU=Wy;DvvG2I9*D*MbJLb-p~U2%YJ1HzphidmPHkHAzGc2c{HcU(-5O3#R38
z8|$4d-bA1tBylFrJ?m&1o`Y=*KgeEIV<#+*@^N&@-`j2`(k|it+|tUYERL0<aEvdk
z0Oa0^doihA%D4TCDU%~L7dYh(Nn(A-G?0UdH%f#ZVV=?H&N4tmz~`=*4g;@om$6TF
zsd{Jx^M#Zu0jkoviAeyp9IrdE=_(OO;eu?+M{U2$8zPPe>C3{j;}M>{n@55l<Z?*g
z>yTi1c+v<~#}{e(_a_TAt-<j%tfi>g^@j;BT9-?jpNii51-$RWGPP-T_>f)bnzi-g
z`Pd*>%MClDohrdOUuI{J7UU+*RmLh_WATp%Y*y;WP4AtB^W+oTC4aK(7%d+oLypYU
zuebLf(G2y;P%T}>E1u|YkFMeJl^(fad(on?zBdOo4iSVom;E`@6tdUnJFgO7F3oF@
zRV;EGEZ^4XcC0(yongiUN?dFiFyzf^sFW-;OVd)r(%b~Ucf1FG=sOo0=ALSJYBpY!
z(<xyT9rcsQV=N%DiVv(3WXk7-Q}6D@gOH|3qMd0{3bG4L><x?cga~bV=WM}ay@uQT
zh~4s#o3upRg6BX1!9R%AmgbOJtIcN0l>%dQ6`#g`;k0>|#Dxyxao2Hat{vnvKR|pc
zK|kwzI9{3UsG^f0!b&Qtse66eR;4I0Ao@6xxT}L}waJ?oGUAuBzjb{)F^+p%b~%B&
zgp^+$JbKE+9@B}p0G@_(kmE9cH8(L|R7q&-Tgrr10;`p}TI#1OidZ<3arEaMSwk;;
z?5ZgZvMv0gEv-@#!iK6jQQ+mWwL-Fyu~B2aDIDS($}!mv&yb+B@X(QMZMNBJ*bL%f
zx~EfW%8epGL<>P@t=1MjlF-q+Qonzb6vk5y5y0OF+Dfoz*-vU_XFxokEPxdyUb+~Q
zt5BayWW8;(8X;3gB>yuTQqM7}r=31fSm1Jo^aDO07`_cl$Uv9C1jIQ)#9G|<>}JBB
z9JBaBT2I+Kib;sSC=a7K-<xhOAfp#O-g<oYp(~CLy_vEJqd|M~enXja_+;ye`CXN7
zEnw1Ee1=gHaZtoVvol2*hR=Q3FifQihDTX^884);cYkp@T_8;^!5Tb<7AZ(Oyw<sf
zh<P6(CC45TSv2PxcmTS+Lj4=}IrL^Fp8;v21Q_ZNWzr9$NWWq<02a}FeGO5zV}MeV
zW&I#kkabx0U^yAY0uk3fic+m$$;}25^(q!39F|PvN4*>N&7Suqew1Pb=tF`<A;8n+
z=gmsL1Ez5{RR%D_#i9{)@wm1j9CW<m{xir<)|~$t@lU+qIB=lTAdtCnx?ad=D33YC
zF97E!KDOiq$j1eg>zD~-sXX&`=mhO!!juYTcA#0Mm4<+LwPseQp6I$;@0U@4qJ8NQ
zr^w5#4e1t@m*KFXF&wAZz3KSIV1#MJ!-PlW=rFYIR9-v<f(rYueS!FRlkPtwL9Hi%
z#L{{lru81+b1~!ZEKlY@4m_!z&gUL;a(#h><5P^0NXO!!^zZ^J@ocgtUq;qJZr<3(
zf#DZ}816-ucqkZUoY#T$<CYJQq*~>wss<EYgmLnx+lIb~!jchFkp7R+GF**V9IEV(
ziC>^6%pAt`-Aznn-!JZOg!?WZRR|le2Xt{rZcEwiL2mGD$&3&fOB6o38A6e>n^Vj+
z0I5!`9X+KOu?B&x4an*D4Db!91@9w$yTLf-4EO_r;mlYb;PNH5q=T~8i@cd2YG`of
zrvj3hDEozu&1(d*v&`yaJ5sRE$OCTyB3XJ$<!oIqk@Sep;Xp~^IJ#7Kx_QYFlSYQo
zz#(GairiZLOi+;_2ugXtsS`)+pclE7mbDH2+Cp;Qy+T72yu})=APdwuC~+^5aaUHF
zbco5YVvO)T+ZO}%xLtmt3RRLsg9J;Fp0m-65{OC$`x_MvhWxPu*P7E$+O`4S?2|^@
zm7%_w$Jo9S`&&WQBQ-orUeV4h`d8g%J+#eA?J2P@g{{8G$xQARA!=PYK{QDx;O7vr
zuuu2c8LC63DI3{ZXpv~s+NuKV!(h-h<U){G6Fg$w+?#Heh|+L2AWPdGT5Fg#$(Z*`
zs4^XY)o{-|PUwL_Wb|&xCuKKNDh7M46`m%lbw-6jW40>f9l>x>FRKOV<;_5T4UyP<
zjNDya-9elD$urPKrp(DsGQdOTx_ltZZT{A(N2`NH)Ac4YUXX7iGcNIcfAIb+OzSs&
z^&OAY^=tqU(Roal%%Og?Fx7Qgo440^z}{C}1^m-~a%6~R^^TF?v?gQpN^?Nnc0^Gd
zn0y0nZ+jNQl>3Qnnra~~b!Jh-*U=0Ys>o>4nbF`yB|aChP<K&qpWqgK_~tdEM!nG=
zQBb3(r7?al4f-*!TMerdvJ*C_b$5D4D8nzunKT`7gxpaRaK~lod+)2cYy7w76E`t=
zY$1>~6J4~&wp=BUiBvE4o6`dc*at1RS)SrROB1ePrpm_lOr!M|kiNKJq}_`;p+7Ov
zA6@~a4swlM;hi!b;(6snb|w2v5RfEP#NUdgO6V+k4+EY$Wd%b8s)p9={d2K6rd5Pr
zTLLu>TGCJ|>E31)Kp2n)$v#m(x-eaFd(p9*MAGTxVEOqH+WU4&eOq7SF#YRc+^2<s
zX=g;Vb}hB(HxRpm9>QZl8yA07(m)czMpDDrf@2Hi!@PLRDIl$+q9o6v@}e}HTjA^F
zGWP&)mR{u4p~m3BJj4L!&jW+uL`0ECyKHrGf`@i5s#zGz^hz$zx-)F3G`^<bLn7DS
zGB?Aow4SnP&BvMNK=+?m?6-T5BM^tZ=&;lOR3z~;SMQDc{@J3+fn265<`1%oK<Iy$
z^OC1>y@u6DV)3YD@~qE*mm<Iwl+rCvQ{gH~8C#_nhBx^O|GEj)j%#a7Bz<1)!h|jX
zE-mF|VVV04iG}&znxx`H0)FENk6`!8#zh7mwJqeq<?ey`bWX+HC3TZAiP0p~UORIZ
zO5!WdPoUu*+a>wJj>-gC-1g1k+~FeQJA=aU@`lYS+Pjs5^#m87pQM&1QJjME;%p0|
z84+&rs1zDXl6J_Io~}xHqeu-+_?gO_6j_~nGpdQ0`3X9sfji(BMc_gF!!kTb?VWyh
zr1QBoqMDA==eOfPmY+;*U>jH8lMU(ylBSzmdRxqIuf!A>-7_wDQbC(;xpXLD<VHcw
zuOAq^MlLmaY6pl#)U1uSDXeH@ofwt-v;}FOaktnKQPI|4Zv0iET+a%2pyiye>ajTD
z{R5<-d-D>F>b~Jx<pRHl!^a1iOhaSaG#pdyE8~8@)GFY3x7&VtXOi`v*MH}&B0V;%
z)LP0Uh@Q1_i;u349v(w>A=Bi$+Tp-36F8w@FRZI+O5W`dGF!R){O-Db(Yf|a_dXVF
zl;G=s;>$=^@?#lJA_e_XV)1+00r46NhggC$A*rJzB&=q-5-gaPc|Gww@w3HpTQk=Z
z_`21WPG$6z`;`X(pmAUC3@V#YGW}G9{Orw<_ZGX<xu+zPh)=|hyJ-%>SjtTG`nI$D
zQ#6>1usK@Md5;|E4Y?}S@+uQs*bVZV)$fofUn*)Whd4{V*we4XmO7{_2cbpdT2?56
zs<mfW_5?w=V#adeRXkB~W^g@DEVd{0X`LzsM0%)BMUpi}8ohcoXLg;0)@0aOC|Qf4
zKJYni<uWt*QA8cx_mvk*R7D@8M*)92shzN2GoPh}(h*u7`BldBtnX|$HZax3`|;$~
z<_A2Y{lt`@Wf6{VrTaL?=XKGl%A&x7cW0^wTSGT*mMJacdH(BM@O=;l2-2{orM3f2
z*y&EGU>5qgQ8X)9Omu50d{zTV`}b5yW;G26LpJD_VEc_AV{}UTLO<GLeDI)z8`;X?
zWb6=vcZ;)EC*V|FC0X^%u%Dd6?L(?V99^AXFg?m(*^oe9zv%5D{DDY4+ot984uaGr
z$F)bp|CM1M-TlacbDmG(1bB_9>8?Tp`n{S9;TP}|F#9}5^?5~@5e1TX6B6Qimv+G&
zoe3fwWSPQ^<Kb3+#|tyfP+@e4Yoxgnf)aM)vlEk;LUDpXHIK&nN*!s)Ws}=Nmt|j|
zm8)_aQ*d;5)XEenEoh7dcd&e_5Ck+KQSk}erA%V+RJ^h1{SC0{;*;!F!rVB40UL>A
z5sl45jld+oHkk%KV=*54X1@yfobe*RWA$JfP+7+qUaTw$ooIwb0Gndf^a&`Ul~Xix
zoF!od9(9FENr}P74YU-CuRKq0svBE)%z$%FrrG+dlYI#<>ozHFJo%PT9Li3e247*}
zh&nANe0udCf}`<W^&!d7&a)p-*6=qpH=tj(%PiA08b306%zv&*xz%HC#43WWd_pW_
z13HJ@p5bG6Ie7HblA?d%-pCV{M)dX<zDSuI=aHNzu6kIF==yv+kx}FKO(gKZv6|B^
zVN+&7r#<DnZgCZN<4+Yi*u|x)XUFZniznK|m3DG9P~?T8{&r{#XsSoRt|cud=3t99
z%U@-U!7kwgf}T46HF4HkFVN#vo-}dh`JO>0K(bB3>gS<K9z+A`u<FJ19--_N2o08g
zd_9eTty+lFMd}vRP_bb{MH=Wf9#vrhDP{2s^`Jm~|F?6iNstUp7XzE(tz)#VH>gM)
z*=v^(ZouV78YJ=nXJW|9A$3p)%pvMe8Z#1HYJG%wu1+Oh)ShB!SL=h&;;J27IXf9B
z`RMD0Zi@Gl*zF$t5hgygw`>x>;bp3up3{O%BbvS9ox|>lH`IaTsMB<%_ulv~+TDxv
zZtp$>$1DmDb783o_m25|{!4;iHba@G9o<ls;2TiP<v(wkpM?-Vf6{I*48QZDLw5+%
zq)xbH?FB*>*)1HdZ;ToFA8Has%|)D}o|r{&trWtq)aiqb<&wT379v#Ga-yg$gb>fs
zeSt;Qn3k_W7-oQhMz^1y8iGHrpJs?)gO(#yhtij8%S<lqDUK?6<`xe~G($N3z8zO@
zTeYa0-PRdj-c;O3b{9!A*hHBEn4rP+?i|k;&sF!h`c~l1S+M(HuvrV&R`?b9^^rLe
z)kZ$3M7!rBvDPVPg%)uHtzv}fUR}u$T<Dmb>g+O3@<TRcZs19(Kz#EQx5FqN5S_g`
zrI7d@!s&cX6s7+m7F|-QRwW=@U~EUa15>z$oJlMGCWRTi`Tao^FrH{ZFn0etp2^P+
z5yj1gs;D%%kJ!+-VmRPgw=8LvQgdY%5B>uOE_a`+-+ilHtg)b=TkpDzJp=|^UMDRb
z2hX3noeH|D1Cwu&96v)dsice%xmFA&rXntp@z+ee{r3ulFDO$ZF=He%{@dzH&7->N
z_YG$ZG7@{Fts2HJIR@#&P$1s7Z4T5U`!%B;NV)7ScH=KRMub|Kw2uj7v_Vb@gmklO
zHd5(#2&pv~5Q6-egvJCAe8=V;r@iBlQ2B0u9(9)d0hP~KtB(VJ)U<ah$GQV`ZZG{V
zosceuf;8D;_z5iYHrbxnf%DMr>M4Lt2fcbE-fQWgU)ztWh5p0jC7Su^NVGSzv)3@K
zJ1u0S7izCW<DhFnBbhS-gb5Xm*qN(e`S#P?DDb$aK9@H9nN5F2dvRVaM0c9F2e)@D
zF1EtKeMYh4g5fM^7$lFu1t7ZRQ({NERs4CHct~gvDLQtac%){3`l}-B5!s4rb#MT9
zYz`-4UX^D^zhiAn>>OsC_8-Z|0Ws_(G&9?Y5V7fuoDBX{Wvon-^z8!irHu11Niv&F
zUPOt9quuG<8(YEaB+NkPcv9E3b|XCetIxrFDO^b?*$H~<Cq{aHA)+AyZ}`za_3)vd
zYfpdFQTJ!}x3I~_x(z0`IS3(i0Za;uw4REeM+**;S}=Z3EqqX~qF^sd(`;xJT1<XD
zyarN#pYW+G^#dKd8r^5a>kinmFvdN02;gO)w8X#Yh>k*$r&dq)6=i5NB*i<qP@poS
z{DP?J+;dnNIM1U1Pil-tdhl;x^;@r08Lvucb{FnozV!_C=4IxlGuq`O9EBTq&h!mF
z!xe<-Cal<<lOUAwjJUdvzb!vnBKwJcFqTGNsjUy3WDO{Z7X9JjeVajkx{P)dA*C`^
zO4C%X+~vgc5FOWyttXh%LY)Hq(+VqPB_0)bnJv59`_HbIrD1qpHMoNrDC7*lCixCi
z4i;fkaN3xBZ)&3OgqyvG`V=wd)dtLh4x(c@c?a^ApRKq4J-HTPG{U`^&3yIMU1B3K
zp6r7J7aJ2-LPS|@-*G__U=wu7jv+tDUWh1^Yc%3RQf+OvJVvi(mklY1Nr@STur%jX
zPQfU62*kQMMg78uV&o<D{NmMx$uXSxYHhs6eD5FaGGSxfD9n3T`>YrRk{Gf_*Mi=!
z@Au6t9k5fOrw%iiU4M(Kylme*=u+OLe*tlTF06iGCM)_LmuE(xp9+5`hwhEWGnVS_
z-t3^fBp$3k%Wc6ena}5qLd{Bwsa2>mv<PRQ^BQ#=leBsbK>7ych_qzaJl<{XFP2|y
zpEET;7TMGGO*m`boQ_RRKJZ8+kUjk3cSN)V==b}zEVdYi5s<8#p+FMZZ0$a$*dTz~
zjj~m$McsCZQnP&Gka9$tv4Ro$Tzfu!0tkSHphF}MWFa~myl8uOR=OBoZu@e40wMyj
zwOCcB^@-lerO6$oni;iXTi&aCVs=L?^vnmq)*Z-?Fm7k~)&9g@f#@x!O0lDRv5_$2
z;Ipfw{KjwoEm^3O#2{1<n;9EIXOWH!IG|=y`QY`ywD4zv&9tv`3}on5^kDRheb8uJ
zly{f7g&j$OQZ`tgSak=nO@4{;RA#A$@Ma=TSV(y7YM8$5rZM4-UMN1eZs-KaxH<}c
z!&dtKWD(6|a~*%*w4!>@&__rbvX?Lu?CryK*)HX1d6KBMP=!(FOMSjF?RZcSK#*_x
z3Vj*s^AaOE9V6gA`pHCB8T=5dd4AiUfncM?B-V*bTcE7d_45cnQGY>}pE8r`W(h-_
z^ZE$;>?K$^GrHvuWu+MfT$inS`UZuU;P#XR_G>C<{m_Or;Woi*n4HSPIs1#+OZB;|
zeZv{{TPW8qK`k<4-TEd(6@$4!t*%^spcl;!R)y;irjvJU?%q#>5|U6(!n;qmsG@A#
zl6jmZNhOx=hsf(cV%U;LwVnz!vYvR-33LWL54H&+{Wf*{9D97&l_H#?F=OPQs%(kL
z`{4!*oRqw@P$x4SEvC5Jxp~+uJ4(Fg>*wWqwX-p2VLgWn40YKhRH212ocD0m9cys~
z!<Wvh0fh}bB2^S(Bk=j>blD3K+)f?m?SWQ-s|XcX(?_19YJJb^(xT(yua!zo7a8&>
zZV<Rr=Vn6G*<-_lxwK+E80><{q?rxjM<q-G#D=!d;)IJsJA*Y@qv$Nj^9}l`Vyzq4
zzuAhE%zY;a603Tr8sRdyn0JwQm%PfWyEJqLI{CDEgy=y&25dO&vIW>ledK4&pw*Hx
z?$S0p&LtsLts5PfXAq_eI<U^LuEde-Zg|xR3kCorRdG2rOjja>J*(J;^q3$u9xvm_
z9FhAGfmx3v>tf#=<Vc)liOJD|EZg94Xc}z9QW(2a332%&S%;aZdFlXC!RHCHnv5{u
zexd{$?-Ff}qMH}F%}G^fR5ENi=3n^w6inGUgZP~b`s468+o6>4M*K-_{^4SRUly8$
z#@qPozs`9ZGjK?p`RKIibNZbZFM5yx{o<Z_1O*J_FWRKI@<321SniarD>D2<NS0UZ
z<#$So_@6o%HCNR{&@*14ZJ~kCOa<s6U%Ah@u3gV=GL8XFaaeM0XmFAz3_gD%8S@O6
z-x+W-y~=iM0cm5f=Ju>4qDH+ny{$8`-d_>04h<^NEAgL^vcY#LfmVGfp0P?Y1LF;!
zI&+W0I2$GkdcL1z_WE<xj(t0XY{gdc_^xQl4Sl!OEZ3uNLBq4!iMa27aF~Ht;nK^h
zbo2@izL%+^?#w@u261CIfYGd9)i!aV0D%7fPSayZJQ2TF57kPvvBKsgUd<$Zv)*n2
z>-zpQDG632We!W*migg_>IBgG86N}Om-GyNz)~tRvsHe9#|K)?`Z^=*<ZgT_IO0XA
zq^We`Von2w1lr1YOUI%4<HntWDt_Q5kI6S=xW>q~K#e*-zr?5kgYv*E`~x}6pI_}W
zof!Gtf?M%sx~eLfFycm#SOx(7aJ|~wEKxLes<kJ^mM!km{XQXI^Xdf<JlAu*wFZim
zNlv<$$jlImUAx8jSwPMp$B$lqueP@uhWej5i1h8hM4#m`tgG=5M-kOwfWrAIL|a~+
z<p|?GGIb^JNYEHjnl8z-nN1{8CG!gwWm+yKd#y^w4574R1e+J@1tAEC4#b5MqHa!a
z--<53^|UXT5ZC#9XHOEh9zo$|0d=J(RHa<JL@R8ltCLHEMyn1K)reQ_OpKyr`5qh<
zpE1p@QkFqTANcR4Wm4B^&0c%b9$ay?a#4l$S>P0KevlW>lN}AnlxCMvdzaS|!)rDA
zPNy^IezM(bkp1@&G<|Ds^(k5*cMx3rxW>j@=Kg=b+Cl#F)s8bnkcA6r1_Vm*q4N<=
z5~^<Yr`inAG9Z(S){atbciDm~A7^XNc-~&wm9uldlMTM5vbwYS`IyO~`8U-60fpZ*
A@&Et;

literal 0
HcmV?d00001

diff --git a/public/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/small-s4-code-1.avif b/public/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/small-s4-code-1.avif
new file mode 100644
index 0000000000000000000000000000000000000000..56548bc6df1724ee80dc4a13928edeef1f744a46
GIT binary patch
literal 21467
zcmYJZQ;=xU&NbS$ZQHhcw{6?DZQHhOyLa2RZ5#hS=ewtF9#%%iNHS~YWmT;e0000S
zQ)dr*16K=EfPZFVVaoU)Y-3^YF9x)?F>yBd5C6{y&5f-c{|^EH*jpGm{}2DqvshT$
z8T}{tBLDyd0QesSnEZbp06-D^ANS9cvi`>a2>c5<EG%sQd+>i%)PI4=f7AX|8@Mts
z{-@&qC;ut`BjM~VoNfN2{<mA5g}tNwKOAXcZ)Eq+!x*?S3I~9|{6|NzaI!b~4*&oF
zdi<Au@ed%_Tew^NhXICwfcWSBzkHnkQ2#prp9c6Jg~PzbUHCt=i=!=<jlGH4e;-0z
zMixf)TuvTN&L%caT>o+`tSt;2J-7_4?alu?FI)?IoBs{&-z<OwfPevjK!SllL;v-{
zvT!u{zf1o+?q9L>f5-Lj%8GI}#s<zv08mhvWc2h>e?VBU5SX5e0Ht}{-Axr|r^}?#
zCP?L89zhj(!v0i}Kr<Mb(Zh$V4H#Tx$JS!f$n(FUq9N^h0x&SF%#z)5%#mY#M6TWO
zvt{WaY}*C$TsnB7WdkbpJ~BK8s@xnfTU7M3D#$)vKw()?i+1<Cn##(*v`!ItI50j=
z2R*V1XTcJ&TjP+_waA@xHBtgteY}=oHLUC7cP;}L?3d|kkl_Tank6NJOnQCBcMau?
ziKbd-mrut6+j55MXt5W1T8|InhMp{lQ*Ozd5F|n0T@bv#t0h(1g&$tprGb=h<)^Eb
zGmlw~rTCL$(P}KMYLHjtpnJBj?mMvq?hIa9%hb=q-`oR5N7{M0Udb!K)eenS1>l?P
zt>&^J1SGFNmp?U&8wbX41G>px>>&`Nhjt1<h$?2<l=kFv!2z!$+ki=13de<@l?*Ly
z1Hp%R#>SBN2#6F?lgw0J&2**PIC_o?I)i7C>hZi#C5?tl?3osWR~s5|Z$KXoSQ@M?
z<Mx_928~+GuyZt%2&^j=;}!>u>{p#AY*h7)wnXna<Qiz*-l)IwsOa^PrMIKOnERSC
z(AQHT%EYr=(0eL@`=J#}n_{>A8MG*=I84UAO3~rU6$dO}b+P4cM$xFl@+<K1tK4mK
znB;4J*xTCw5b70poo_7Y$=fuKfV|OVLjfQ-5q;8&>4UjI5!<!xs9;Oc%?<-h{Da&u
z+WU^9PIJo6<&nBGFq3QxfcB$YsI>X#=WwEhrFQVg*_tAsC7>K>ikk3Q(MzFEwZjW&
znC?46a6G0h6niI}zB2uGlJ-`42HDPF^s8Q=57aODV1<@zt%$u#2U;_fNJg`a5UA7P
z3yXb@8(<MOd(pe5TJJ&pm`90q3eJaXNs&?-Z?Qz*6(SJg>SsGabKR^QPpPcijaH9f
zAv}ZkGofeyl8an7Prv}j^IRv{@8w-SI6on&xewQHRRr3>f^FF0%8ez4q`=0qDA1w;
zhbk3CXHu*8Xri8#NbfQ>A9JudKBzakz2b(n@Y=FL*VHp9Ad8-Y2JD)I&_rCv3R*xV
zh|8_e&Pb&RLkD&>%Q6N|_Mxj^N?UOK1ZcT*_I&!Yya-ia0Sj^l5|AQ_-JIjVb(Wa(
z%qLut)PXHcicUE{?X@)@4V_gXW>54GZXpZ_r)uq92s4}a*6oQjKmZZ<C0*@8p4STe
zW|2kL;V`@&IO)`XJ5AdWJpA&Z7vGLQ%vBYIDVTTqd^J-NA)e^h0Z#1l;ZMQ21F7t7
zZygpx7|@1Y8eQA%QS9AoRWs1hUA%Z{E(`l`wWI`Zlqo7uW1~JwaxD<}lgc_^qy<4_
z{^4e?Wiik-gGVV99gr|$BKo(9llGSNLR-B+p3uS^A_iVvYl;D|LslrU?w?ViwH3D2
zO1zZ6@RD8iE~5=Ipb=L$c+4zEAF%Y9C>BP76)#c2yxC#BsT0#S2*k?C!qX4QFteEv
zM{~eX&KTuO?p@9cC#W6ZEm-d#N_jDJR}R$YfYWt9G}k>J5StUTeC6q*C^*l1`}9c-
zN)}nQqblEu-)hX6Y*4bsy&gN#(bdV*!Q2~94il$GXPb973$!H~V{4I8M_rw2*hH21
z*ZlP&qQ<yVt5172=N5MiZ~I3l;pv@C^l<op99+(dj+hfW-&$2HL9iZ4BxZmZE7}X-
z&o(+t@@E#6L^Fi2Fx?C?oSvOag05vxAm&^zHyUx|#~U&DD~j9|;~L6T_EcXC@{1|0
z=S0TQX^@PH-fRjSNpG^=&Z*D_=(>FUUDM6qP8)>`2yp6>i@UbfZiCAnO(5%`)acds
zJM`gYqyh7GDGIHAOo>UZn@wwIx|F@N6F(VcG)?>nr@J7_+fa$D(obBMO?;d(y6<EZ
zlA1&N`*f(}=e<K1o1AByIf#53HM$0``3`)DJqs6z!0ZDhiD?17CHmUoM;S3ZnydK2
zucy+%(R0Q3UTMsEqjfqy7}1xIR4O%X-FQc>c7WKvad&#jQ66|kk1Qf%EYzg>YwFVw
zy+ZyDy)$w{<&sW*7cZAh2YoZV4F&uHh-QHD1!-RehTlZ~9?T(9=1tbL>X6@4l_97M
z)=(6_<Wl6+u9cbo=|kr^8T?4Zgd}PKk~MR@uih8vr#hv?5TmT0d>o_x&Pt^lqcAL0
zIPY4V-2=r~SR*~6wnAot`s8Cr{FL@tf}mO9gL7AbwPA>1&&REn(_jeX_78^Ac%)if
zD^-M``u=7ZdTXG!I!{p%+=?Uo7GqEWufpMcc+#b_VY8MIxVLFt2i84f@_)Q`^d%wB
zA)75!`C*8jvIQ6@LMRL;9(lRdewT(Mn}jK$LhxU<<ij3tKvCN`<iwhUYishw4z8my
zHf``HXEVbs&=;Cxi2M-V6P|rKw%`Ge--`Xgdx!TvocYpc9v_)e-d)99y%lwj0}Caw
zSfVl5HdR7`cR29CQr=@j{?x+s{jQ#t_o|<0mjsTSc2-WanL)m5)XkE|qGF?*PhnpV
z9jF;qd9`J^;`9#Vzibd3pGQdin*CVNwwn)&*Z!v0+Kc=O3nq;PkTJlbvxQk4CYb*l
z9zK-drDxg>NaP;*mA=s?V7-u-#$q#n7!!x5dut0w3&7}kGMZ4OfCd9%S;BYj6(dwA
zA3nR_{AMS(oAHN#h5-k;!$MyX%*#tk=@LK#>l!u2HVf`J=a^QQ@~*=Cs`G(Jm79t;
z<<DV8oI$ST2soYSh6QPMJE*ssZZ$bxU2yQ;plcuQ5YpkI3X7q8?G#xEm%~#WN8i1&
zQ(39nth_lFVz3&4|44Fdgcs^XqK%a&Vy&FnDS5?w%!`RGPMEJo^riurc3DrvtgYCk
z=&W}#*I@5UXSn&GRm`N@R-Ga+NEGS0A)$$>mUcWCyEZ3*uAGqum}&tazqDj*g1!9;
zEfVnz&6cdia*SvuU>5vb+9!>G1h=s_LTj`8r?0wU<+<4`7}CNf&4W8QzJrdgEv&5$
zo2O#OmOdQ|pe#H!{GsvK9aKkL)g3qF<>ppx;vRgZ8Oaum@0kf&)3k^k^6Sw9p?Pdl
ztY~~ddLv0$iZSn$%i2!dbdXspDt*scbNi+M6T1@}bXb0bLB`emmb<^m_iiO2*9+Vu
zr#WQR-AyRUrU|d@&za|#+GI?|c2~+%?zN#j=A_#v@+r|!e;v?7#C1*%8<#=TC7KE)
z1jQ80tMMD6?@Es6r-R>OT9E)b{46sJY*uQ7GA4FgwzU)xgWVe-!SS66ckv6l1x#F_
z<+K9}9gOS!W)mRI&^q~dli3YTIQMM62712Q94~`E!DgI4m99s9%kXY@!TQgh2W|l%
zT`P~MZ{t%i&J8PI>!g~rDCap$Laj{O$0R8On6cPpz2C+HT{g0bO%w?f^jD-Op1fqI
zn@`8qNq3BNhjf$eGeb}9$H(wIvJ%!wqM+{Kc@SXJ5l$qeYVr!Tgbw@Xgt%$U=q+h7
zal$+kmxW)%;d?V;NyS7~Qh71NdI?HY{|B7(czby^qi<IP_)A5<Z|kS4VXoldjY!)~
zR{F2bTxOb|_|nvUL!9X-?qd{-_Wo>X!P9=Goz#w87&;%XNwt6$V;Oy`C-C3kDo#CO
z9YxJm3WsN|sm*&xHX4+IC=hO?<t<B$a~!{wzClvSSA>FhH=P}-+^%P4UnaA@6`j?B
zA&&O-(dXuRzyzQPd|BxN!&Ys<jFS#fhkLiX-6-DS*<TPm+7x0#b+PB64A#Wt+@7o1
zQ@M6k6V;0sh=9Uqp;XeCk!At+zF3_m{sxuJ4SI=ncSiR^49;v^$q^b?o3FZF?GF^~
zM~?>u4!5<29_YEkeky>3BvA4{<DUWB;%@Wf8H^L>5B|2DuYyf%$F~R{75aZv&7}Lz
zKKVJMUH__unZyXkEnh>1fw?1AYL_f;`FEZULVT$sHqR(Tldk4p2!IP@^&t6=r!T3Q
zqjkJSmOdp|Z&D#Py`(Hg-(JJw>fWWFY3zg1_0$4G_OB?~*xgf$x_X<t6>H6BQtF{j
z{^leIT*KVCUR3^25baPo-oSBK)*5na`M}K?_t_?Is>FoAA>O3sl=G2ll6e<td;T_y
zRhhwS+hQU7p2pe(%_rVp>Qv0107{joWFAbul1b3DH@J0!G7onvSPwXtZ<KpwQNPRH
zXei9yj0jq!VD_h72;h9G;TQXiPIp;|nX>@-<P1_W-kX|Kac5@qZ|H^zdI~UTu|c4e
z1s>!;P~tHW_h1LLy+RNF(A7iY+<b-BWB~_cm6)y!=b|V0SR_nh{gT#`^2svOn{8+<
z@S;mEmhW8G<aLcT31yy9X>h{qx#ApWIM?w`lTB@1r2;u#EGm+h3M?o=17DYCxq`|X
z-E-4VbFQf=sXSywLrRB}n-@o6aq)1;Ao;LMuyZN&#jh8LlT2kv`ztzg&pd?e=KxjX
zok3K^C1U%*tvk7+mm|UrCZvN&$Sv{RUJf~6fauvz`9oB_n>cC;1cT&z6;`5&z*{~H
z6&0jlEp{fLq=P4SM1w0ryQ?nGzD6Ym85Rr`aws3V4v9;4P;06Ha<!N33%!2SEd4$`
zR^QOYFdetA-EqctU{}@TLmqc&_=i6f`>udx<%9P7ap6llFF=<UhHm<u>-uz#^PqaS
zoNSm_ylyzCXxwzTWiqJQhVd$7#uOM;gi*x?0_Ft#QlCw3dQkW3<RRo~4i9mp(jy$t
zKD!r5_GAAooA#)p^o&hTa~ASr2M{mDW$OF&l(XMQ&B`T|$KRS4DM3yF!Y5a%x02#D
z@_k7Tc}k;Xad$~aU1_FM6C&cqzwez?9w8XT?rIO3hcld*2CJNml@r4@zTkzDg(eQS
z`-yj#B+Nw}Eyc(?<jQVuohM>N^X{bZ&=_16l4}(iWuXJo4Fi+XYCVmk7lfyjEKX|i
zTerZ{q^ra=lTrK0+1amee`EAIW=FE8vLcTkXsCY3b>|9s7shAXoOI2vTh|2RAeZ$P
zUE;VMi?ZQ^#ONFKkvZgy5FLL7Kt_)zdL|@3Kqv`MJdc{dsQa5dMgdo7(K`oBH;QFk
z7$%J2GIXD#BRmgRS8<C0sV#F-UuHFI|CLBxq**hNl`;GA^!$w}g*<xZwq%JoGe1;I
zVzM}5Ykt-0OZ^9If4;6<#1T@u!%eBfq#;A=%9Qogz_gzP3+KYqhG$f9*I2v4lsK|W
zCohkrmKf+o0#M>&BWnI^o6m{OG6Xbxr_f<QTvb!jeTbo}5-`C^JXe?ug~5sgP9Z27
z36XQzP#%w^b_G_$k&s+3IfkN9;m#*LR5}S7h5U>*6LB$Il`)nqKROw{Y=YpHMqem?
z!^J2uTG1sf*=Um52H-NhoyThjHpF8Mu1)dFjq)W+^kr!@k#W^t5LLd@4&Mg^_bsT4
zxJ0)xnvI?8YE?xbqjRBz+?3l&1n*oAO$hJln$Vp<c9v<~<jsVb4m{uIDQ-PGq@DY?
zB_Q@#Ta*RP@cL36(PL6*;Wcn5Wc$f2VAq_*EM~&44gwHo5%s<d3}32trE*Aph?oZ`
z=Nwacc!Ky+SV(=>Ik?DI%DO^{HW^Y-sI)Ti4>~l>p`@$l=VHmbW)3Dw`I$BmWz(bV
zK)Op&n4Zx+q<~xaV9s;r-R|84ku2P&y6W|+U&g^_P~x#!dWyx6{q+VBYizZ*_ez4p
zqRzBQbcd)nYaQ3iM7DvAxDetx1nXY|@45RI5WEd9?invITi+!sgL8PvyI-Xyj6Qn<
zL6L7wVA4K`!By^4=Rza<UIC5oTDJDxuT%A_mI$w=>=Oa0Ai-iC2iu~sbSMSs^LaGD
zsBU8(Y#rq%b^xBv=TF09P*JC{#0SEZ%0$8V=M#2e?wc-ST6`ikbkj<-#oa&0087H<
z#}PfBQWwp+)L=oWvec#}(P6K^+bKJRJ=e3XfyJ~HskS%`M9z4dwEpt%;BLaU3{!^X
zoi+urdTd>=fzbY6(2>+Q%&daWd2?it`w^?Y(jQcky#}X3p~Xt#4?!%VG3R`;aMZT=
zZe^%7V9gc)k}v8z>_c4_KG3P|W(`gtz#UOoGE~2##FINGCbBi^%&sm!(Qs#S2_wi{
zVG$kF9~lyK6*%CebY9F3G6oXp+Xm*(?I^!y_oMrn11<P-%S3hN6m8YXru?7RLlfz_
z0#nHa7IdINypeLZxo)OIqZ6Rkgie>fQIzOs?m-4l+*iZL;<wEk^@xe|fMkGCJY;{e
zgpp8_L(&?84Ndkw%H_qf;SmN%-7bQkwiQFrAKa|=b=<cOqBJoi66sK4MY5B^0-yYj
z`QCD3J^~3B)e1?|88zjQsVd_gP!7{L(ykBoM?Sfi3dT<XOnT8}lj?y{M6WSwLT<}x
ze340v5)ojfxe$x#T@kf;vl`si92jwrtS04rHMZvCo(Wo7zyD4q8gsgM%p*Nbq<g9s
z`=PQ1!$PDI1wf#OCx#k_7I7ll#Q6fr6uSyh%D+j{C?2kC$Et+hK5M=VtEB0MJLzmC
zLNa6iY+i;aiq!@@K|jINQn^(t*lEls`cgq|;vKCYwrsNdyE8&~Vb(^jbHAk1?mBQw
z8s0P~h&{-HQ^*;8=AhK+%;J>r2oi_cV4wYXVBF8IF$);6PW)w5JbX>aO-+PiQNFAr
z7G9U7Okt#KNi{SfUpRW{>}__B7HZ2l9BLgg+?j)6R&^Lb8er}G#!Tt#fn)2cV8Wl%
zG?b(A8xZY?zQriZWQ6Oq1V7`v|Mv!G?F-&vlA^#sr+TpxwniQtJ^bw`L+#EO7DhZ@
zl5RX;+mjGNKLp2@09mIm?DIjM652kgODz-Nfoiuq1!p2u$i@N@x3rh6Fajz#EZW~K
z7cCL*52uGSlxC*l;vd_i*K8VC`3lxQE;flC3$dR@lwps~UeDa_o>x=(_AYcMFP^&f
zT907PbZkK0SWz*t`#N07ai#}z-6-Lz#$w-Rje~yjW?;-0fA~{=iyF@%PwoYLtIz<1
zJk_9L+q++vb<V%TEn+2!WjY8Wo8WU%gOh4&L(-N+Sv%zBM}(ma-;eDhzFrgRM!B#U
zIj;Z66BbANwrL0tl>*QnwsMMRH9HZw1I3Y!8+wnQzwZptG2}JbEvN~<8SGSA^Gwm)
zJJF(N8BSKIrm&F#d=&2=Mhk$S5!S2qWMQR)Qvt4>_Z!N2NJ4rtqNcLi5(*4gD;Arz
z_r1PC@3Jr5S5R((gebb$BLYit*qd))qR970o#iN_w;ThpZ(BnL5@3SD;*b?v{&kw+
zp7ZBx2A$|&Q#uQE6NCYPmIKTi28#1EZYH<Z0&x7@#{D?LAz$o1dr^f;BLsx<NRoyZ
z?a?3CmRnOk<AQ4jqiEKc>La)LylY{3OWr}mI<A+F@bqXn$W8sx4^Do)O!5UUM}gy5
zDdq4UWs591XJ1ZuX8u9n{7d3Z?UFx^63a6Kro?>U!RF>uioBJWyeT1{N{os=L8eTb
zq7H|RVVE>EGqQY6g+*iJCPsGsx#~d4X+{b+QL6|<ZY$kduyp}2R!9fg1FuPUfoFxz
zB(P;P20u`>NjKJI$McwJ9!%-xg@7e7`&m>c_jTJ_H65#4?rZf$u*ZG9y^%t%Mb*$}
zl|>5iEYL|<x2Xku`BzS0t<YUl<Xi?pt&Fy+7DGUNNS>h**p-6?`dTR-&3x6bHQ6ZP
zgK2EWQI|u)yBXeq>X<=T!xJIFcOTHh*cv2%6L=~VBpGRY<>GQ}la`=@0f*)-;GwP#
z%YWBwCE&gOOf%iy*qyqQt+!VBFF@{-c~3gq#a!G(USBA0AfoG;*lpI+0zx$y_DiM4
z%M)V|AQR1BJ;+`}9vND~qD3D|43YyurzrD>#X~5R^R2tM>J0$#hrT92lbr}MUL~NR
zg0%Xsw&nQ)56Q=av;1f;j&oFB2ngy>X-J5E#KVOv03CUI*)D#o*Fdq39aEF8s{?g)
zFD+DGp40MJm)$2c4Dma0>{?;_q1U?V*uxrKuAX-?N)Rf>;&h}StZ@K`b}7Vp!1F{%
zvP!TremBOv6@8xL%_W~z63|UvF#L9g67-|&2=hjr^Fp|i$3J*;9QDejtxy`VhTcMd
zKjP>0S~7s{NdO(v`jbET&=*CJ7D`hsgr^}`*&^ad`T`fr#3tVI;yCkWY`$kZMbHJN
zJxf8jiVx^U5*x&V!b^w6PZS6#M@aSiO@ti999HBT=2(T%2a8`LbDU-_Wph|<OGzT%
z2dvR#f-tW{NBBf-1w4W*0SP%mv9y`7CdoN{oX@Fzh<my0+A1g*#LAga8*DVmj=Y=j
zlda@Ujbnf|Gxpi?|LRMlfqctPNUV&9XOc$Bh<{+r?K#2J7i`(ERPtAEhDtfZQ-}aL
zbE7ki@H~RVGc-ZeW8_`l6H|qp5RkU>IE)l3N)!34PwyW`QG&a^Xu=^)pRLTj!G_yt
z{T;d#Qs%zNbvAO~S=<ZZG^&~!{kT!NIJHGNPNw`)Jh@(bw{oRJsOh@Cx?Ll52X!5+
z#H&7oedlLjD{L~$R9#)w_$I`{;PVNOHlT(;rzXDqE%hG@vRezEuBa&M+CTN`{|w*l
z-zef1n&~vBUAu0OYLl1E`5gcTg856pQmVkL*Nw6=%^MheO6;?{h&C3PQx*)a>9Rre
z4C`mVu5D4ghHX_4(WHMcjqPXGTN|~;K~Rwq!;sU@T%@CKQ-U#ihY1{{G(KY*;DFEM
zFJ^J{yxuP@OJk8wX$g0SP#B9@roHDk!%^?(egqS=x+Ua20je1BkwY7oFH=|TLI*V;
zt>z|d&uZ&(o|gKzZ!Ogj`CZghW-cbETH%V1$!-?Z7=m|7v`f$hM~PDM#q(v75~AuX
zAramq`M5sjx=C-M50%-PaJXR1Rw{`|5=Fn={?eK^5)wd1vDYC`js?b#r_DJh?dp~Q
zFUr1w#cADZ010G_QE>E}qUj#7nsrN0Ct~=|xjod4!OD0)bW9TEncAKM<e7~`BFVl(
zj7UA90@kezo6kY6d1MX!>Bp%*YQ)@yZukv4&MRYOml7r|Wy(5C1G<y=cTo6*Y~WS5
zM@^Ji?KvN9DpY;LeGe>6WaP5?xZGY|MoJgymjpJ;-RJV=+gVkyZkuhrNWL`oqzs9`
z05(Gx6Ql{eo81nXdB;JUh)F5gbDmCqFn(qEbz~0gmv!@em`1>aU6@yYg}V}S*(Q!@
zjQGs(_`SK*<c$SP@3(a)2qj6}Sc>E{dM#+v*&cuVistn=cNX>(RZO4Nea4O_x8T%Z
zWF+h10cGs5Owd7M_7s1)Gmq*)kPwXtVv~U_b+m0Y301Lp3n&iv8CQ;o6JPTpWd`yE
zfZA78OiX<77k!{@ofo?N$Y$1dhi^k%={AWZtzqYn((#P_V&2_lbW(lufn{ahh@0Qv
zJ|}IzQ;eQpqD>^~5bWUbM{Uj%InQiui#^_Huww%0_%a}_Lcq5Cs$1Cu5tBnJst)Yh
zI!^nBaT4z*$`tS#jFrpG$re0$=w@`{xbYvdMbdi+yCm}1*YPSTr~RQ?gt(ciNNn~e
z8hB>)p6>`*zaZ&}N_y=j%(RA!;U|cQu66g|Ka`10qhJ<d1-OVK2oAlzu1wWmCJP>N
zjs-7VKct`feQxf9fFKFrZRxsdK6l@fMg+@(zd+~_U%x^lZTmcn3;DwJD-;vB)*Mt+
zt4f!ziZ0)(Y8##=3stJQBk!+M@;#Xv(e`W3vn%}OKu{%`*T`6yxVJ9j+F_u@u!>8C
zVfr~og7z5|M=f8QEa32J-n;eL;KZP*vWf*?9GY8uh;+<5L}5(*uYfaK>Iu#?nQ--p
zE1G)5a#o-$13tbm6}oHrCA7%W8vB~Aj8eBt9fd3Jz2=K0^gcuNIJm+h$^)$a=m7Fk
zCaXVaajnTczVWoU_u+}2ZZnNC8~aJ`WsQS*hM1PV?DG=swyr6eK4<7Xz&makl1V~t
zTt<_{Ww%c!VOoQn+Al`af1SYm@$*YN9yDjSY>iTFabw%kc^uzs>vD){&9=H;`Ch=`
zp0N2m*lz@%X^gJUFUbUq^uM8do~3VR-kN|t+V|xz@dV?4mO!1@J&vhM0$u9IM!U<n
zRKbv)&P8U&JYlI6@MJ(9Bo<m#WF_AEK#5vOk^cC-npSBX%t0pJ@E1l*E!9*E7d@^l
zuKvDsO!tGk#W_a(WwY>+7N`Wl9<Be;=;Wy{7khp!=$7huG$lA0S-M3Qvba<;wpiWV
zK$44f=-6O|B0{Ne6I0qW5?>xZjiTwm#MjiIx*^#9yYI7ZOh6XS7?Wn~!w*2WITA1l
z@J_x<<{`~}bp!gFMlg|)P!=fsW+H;`uV!uEeT~#0*)|#SBV5x6il8momr{2zBP*eB
zrD`$vRVe1R=Q>2txm&)Uw(bGEwNV=fU5`PJjE0-2*!O+a&~+0bEI2~kODPX&rCU-d
zHC6aif}q2P72UeM&lr}TTV==NLaNkT7<tFOuf-j>?(govMp6LE3j2jM4TU1|@n%mV
z3)t-v_!h?G70JQ)B4*q^$A4!*AF`Gi&?&qVc*mJoEUB$u;RFUjYD}gw5IE3CDm?@%
zCDJFI(Imf=b#fiodVcS*yBDaIF3%9BNfYnnGcK2p<{&q<mhNNxm&Iaf5%FQfCe$?g
zt15-Cx}=4j@J=wz_U!B3P0VsY+^ub>nj%>7%oIob8ab!yem!fM4;Pn{6bMz}35(ms
z3um=zs3vjjC#ZB8C?qN_fJOR_#XQhvX$=tEBXPyqdr*W7_x@9t-BCMZiKRE;OfB))
za!I1ChbtW6wC>5e>Z)7t$*2P>pYs*P!8~0=NTpTwRDfguy`4Rm8aD7Wvb6;|F|#xe
zfO6&d>v6oc@0GQ7PYa;hy@j!R@Lg<UaxKNXBz_RzyKsG?KS{S9jmVVy<#&EHS7yM!
zGz{@lMNHZ!=;gM=x<T>LbeWhSz=6sKMRw~u?-1a%_fM3FTjqC|h*)wp4R+lrT0@<=
znbr@TtxnNJcZF3soJyctjI@%<{;^Jp#F}TFkit*ub8NIv<31-_??Wq`o4P#Ah@xyO
zEpw~dU9!e12_JfW-I19*HRzboYS842FIioKH8jh<VdF_i^T>A8YH;vC)Dxk&?M8+J
z<n<j#sOKdNssl=N*d#rG<cGf+<<u+BW(~I;GcsAD7P3n1seS3@!iXmCweE2IDZR(I
zW8aarxJER}Vz@gPY{@RjsWUt;!+XwNn!$biX<PG;KD4$%6ddjgt}o5>jS90UAzBk}
zm$V!qqDQK!eunBb$Nr0u{gp|v`WYtx0Eu}F&(&f%j!4*z3BPzHob1m2CL2e<;j9KJ
zuv#_|67O)gkjCur%9S&^s)+_XO&S7R9G|?ic|EW}(+d1pcOmJ<;*0IapwsX9lok{v
zKNr)bq`hv3g%L=F-i$@Fa%QRj3vxmo-09}iW~iZFzspZ{1_Ck}<Tb%Ov*#65Tt~j8
zavHTXu5LPR(d>c}j$H0@PJ3z+?uRO_Ha=^ckyavdRK29c-rEc_m7TKbTGv!uWIcd%
zGj=O@T!X#FD;noqFsfU;W7iCCF?$4BS^td@z@Nn^=QjPwH$=nAv<@HKd7-JLkC>~O
z!#T-;H+o8{-7nDK`u=mkn|r(ouhvz^o@MDV^4ixpepUTVo2GFCFcDC_=Cyf(fx)n&
z7aH5h%LhL}r!BQ;)Zo+2ncNMPnM}1N0O`3RR0wd=<rA5&F<)m5zXJPYrYo2XdD4WI
z<DC`Z-={eMQOZRXHlpWw!63aMllg|}A2)Zh2ry-6gFLIzHz30Sf`EF6MMBO%*_z?z
z?z|YwE|)pqd5q2m^sEC4)Xb=#Y9V^{82l9SWeFW9=}r310JR?jnZGAMcNJk%*hsYG
ze@R$OcoE9QyWP~LVQa~#tOQQ9;&=&uhC_Q*@g31lBCuVSuGE*u@5~k!gR?>yArL(a
zuyF%<L<4&!^Fu4bcdwXPw|`ga@44A<cI|p0Fnfh5Kl~IWYTHr>KQaZL<XLW0R+D0&
zl~2<XxyRR0_;v@+4-_3k63b^M)KYO9j9`%%<LNvLV`N~i>B{0VB4OxAknLFmL8S14
zd9Q}{1%!3e^z$yPw8d+_q7x-hu>Mr1oj`t(g)Z?KcLGt>ou_`B0Hx>qu?xJ5gL7-@
zAxMH*E`^QzHLD7v<BNF0aVb;t0I}|A^5zX71`BJ%RLtVHLv1-qP`_sDP$SXq#)`V9
zz@J2U@d*2Nt4=5gsamS&BKM<J0Gs4Jp3+#yV9L01856U2C9jD|?te2tUDQkjt|5ts
zRz=40gW<(qDgV5%hqkzxy&^=G0Yr@aUPF6n(la85W=vzU8eR&GKpZR<6!5;W8W6U%
zCbALb=?F`E2Gi0FE*J_>gRna!PIp4`8bd#<m1Nx*EsIRkQO>KUiU@NF1=F<v3+*9l
zQ8dV;6@v9I+9c)nv%!_!OCOlp!B?qC4pQnRkDIC!-*bzy?VV)1+{TOrIADmtC%BNR
zF2C%l>30xhtBSwNQsdapxO8o%dhaSiGGx@<AEd!g_iP{V(JD}t;!aM(n(G`r(^5VI
zZ)2ho?K)+CuA|=r+GDw7%5?7NY}3+^+?5)H(Ch<Lu|dH^O-?Cd_$Ur^UM(Vf=Gn1$
z2}G|98Ci7J6nvEF*fyg*&C4V}ck;Jlc_Ap&LJcHSEj2a3(>Y;yM=29Mzjx<aRplJ)
zE--+P6RiCCse@&(4XFftZz<&dE7fqX@H#M~5*J98<1CoH5}$l6L8lM>QxE;>w6hUf
zQ-iXGx-4f)ZHBFd<}>(j#R(8+Y3qlT;poFjCUM-5CY)EEOHY;;2-=UPI}xB^Gx!6-
z3z9}S3UchYQvC^)I0*udZx7x&Ha2at#8E~^*fGH6mOBH&zJ%%S?_Hc)6OcsDm+-GT
zZr?|4T7cBI*TnX=6C3pgfZir);Z0ELBhzU_DoCA%c$9W9MJgt}s-=v*QWF9kx*1Ff
z*BP;c`4U6oyvq!j*l<0GkiiYAoTsBj-tVxWFFHnyGZM-g<f_T8BA&R<SF{+TX=IU~
zg7a1eI;fRP7>N9V1)wKfh|3o9COYQ<$LB)UD^dl2nfJYM%2oFFy>JB=ibhJvv#qr-
z1nmL%qg!RJ`5sI-u$dMu4|@HxnaVN(RE%u9D)R_UaJ<%!Vb2n3wP=7?LAoJk(C^=q
zNU7AfwQT!vVD}6o0vTBqQiMjXVS~%haWuUB;~@Fsj<49Cvld8_LVHzTvyddP!Hw08
zEMC?qr(SEs5Mqwtz~-9Mrf0XeVRhNk7y@>!jl6Krg^J+=qCyoA-lOO)BZbGN-NIkp
z!3`4$6cD$wbWnrmxn;mFmQ3mZBlEa|`-SqIUGA8oEDk38p$><L<0yLJf+gO5s<1r~
zl?BS`1+|d;h8;gy{xxl<r~3{pso|<=MWeE;lt2jQ%r0E0`X!7o&{FKn;5gwes&EMU
zE*0{rk=?r|ro&>FF|P1mHu;X?hX#F(LUVt7X)ODv@p6r8RJ^^>?0FQXnKg)Gmq;3a
z1{Rip{)xwe7Q`AD+6Y^1%@8To&~HJBa>df7K8wMqH=iwE2_%ozzYbhSlB@WK8iY&c
z$wn-@GHMJY@PN7uai>$+rXQD#!$z#M9O)*p<xVR$&;=7i&$h^p<}I=s`Z)se=zqoP
z$fwiN?^G52ks8$3G0Tx*G}+9%A=}b0V^@LSw(OFDMvpCSOJb-J;yXFaT$-bgJ(FaT
zqmt0rSlUejqcm$Fg`DZG6xB>PTJrLV_26<A{UtG6jekq<o$6ge6>TzE*aSCUK;*dj
zYbZ4J@3^QHng%PUdj5)>6T!UDYKgTldVYSTnT^1z+;Xwwd4n<DV-D7UCUunB5naP+
z4g_W{H;?m|^K>)5LWX<?cJrQq$KDPY&G*erfI&5bSaZ;doGE9_m-50SB&@QHDv%&9
z{ys>^7`VAgL8p78Y@m$izcNFR{o>8W{{B^{)_<pRSAVWy@S|EkF!QN0_~mmKubv~_
zjR!V$(DcIgye!2xJaAW*Gtie|<M%tZSd63`0krVsUZBe-Du^TBrc!La@p^aV_L|*V
zWyMig*f<MSn^c-bg}-)eKy|rEW9-FoQa908QPcIany5e{Bkr}RfQl5Y_#swN-9M)0
z$$@BGXK9At{aLtS;vl+I{5g<5Wi#bM$n!^d{)Rf=lxPyJ3(qEc2e1PwZch~h<Ka<i
zM=zc<cu@RkTLIRM6UWf*6rqX~Wo+)`&$8vKHhp_tb_@lUo_&>jkVPcK4--q46m+4W
zw;&)-(*SEDru0BiBb#rUx2%uRQbg>2`GC>$7QmGm8IL)A&6R=GZa8c^FlW`lgYam|
zX$qYPk&hQ)Kf#>2d<ZmTwm0Sv6ua*Eh3b0!=@_hZTUz0i%q=W*7n8d#yxmH7S%rj}
z#)IYu-$u8PZZ>2+Vb~q8u0l>qHk6hXVyk7Yo{*9t;FfM{bB0^`)Sa(8C|!WNemfpQ
z7@ZPAd;P{U>d!$=D`$`>x1n2H>roJ^gOl@1nXNWrC5JCus61AlZJf<!W$^y<b9zCU
zvt82CBbi<OOrdIh(TlqAV6gt#bU?K+B)M}hQ{3zN+fUwi4!H@~@tuxZJt2c<KXByP
z(fjG(1Gim(<zP|1k#bAo{^;7nb+4kF9oY{u*1ZFSP{*cOdUKBRg)ep|;6-O$iP}Ns
z<^zs4wLCRe8`)LAQX!in__54=a*N)NTped)6JxK?8G=`Zvk!Ni+;*yy$s{XvBKlXS
zn*SE8GDWCyP=uII{Ur<M8=eZ6KXw>3EeIm6$gFH~WLp%JS~5dI9<LvOLzp?T#ZQm5
zQY-q5CX^SmEK8lvFw!154DrOuV~0Aov}?P<l9foV6!3axc6J}RLuVG#(>)AMDJGl@
zt;k-n?RxXAyh#^)xK|o-8OogQdv>_?Bv-b)*Ih|;)!7gXrQ#(fu$DyP$KK02NuW`X
zrx)lJLU5c)p8*6yf6-5e2a&u~-JOgxXBL)_tGI^u9sHzBv`h9~0|KAYexznKH8)Gs
zW0g4<NiLcS=h8oq557m#$4&rfxW5FOtU4G_s5^I35LjsAs1HdNwEbl&)Blp-vpWtp
ziRmvtI90JBi&!ES3-KT2OXYB!tQW9F3TeIP*J;-v$B|LQw4}!2F|=!y6qpN&IFmLM
zrm(5c$fP}req_oP6}7h_K?rSFW0u;+`xv=2UzX&X2yKxCl-Tedr2XfMPYgzKIPQhH
zq`IZU&W7b`&1T^n+ln41*`M1oG;<<2HpgEP1Rw%-uWl+b#~T{{%qGg1@6X>u(dijh
zX!B#xjiz=P@-X29v(A_dJ|$M73kxrneTw{N{l0}W_^2>MUSn@iwhU(s+ieo!=ja=t
z9i%}13b2BArr#<a5!?xo0#5ww!xBdqhv53jY^=^V!08F6_CVyFkda!+5aD7+025|_
z#zD^D&hZJ=3<!ZD9|y|j#`rYf@e!C1-BMRw#neBd0uvX2*ut6pm<EA%DhcLvIqk3<
z<6t8~AOpa3w~RbW3Sf(UHzmPhe3_N`5`^fQc`K%d_Sr?9ek&t<enXOEsylvKX+33~
z*xAj<HOz4+k`j;bYuQ7v_!!fg@6bygM~A8e!{ltb*DpQ_jQMNDDU4?)1DJv+Ob%wM
zLwY{A0+PPf7;rCEF}@CpxgE|?NQA~KCAXc?WOpXAQO;$2pCpx5-DVN%fO|ABd9^f$
z7kga;7P8^31cK<#Br?OiX$rE5_9d$8OUB3~#~KJc3=C(c1mvhWW>aK4SN*)EH_dM-
zNy!IbosVaira0)niuZ|lL*Q#r%TQJl=+6A@EF}dMQm}G|pY7|$z1df%^yi2&H&-qW
z(|)X`SO`K5x+L^2L8Xe?pro(yU{!T4GHct_PZ#>RCMD_U6}+9iAjKx0W08IKXyX_V
z?A^eVRjV%FG>z?ws04}bjQsQL+~UGJm&N@yHZB+9TJMRNOGh>csYM-QVxI+(x<~^Y
zl04THyf|S~)uKD02PVHSI}M1i6j<yNTxe2r09{(dAfP%_XgFgZc%z+UMVk8$|JFKu
zq9ef26KIyYD9IavIBqo=^OMXC$#r>uJB93>a<6Caqt3GU;m-A>5t+5<Qig$3PC3M(
z27DWp<`xlGCSsHc83b{}{<hAB&Qp%1iy045BvDq<IS#`6fzMd@!777VzGrIo?i}&p
z_N5yafB@s$8>hG;irL~{{asK8=xpjnj-FCCCn#kszl&E68(obr2Oc2(AuH7O;XECX
zj_p1&m&%7<GGfZpdIHUS-(UXrNP1GJE7&?RW?fw<wT`%W{GfE6N)iSkz`D0&dbFQU
zxTfubX?jL#!WqCg&7+EQ1Y-AY1fsUB$xj2mJCLRPopP7U`n3`PCeSA+)pX{4@C~eD
zCT@NeP;q^u^a1Fr5hI*_?Ktr8sI38`jYOD2ixt%+s%&tmJaCE(Mpz<@R<%#RZM~3d
z<4uNURv~+jFhKp{uajFX%%KLIcE(RO#=YyfEuS8#FPa|q1}br}(seU{idN*N_B<=Q
zs)1ZC0ClFSY-$jW63WKrbDZdc;mbehaj{e$oUPfnp`QmakQ6$UTPpN*lr3b<<r5Yc
zOf@f1y}!Q+or-wo7&fm+F%JLkmQ4m-$!0}luYJOyR?$IJ0RA-q*Vp!00^QYo9*%r5
zGn_jmDq~TLGzJXaJ}oj8b-c)ts=p;OQ>4k1YKmwNcHI>wu$)I7hd`g&P$PX<;M<@u
z=?RKStj)xSxevoSn{qPKQiO#eXJ!y@dR_~CXr~1`f4jO3_#C$bxlK>NBGPBxTSWg`
z;qGP7mMk&=MkEYA(8GM-5nP4a=Dp`W_`Mg$`TJ`FKD@jY91L)4DFwfT_MOTunRYaS
zv&jP`zNack7FR9_t!5eGx1NHi93>wmtgI;!YX|WlD%JDbk1AkAhvm@N5??FSNW^;r
z>&dv*_lF4SG*?HFOE<1obg>~EEhs`(HAV@Lljk$+gJ2#P8t~;eM2>c1Lq4@6%lJd!
zk&2j=q#6@{p7nxVd?@rW1qByKtY}vYTY~i73(9X=1>P1T$e>tS5m7Va?n!bI3rI<+
zrtPbjTSriRaEAcsU7@20DU%LK{3QX|cXX3;E@~UVseMWV&@X%oKt@qQ3;W6sih085
zbKY5CIiXSv-j3T=bUOQ!LsqGyfZr>vxvJj0LtYl_E4lt0IDS)CXK1>lcluhHjaHYf
z4}k65(zZ|tGo;%>AYOQ$Ef!e$8RpAnQsCbwvot3r|0S@}?6x&<+Vp5(jYtKKoM`()
zbje=kILhjyZQ(get!Z8D2mwx%Dm7gNf{D!w;%-*=xXBmAK%2W~#M5fDq!v|1!7ALf
z8ZIxoO0BJLc9?)(@Gi{}N}SWMut^j72Hk=iG*c%B)=JQVtCX>ac*OZecrTA}5gog(
z(44#)Zj{0~0_r7Y9}VMe9h`odc?s*aZVuLz)z{XGJn|O+ZQ+-T>S*r0j+v<`7+&bo
zSuG+`$pBE1wr{3X%PF|*=CMSo3<2Ldi{XKMEw|v4wY@#kmsufh@}W4T;vl}$E8QZ$
zL?u7u%!C7t-~xoXHJFp(NgZ>l6J-05b$cepEizA+ZyfXkhXW(sxk`5YM7utGYN|v4
zha0Z_D3Eq?lgOj#pTi=7&g*BgIBDmja*L{i9#_3X-ytV8?`ssexgulwCzxMvlsiw(
z`B_+IH}b`wZ2bvRoA{~Tx2U96{VJ2QCT+;Ubm(X=KU#PR!`=B5Vj7{fS^=|04HBP&
zVo{_EQ*zO06lfebW@>@t^%El1FLg5N-_Tm17Vi=Z`}*&hVTT|oAN3+L3e^oK#*J}K
zMam!1x`7~JiEQmoze#W^`qA2^iBh=S4$JQedU9i$QSDayGDJShd+CdblI{eLhpKks
zg^detkgvG_r(mfl<cV*WgW{0#NWP2-@$r6`pt;LDar0QZ*s7~WJ@<cq(saOmz2)qr
z+LN;#68~~7uCZme9ut7l@wXS{$ud+LD<@RD%|eU+_5H*hrXAZ$((w;5P@Ll0f-fVx
z)Dv`u%d`b8lFyhUhKB_--!2FMs2YZ{{ab;*yeldgjn5~TL)?;w`vmZgAWj6yyyX12
z@S0Ys!&3vPH0|YSisa*WZjy$q{OU~R%F#>Wojx(0Po3|aMhk-?N&8|m8f2uGH>Gty
z7=Py5kP%rIy}Kb76~kcFc96f5rM9?$c6HaSvhWhRPGh(<U&dJQ%ZG*dx<W#=NYE~H
z)!X{ZSTMc8jKi#NI&x*58aFCO4!r}8__gU*+XnYJMm4V*0;k;|#@S)ZV>v?Jaw4CY
z0;;<SiD@dUOma&$kT&g{^n7u>?PO=%-zBw@oDWaZM0YP{u_3uE|H0!rGxbO4dT1*T
z%eUyl>m5-c#Dz1W;59Wq@uGbs8~vS0T{Qcc$-{7)1ND!At_eft6zxDbvX2HU0qmwT
zoOiJ)`)uRFqA+IJ@s<(FblFztt`6df?q+~>TQrw4h!E+YIcs3$eq1mKPUHSQto%4#
zV`4G83baw?82T4`y`EPd*tP1T2Ta6t@gz-0es>cKOZ~bhMZkq6)u#%3x4Ud7f;`8g
zwDhC%>&QwnWBtrF9Tg6M2HAj!d%|NB4Sc%@WLcn|leAF0BUbG_;<ISa;i#Lhx*bT~
za<t>K1of^n?(yuHKoT7LkX%o$a*tb9Dt%xqK4e){2tt9Zgd8?QRH>90wJS4U;c-h#
zndZ1)<<AoCT^RcVF|w&cipwGp@KDcan4}%`twE{D@7S1#K;IPFujs;iZuX^5@9(8s
z%O9FGn$D-cWi@|*SEa3hrJFf58Ure9Ft8x<ms+s9>m^FqisduC`Zw``zOYQJIh>^x
zgi>OXn|jaP0m(1Se|+1i#Bx*$4PuJozhXiZ?ptfYp1hHReoGnKVSWpJFSkVw-v+zz
z#I0j4wT1K0j#ril#`I{KOenk*k?dv456~m$z7hhwo&(!ar_{hKll2Ps$a3Sc#wSg^
zIS5BEsw6+E;0lD?TPH-vO!?2$$)D3V`p5rd!Xa0dP%cdZ-s>tYJOv)n2#XSSHeQbw
zPM;mpUxLXCa)Y#<ZofMAxz2A;6Y&n~?@4jcUI-jz`$_N_uY}M)dYu)gdJ|#<Fv-7W
z1Y_YsL4F4jGrJ={RzWN@J}+=G85(*OM9#4b(`J|;pEECqJf~G*`|ve9c%9d2+X&De
zD%R5Q4P{<8;{Q3Fvi|K1jQbIk%y`8q=F|UtwLGY-<I}RVbVWGK0yOvE$E`FgEo~><
zPq#U$YRBcP%t?ymaui`iM-Grr0OkJncL;#EVOdP5Uw6-Jj>SOWUZKfKB8$p-V9|Yv
z8B-b65g)X>!2pg#eIUH+g!QIWkyI|5dw(RgIP1QxLT4{kj<Ce2yFy(PDaPpBFthO{
zBhM=KV6XC*SnC;Z(ESt>Y+VL>pPHlP!Y#^PpdU9C+A<RD;ga>!trP~WNLxSFKT$HT
z`n`0M#s@iu0KrvN%skcwU^<(nvUqYNvWHNp6yXQngK6Uf?lKuG0g&GVB1O*h_Hqy^
zGq;Hq?fhwwXqbIMYL>ts`=k8nb(vEyV^6G{&L04q)`SxHju#op+{F`FhqBvDc94<h
z1~^3_Z`MLZHP(>fq{my|JM5R}5bL21<?obj=RAh+N4xxNwY*?aX9>Ir@ij(wlmYE)
zjSFe^6YPZu(>xzDr=08oEf_y*pe4`E<Ig$uWMioOp{$McuUIs3;B4n80^W9mIVWiY
zhQh<XaYq9&3hrMy;DUq@fP-G#2I}j*G%QR{omRf~Q2+7h+h&2>UJg3keYC;hIBz&{
z`Zc;M8_|{^Wjy}$vJVA)O|N>drQL|4P>dq>hyBpis>f5~XWk96HKl*M)2?UHwYSq0
z&8%sl^adm|e;xP8<HuXt#C<!Ce3s?jy4H@GgS}9!T)>|Ib|sfMv|4A3ME?|+Z6Jb-
z-I6BR1*ayJ#a(=&vxfmzK&-01TO3KqT)nZ<sw^IO7ob)0q1W-$eVXR!fp0j?<N;Ge
zGB?NoZ>e>5D=4$mP*Ii0Fd%#xCzpDV{LOm?X~iw6-0n1v@v*yceM-8jp{CmrzdaAk
zHtmjBf@7)CaBJgTYPZx?p5~(9{;f#abW5gBMj?%a5d^OxypV^a76hT|3d@U0#p~nd
z6@VZtdf(L8yBisTEH)?|1<@pPvl8p#%P8locPSn;w}e2SXWRhB*~pH6N}O@yh`xK+
z%ExLE6)c%1v{?1?n5F_+NX<<gB&sY87I_^l#l@kI8SI09G)|Hzn{j101ZQbFECLef
ztZ3da&BV8y;vd@`99SNLIAIfGfv;D4Z#|(a5;dKB*}x`<800$b<KI%UJm&Sr8lM2t
zo-%hsE0i9K`Quj&XY5KBRlZ6q@-W-+qbr)@O|@kuAMoy4IpL7M<MvRbCLjQSNKVOE
z%h@}3q`vp|gSYAkR^8L5w?mYp+5YgR9KCccY%&vnE?qE1n(?!u?xZ*utyt&hQD~SZ
zscXq`t%I*HWOQ+4x~rRj134TR2y)$W$b{v#uNzOBakVXM(*qLs*hB!NXaw&j9^9HD
zhZYi-$&xYOo~sel7>Uc}0ZI#)>)S$T1FDDwA>%Wsnaco1^JbC05>TMbFvl^YT=iuk
zJ)3Jj+VADy)dg>yA)ehf00R%M-YqmN9_>6~5F0>recn9X1CzI2u}tc9I(hiP7NEn+
zfW9eTL$AiY6yI|h2e4P>$-+|Ol4Nn<K+jLw5BSH`9~)bT>!Gbd5RA%yz+=i>INzn<
zG!Sg7hf=Z{ev&yVkSRG+{zEDM*78ZM3Z(i)=)w(h&AfzAM#f!a0!xA4lHMI@(S2oS
z1%))OIorr}_H#`F7EAm%aD83o&FbBdms3L9sVjbxFyQ*j<&*$1hWIf-lIjk}p4rV;
zizk{MA~Y4tHXNRo5ELiNClJG6sxd!?!DRg>$jbuX^G@cugUKKE!@r$O6H{euj$m<(
z&nkxutmK}odiu3H>%BfFflDogESt41=qcpJbUL)(T};hrgqS(hT@bLFE_-5Df&kpW
z?X?y{vD;(Re(((lg)3u(=gIsYuSW;?1r+#ed-am(1C%Ez>yk)qZR@uMf)USIoqp*y
z&9Pw&a@LLIY&~Fcw66&jOqhYe5=d`2GSiva%%p}4Qy%a5hX(<&P=*MfM)|SmO4(&5
z$x{x%87amFV+HeCV*ToOz%uZ8xr;C9+s+|<38S!=NCxb&VVws*f6;n%p*`Mw`xf`k
z?D}u*2v~jcV7h_%5?(rFc}IS99=tY>Ww_ez6O2&**HfAS(KGts9Jn<x7BU0a>NZPg
z52mM2eU0z-4S@4JRe-iJFB5`Nh|u;@ECyt@A>aQCAQRv0teFl^uOy^PO!?dG43t<M
zeO+D%uHQDeUg|I|kD0JOn@rLc!G6to)~{+)ajq)3;x0we`lJu~y|e%Rznj^UWmiCY
zZW!{_!<@!jOsEydgG2m*OD+4`xgj?Lf_7X^I~hTbpDxc~5YEMEf+3~NXd1+wPALb9
zG6!ci-WC(56XPaKLmAPyekuPKO6xQ{5wvPOA_yOa-@Bp`Xmg1&S2apK<Lh2?kkG<F
zs#<Lc48)yp=v0)aMB%;!$2fwH^(Q$<H&4ifh+zZUA|7dly?=PXbJW;_H=&B}`9IS{
zg#lBYqBemS^y@M2xZBY5aPHKxA6}8OpF|*v$LF}3RnTGIElLp_FB?%Rv`;`*$murg
zIlfbI>wr~uWJZFuzUE-TRi;h7#EAq7t0)}3W`A0oo1Me>8n7Mdom&9v@x+ilgSo-G
zL1GdNgB5W6!n-ykP-HP-Ri1r`>Z$p9w>{2;?qMUcI0m26NtjG<gd0(Eq;lJawqis&
zTDsy1V<r6_9e3W2pt|+k$bYSZ3n*<=;_8zHNHNPdSUqTa$R7@@0xmRjmJX17nkrl$
zNCmp-;Q>3|BcICP&1b+q*2Z19$Oy=wS{I~UJ3cxPsv~Hm$NFcshf6Yk;QQP-jL)?|
zl`cm>q|8L45lWlL>|Pb|E$06C3(a>c=jabo{D#GQK<JLte){tQ>7A2dN*(dJo9b^)
zp3}6XH7YF`CBm$e_)fIMp^7j-nGcM1lzrFgZ{n(=|MsjMBCu3~Su0p!1GC;)wx@Sk
z4!`I@qLIODzo2AYpNY*;B6|A2N>1<m#ace-CJ(*{UUT@ZhEVTryk}r+KGz~}Je!`4
zZRBISJTY%zZo2XYn>+5seHWsPL$-OSBzcSvo?qnT^(%paQ(=0%{0rKz(+xJ>1?e3@
z+?t9NmtZ|GrTD89pnPLX;Vc+n2YGg>{K*74t?BgAgXw2g0L9Zws-&3~&)Rj0WN?Q9
z=SU5#=_cE5Dv1FH(}`mh4;Cb-6Fxopk>zf{%m05{!0zwQi@uWZOWZ`X0vRB>89TRo
zmxENaSR5$H=j;{^<K}~wX%uvR?6b7^nP^0U7uY@+`2@_+QR$Sf)OG{2dyL4UbsP<a
z)7H0W4#@By%iJ5)7*$)?t2po=540jLZYp_luRSZQKhHoqEA>=8P@ty=J+=eFL+AB1
zXGRK>%o(icex-R7Q8ZzQ(STRdV|co6U+n(HlaZ~wOzm|w=ZqhAnN9IXqJ$zq<EOeo
z%P|>S74~}}Shs%SI7z}fS8?A#4rS<W66`Jy^eRw)4EFbpON=c&(!)dn=2Il?>T_$%
zieuc&A;2or#5RZpRD#q|tk76x!M>SMYzxEK&gCygdWghAiX||OR4ru>4?OKiAT|4c
z4Ja@t&0%Ks<%Xt4mMUE3`Z8ob&<l`lXC-RXsV!%h*AWn9zg5Ykdqj>C(uryooGKb8
zZfE>5J2g%>;>-~e2LHifSU4N_9*jpDSc#gTrdHBcpRo^$@%*=9-3AO6mgTnram3B*
ziUfDE+9x9d&l(`1$vPuIWy3ha0Bn<fvRg=`{ODSA$zvOQQFj}#-N`qj#jb7{tGdb;
zm_mu*<38uD#ih3rVaqT|9&$hdyL*~(^4ixqd5u{NAA8t8w6#BX@2A5B+ke5y)z^CF
zWUJWE`6&}!w`fMHdNTWtKMJL^Ny=8F!rq{`v_eORHrk47;@X%bpKq{A4)c@Xnib&;
zc7Fy&HV_Ge%sII00~Jk2jgS)>7Or7=Tq}3H^C_~=Zj5StlxrWbFV&K*497BKy9UF%
z`=y-Z7Hl!{T?Gu1t5%Amxy2+AtH$~PEP0h9DLziTAjWZ?4XXI2C!xcD5>#nSYTxh>
zI`Axvn=<EWzMa7^ijY+yr#n^TVPjhnZ=#BOPjbu(YH~1Nh#10caX=fz%D_vxPP_oI
z;U-?|C2NSy(0?vRNzgu)(oUTlwF+k>i3W=<KW;8oxjoLL=Cm2`1OLcnBaiGzeVsBB
z=+D+ru^+eAk0X41t;BZLOaI`fK&Y@2vb%Tn6?Le;Ii$2_GriCf@R`iEN35}|QGlbE
zPZu#MV1f?xBluwmcs1t+V^bopxQ<*|N?C6R5d(>-*+H4bh%-dSxJ9@GP<t5uPh1UL
zT_|eUg44vg?vT9a*r?qAWnEJJ*?*515AOrH!qQ4Zq)w{|9OotBuPn~-cDX>E;yfz3
zp9^bai<(<#*{$$YVgvV=xM!vWwa%$HGrswB_I`xs4&Qaec-tzrTStx($TMhmiF}zI
zQX1Hn6T9xh8=cSIzL74}1Qr!i_pl<5k*t^;eeY2M15ryG+MJBd+<eGZ<LD*-PXl%!
zM*~<_|KuWigH%M7@MdKQ1%w?6^jEh+wP()P5SmxfoDYN8S?D=CGHCX<=z1b4z@ME%
z%)yz{=bZc*AeEtObe=(0&yJ?#r3)9?b!5GWt<wSZh1ngj==NorO?P+;W367<D_>;E
zr`cdRw{tjnY|3k6TKf)c<2dINpw5>#)hg;K#WT}Z3XSOL0a1<KT`bk>ZZ;-g;n?Zz
zv9Fy6=AgUibxm2vP%MW8!@A96WfaME(2#_M?s;S=6xhbdU(<lrUin0;Vu0s#WiMAN
zKaXqpcP-^-cgz2+q+pM?HLGwIKP}3%nOh(#8@os>9TFt-dhXi_k=2|V2UC<ZKbqga
z4(qBYm%|wizRy*OsMDu*{IhvA2~<yf+vvFJLz~c7%tyWf04Cp%lmn3f9V&Oo%=ZCH
zMNgxpf{?FbYpEX}<^k7d_=hz0)f-Omst{re#?i*aZhTF5?jNV}!pcJ`Z=MV>u|AMq
z=O#^7?6h^Vi4p$}rrLo^1_X}}O;$2!pPj!R!&cQNqbog7s66@rN`xEH<i9v$UcZf%
z37kY^NAO0_ZUEKSR04Jn_I+a6a}tDe;@}K)+K=(h14JlsDYA^%8p3~WKM#NF{HiZ6
z4bNZ=1;aL7FIg%{%kp3RcGI=)^v|0A3@pv^pgKxGDedWSCN3AH;tXDx%SK{G?qc|e
zD!~{W!OWkh&I2BjMA(EycOxxirvJz3ZVDe6khmZGZAo+|pHbD5E+H5{K!J1PtJ0?2
zJ|7+oC%#XsNh$ZEoiP0cy?MVZ=bzS^t-Rq)$=Ao)f~-Pd5cIN3jbH~riLP1faAa|7
z$e<`tq{XCu-Bh$EYXE!#2NByEnBj#+T%a;z*BeFv2LSM2So;snt7t(n%tOb;WDv8T
zzbgj1S!dJ@8WmB8OB1V}DIlH5Aun7tP-6Sk@H9JE93B8Q_3z9drcDPM$S2MfRlQl^
zR^BA=P}3%J)|BM-Zx&-%oc4Q-+yFjk^5?A7+F~qGPe_|7f0chLLF-d_+Bx)bo>7(x
z#UG^_x`NLvfPruQCouTHwrRKuj27nSNYAq&%0n~sW?Im$We9+3+gG-7Tc7EPMo@o2
zrXPI4M8V_eX5g6U{E*WQ)_wZEopk{ZLoY<m&o4Vzysc2fy!@X;Z<l)n;ac!zR;>7t
z5ZT_o@6gktpCS$5itDYih;c#OAWxv<ysY`~Io8Y|9cc5PAH>Vq<z=1sbX173pE1`K
zRos+5%0u2lWG9Ly=5DQ8QlkgO-Dq7;cRjZ@HR%-a87C2ojNB&#s-HB9uXj}2tOkOe
zYlNl(Xze=ChWO!|f@4wt@Yd<#_+*Ob2=4P(Ej^=5>6!c)c>3yiMx?#%SALix@}AMl
z&3~2{JX2qz0bd~YNjW-aLDXSQH=}-!Az6-Un%}QGF9EGRI$ylmLA|KpV<tTF@16!A
z6v&cd!>#S((Rk;8D0Eb&Ba~I<ey0&>HyZOoyJnE&1y+U~`Sa0N18%fPmlwNLkxwVy
z3=a$I4qpVmlzSMno<_2;-AewZvVsm!yurqfs5L1*BeWmL5y}}pwy7De#5c~m{(9{Z
zwKkQuONjWvs%MhoX^CDkui$xcNP8*=%0KwCXTbnWB*E%Wc45aS%Te;3KZ9K7jUqsM
zRg_}~%c>x@@8|AtAm?Nnk1hNfrMV|epNw#l;_!P4mkjIn6Mek%&J(&CoP)WZfz?&o
zR3<9FJ@+DaB=R#fiT{9Ew!e7l^}e7@=j{ix58c@1w1=Wg4qC32>qqCl>Xd}k<tMS<
zhptA+d!_(E^_MfaP?x>!xFL-nw=Dc~COQ+y_2voy#!=-Rxq8{GAufbYVdiu4HoyB5
z*1FHqJVSir&Ns7IISxYj{w>BI|4IZaIRt|3_Ym7-oPH6oQa?sljt`#zup}2KEH8h}
zmi0xzzW-5$hm3BNi<BLlF?ddA0s?*Pe-+kPvNF~v{U8N20fFNN^)F!(sB=@&PUj%7
zU_8$4=9^m&Pp`Kln2Rq)l?2>7sdVo}9_y(XJ@_s_*jzK*c2uiilaqf?&abWV&SoN-
z6afa?a?KFxlnI74wC_`kV9tg=B4P>^|Dp4-^*gB(n|E`JSBq7v1rG+^$M&c~TuASg
z8^V;kO|}XuDtF2<^p5KSa`Qhlt4dff0Ws=`r;6DSvjlDRG<V7oWQIKzc^nNBP3?3^
zMSqlZ?pb8q5_{?1(R(~N+J|x5&gh6a2SM)9B(AgUBLaqV*hsR<mwM~EdUm=H$2Y5r
zMHM9_za-m|;TYmaA3#-B+5mGo-C5g=Fm{xh?@jOEwLO&9v;vY<$aIm62F+V*J|&ph
z+!NdB4<9-F9b{ZkCfXQ!M3e1(-&@r33VtWY^$^Lj@$?wf$g{zw2&X})rpmu%02g10
z#MEwD<)oNw3dmwpN1JtK2h7+7kIz&>0h2|VwJ#1|R!z4ba`0%IN@BV%zUeF7k-tAQ
zu8@c}3;>%OY|0)LP*~?7_5F^T%jI^AUd?o85CBzH6ru~J-+&AI+wWY;e?~v4=ikJL
zRXYeZK7sOrdr*Ui#&aDqS{W<uRa`rL-_(tVuqaU9+*f4yHnu84r``K`REm8`@^FD&
zl{YdmPfPSTs41V6iioX{CojTes+91H)ciczg@H9`L#dYFG#*-qU;Vg?)TU-q=<GL}
zFVjl@ORxd}Gck>Gq}epHZN}9CIXceeo+_W(H{m(EIv$ujs()TMp**foa+;?rc3?x3
zgNLw1MA3J}OUbd>`x;C%?&Gt^u@P>Z4Qyb+z0u<aE@%@l!yqB132xnB5nPt8ru>uf
z$120ic4uCE0wabnG|NfLfX1TO0U1ehz5wvJz2kWshgy*cFBiUS<*818-p!j_K<f4)
zGYiz@<*0faC225CNImp2k~^~3DafFW9uw>hAVH`MctH2{g>RzWt<|M!AHC<NJv_xA
zox1ZE2WKp}I0;%YnM5sH`*OJv+6q9G@jF1aog~OuNs>Zw4;jWRxZ+rN7xVLDL5=-_
zPwI#Y8@oOBF-0Ivc4@7+GG*i5>W*=7Hbd20qP5AP3tsYyLH-E-;*b`i`W87)t&{HS
zeQ@$rZ{{4((x0Ndt?^ieJn5Fw&5!XUch>1YX_s`=@~|Y5?MocOnBDcLNvIxntOom|
zYe4y4?G1D5j|h!wPNGW)Fl`Bf5*&oBPP4QzpwO`gXhpn?YjIuuIJ2*I%&$EVIzJGo
zIa<#&;p=y~hi&Gg^dMs6Uxog0;o(aD!#4f=`T4JILcoZCxhu0T@q&jl;=~Nb%c1Jp
znZs|2K3x49^*-|(f3hBsKYF+ktGfkZBUALs12mK0Vr0ws!G0Z4uBQpN<3&3NSLNpb
zQ=TqE%WC?52I+SIj2F}yaZoUBCpen$x2TYcwR{`XPFW5%LP)95*^^AGGgDmk><9q7
zDgQ+rj=yd%m5v`a?K3QjqTCckCHPmpn{8LtA+6W4dOwu;*x&k~ve#?u$TU=L=vE1d
zv9)4#D_KfsU<K_J%(T~sXV%k-%*lF-1*gi6LcRGB%0*{Kp5m!(4T)a(e`Eg?epc0r
zc1I_pqd^Ye%zKlNgbSN_l<eog7^P}2MpY{|4?xRpSCBjo*qJ!420%y(Eg><<h(U6^
zpts+vtc#TzwBls3qXy73sq~LSU4Th=x;yW%ztr1W?cBUYKNR`aOlG9MFF8G9)DaCj
z7Lguyvq|~3t$&)FW?Xl<Lc6Aja5}I-vAPHcnt%V8)@f!wWm;!O*dMF;I7=W`T}MT|
z{+=ADYm1B>iT#Ju(Pl_^m2tCk@8oLGJHKBa4!asbo+BD%n*f>09=1eA&BA{B%NniR
z6BTXAo<V0f`ja~30)7p77oTP%Dc_P$W6hrVG8u6a&n#fKeI`34(>;7O5bXh{v`k*B
z%4DGoXRe+J?h@t_$@_S{?jvNCfD2;?Y?>8&*xweX7rs^lg^V?>(n5_mdwY~oWqED3
z{pHI-t1@+-G10a8ME2Xu$h|t@+AUFWG8yWcxFmSM5&#MZ8740;PYQrCGZ4@M2tf3_
v^$bV|vC&CZ&;Zw~nm8+NCsyJNQBy{BLgA)Ho@2W5ybVRIhit^IR_NSH2(luz

literal 0
HcmV?d00001

diff --git a/qiskit_bot.yaml b/qiskit_bot.yaml
index 83c1ee2bc42..df8a4a9c3f3 100644
--- a/qiskit_bot.yaml
+++ b/qiskit_bot.yaml
@@ -413,6 +413,8 @@ notifications:
     - "@jenglick"
     - "@garrison"
     - "`@beckykd`"
+  "docs/guides/function-template-aqc-trotter":
+    - "@pdd23001"
   "docs/guides/operator-class":
     - "`@mtreinish`"
   "docs/guides/online-lab-environments":
@@ -831,6 +833,8 @@ notifications:
   "docs/tutorials/simulate-neutron-scattering":
     - "`@nathanearnestnoble`"
     - "@kevinsung"
+  "docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function":
+    - "@pdd23001"
   "docs/tutorials/readout-error-mitigation-sampler":
     - "`@nathanearnestnoble`"
     - "`@jlapeyre`"
diff --git a/scripts/config/notebook-testing.toml b/scripts/config/notebook-testing.toml
index 7a278f3adac..09cd5061827 100644
--- a/scripts/config/notebook-testing.toml
+++ b/scripts/config/notebook-testing.toml
@@ -145,6 +145,10 @@ notebooks = [
   "docs/guides/function-template-hamiltonian-simulation.ipynb",
   "docs/guides/function-template-chemistry-workflow.ipynb",
 
+  # Deploys a Qiskit Function to Serverless and runs it, so it needs saved
+  # credentials and a deployed function. Not runnable in CI.
+  "docs/guides/function-template-aqc-trotter.ipynb",
+
   # Only works in runtime 0.41.1
   "docs/guides/monitor-job.ipynb",
 
@@ -222,6 +226,7 @@ notebooks = [
   "docs/tutorials/compilation-methods-for-hamiltonian-simulation-circuits.ipynb",
   "docs/tutorials/solve-market-split-problem-with-iskay-quantum-optimizer.ipynb",
   "docs/tutorials/simulate-neutron-scattering.ipynb",
+  "docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function.ipynb",
 
   # Don't test any learning notebooks
   "learning/courses/quantum-computing-in-practice/introduction.ipynb",

From 1140ea0679068de99c1e0afeb84cbd4ca79e4ef6 Mon Sep 17 00:00:00 2001
From: Parth Danve <parth.danve@uconn.edu>
Date: Tue, 11 Aug 2026 23:10:12 -0400
Subject: [PATCH 3/7] polish

---
 docs/guides/function-template-aqc-trotter.ipynb | 10 ++++------
 1 file changed, 4 insertions(+), 6 deletions(-)

diff --git a/docs/guides/function-template-aqc-trotter.ipynb b/docs/guides/function-template-aqc-trotter.ipynb
index 3f73a51b881..2a490e4867d 100644
--- a/docs/guides/function-template-aqc-trotter.ipynb
+++ b/docs/guides/function-template-aqc-trotter.ipynb
@@ -83,7 +83,7 @@
     "\n",
     "```\n",
     "your-working-directory/\n",
-    "├── deploy-aqc-dynamics-function.ipynb    <- this notebook\n",
+    "├── function-template-aqc-trotter.ipynb    <- this notebook\n",
     "└── source_files/                         <- the renamed folder\n",
     "    ├── __init__.py\n",
     "    ├── program.py\n",
@@ -480,8 +480,7 @@
     "        {\"n_steps\": 3, \"ansatz_steps\": 2},\n",
     "    ],\n",
     "    hamiltonian=H,\n",
-    "    backend=\"runtime\",\n",
-    "    backend_name=\"ibm_boston\",\n",
+    "    backend=\"runtime\"\n",
     ")\n",
     "print(\"job id (save this to reconnect later):\", job.job_id)"
    ]
@@ -540,7 +539,6 @@
     "# Run this only once the status cell above reports DONE. result() blocks until\n",
     "# the job finishes, so calling it earlier just waits.\n",
     "result = job.result()\n",
-    "print(\"execution backend:\", result[\"metadata\"][\"execution_backend\"])\n",
     "print(\n",
     "    \"AQC fidelities:\",\n",
     "    {k: round(v, 4) for k, v in result[\"metadata\"][\"aqc_fidelities\"].items()},\n",
@@ -569,7 +567,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "neutron-scattering",
    "language": "python",
    "name": "python3"
   },
@@ -583,7 +581,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3"
+   "version": "3.12.13"
   }
  },
  "nbformat": 4,

From 4194bac37fd7c57dee187e7db509e700614c5280 Mon Sep 17 00:00:00 2001
From: Parth Danve <parth.danve@uconn.edu>
Date: Wed, 12 Aug 2026 15:29:37 -0400
Subject: [PATCH 4/7] vocab

---
 .../function-template-aqc-trotter.ipynb       | 24 +++++++++----------
 docs/tutorials/_toc.json                      |  2 +-
 docs/tutorials/index.mdx                      |  2 +-
 ...on-scattering-with-a-qiskit-function.ipynb | 22 ++++++++---------
 4 files changed, 25 insertions(+), 25 deletions(-)

diff --git a/docs/guides/function-template-aqc-trotter.ipynb b/docs/guides/function-template-aqc-trotter.ipynb
index 2a490e4867d..a1292826f88 100644
--- a/docs/guides/function-template-aqc-trotter.ipynb
+++ b/docs/guides/function-template-aqc-trotter.ipynb
@@ -6,13 +6,13 @@
    "metadata": {},
    "source": [
     "---\n",
-    "title: Deploy and run the AQC-Trotter-dynamics Qiskit Function\n",
+    "title: Deploy and run a Qiskit Function template for AQC + Trotter Hamiltonian dynamics\n",
     "description: Deploy the AQC + Trotter Hamiltonian dynamics function template to IBM Quantum Serverless, then run it on a simulator and on a QPU.\n",
     "---\n",
     "\n",
     "{/* cspell:ignore Trotter Trotterization quimb cotengra cotengrust Suzuki fidelities isa */}\n",
     "\n",
-    "# Deploy and run the AQC-Trotter-dynamics Qiskit Function"
+    "# Deploy and run a Qiskit Function template for AQC + Trotter Hamiltonian dynamics"
    ]
   },
   {
@@ -32,7 +32,7 @@
    "source": [
     "## Overview\n",
     "\n",
-    "This is an experiment-agnostic Qiskit Function for Hamiltonian dynamics. Given a 1D nearest-neighbor Pauli Hamiltonian, a prepared initial state (optional), and a set of observables, it runs Trotter time-evolution, approximate quantum compilation (AQC) circuit compression, and mitigated execution, then returns each observable's time series. Swap the setup (PRE) and the analysis (POST) and the same core drives a different experiment:\n",
+    "This is an experiment-agnostic Qiskit Function template for Hamiltonian dynamics. Given a 1D nearest-neighbor Pauli Hamiltonian, a prepared initial state (optional), and a set of observables, it runs Trotter time-evolution, approximate quantum compilation (AQC) circuit compression, and mitigated execution, then returns each observable's time series. Swap the setup (PRE) and the analysis (POST) and the same core drives a different experiment:\n",
     "\n",
     "```\n",
     "   PRE (your setup)            FUNCTION (deployed here)             POST (your analysis)\n",
@@ -41,9 +41,9 @@
     " + optional local kick      -> <O>(t)                               quench dynamics, ...\n",
     "```\n",
     "\n",
-    "The template is published in the Qiskit function templates repository, alongside the other application templates. See: [AQC Dynamics Template](https://github.com/qiskit-community/qiskit-function-templates/tree/main/physics/aqc_trotter). This notebook deploys it to your own IBM Quantum&reg; Serverless account. Run it once, and any notebook can then call the function with `serverless.load(\"aqc-dynamics-function\")`.\n",
+    "The template is published in the Qiskit Function templates repository, alongside the other application templates. See: [AQC Dynamics Template](https://github.com/qiskit-community/qiskit-function-templates/tree/main/physics/aqc_trotter). This notebook deploys it to your own IBM Quantum&reg; Serverless account. Run it once, and any notebook can then call the function with `serverless.load(\"aqc-dynamics-function\")`.\n",
     "\n",
-    "For a worked scientific example, see [Simulate neutron scattering with an AQC-dynamics Qiskit Function](/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function), which calls this function to compute the dynamical structure factor of KCuF$_3$. This notebook covers deployment and the input contract instead."
+    "For a worked scientific example, see [Simulate neutron scattering with an AQC + Trotter dynamics Serverless workflow](/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function), which calls this function to compute the dynamical structure factor of KCuF$_3$. This notebook covers deployment and the input contract instead."
    ]
   },
   {
@@ -56,7 +56,7 @@
     "Before starting, be sure you have the following in this notebook's kernel environment:\n",
     "\n",
     "- Qiskit SDK v2.0 or later (`pip install qiskit`).\n",
-    "- The Qiskit IBM Catalog client (`pip install qiskit-ibm-catalog`), which uploads and runs Qiskit Functions.\n",
+    "- The Qiskit IBM Catalog client (`pip install qiskit-ibm-catalog`), which deploys and runs workloads on Qiskit Serverless.\n",
     "\n",
     "The function's own scientific dependencies (`qiskit-addon-aqc-tensor`, `cotengrust`, `qiskit-aer`) do not need to be installed locally."
    ]
@@ -68,7 +68,7 @@
    "source": [
     "## Get the template source files\n",
     "\n",
-    "The function is a small Python package that Qiskit Serverless runs in the cloud, so its source has to exist as local files that are uploaded at deploy time. The package is published in the Qiskit function templates repository.\n",
+    "The function is a small Python package that Qiskit Serverless runs in the cloud, so its source has to exist as local files that are uploaded at deploy time. The package is published in the Qiskit Function templates repository.\n",
     "\n",
     "Download **[`source_files`](https://download-directory.github.io/?url=https%3A%2F%2Fgithub.com%2Fqiskit-community%2Fqiskit-function-templates%2Ftree%2Fmain%2Fphysics%2Faqc_trotter%2Fsource_files)**\n",
     "\n",
@@ -480,7 +480,7 @@
     "        {\"n_steps\": 3, \"ansatz_steps\": 2},\n",
     "    ],\n",
     "    hamiltonian=H,\n",
-    "    backend=\"runtime\"\n",
+    "    backend=\"runtime\",\n",
     ")\n",
     "print(\"job id (save this to reconnect later):\", job.job_id)"
    ]
@@ -555,9 +555,9 @@
     "\n",
     "<Admonition type=\"tip\" title=\"Recommendations\">\n",
     "\n",
-    "- Work through [Simulate neutron scattering with an AQC-dynamics Qiskit Function](/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function), the companion example that calls this deployed function to compute the dynamical structure factor of KCuF$_3$.\n",
+    "- Work through [Simulate neutron scattering with an AQC + Trotter dynamics Serverless workflow](/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function), the companion example that calls this deployed function to compute the dynamical structure factor of KCuF$_3$.\n",
     "- Read the [AQC Dynamics Function Template Github](https://github.com/qiskit-community/qiskit-function-templates/blob/main/physics/aqc_trotter/) for the complete input and output contract, further examples, and citation details.\n",
-    "- Browse the [Qiskit function templates repository](https://github.com/qiskit-community/qiskit-function-templates/tree/main/physics/aqc_trotter) for other application templates built the same way.\n",
+    "- Browse the [Qiskit Function templates repository](https://github.com/qiskit-community/qiskit-function-templates/tree/main/physics/aqc_trotter) for other application templates built the same way.\n",
     "- Read the [Qiskit Serverless guide](/docs/guides/serverless) for managing deployed functions.\n",
     "- Go deeper on the AQC compression stage with the [Qiskit addon: AQC-Tensor](https://qiskit.github.io/qiskit-addon-aqc-tensor/) documentation.\n",
     "\n",
@@ -567,7 +567,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "neutron-scattering",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -581,7 +581,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.13"
+   "version": "3"
   }
  },
  "nbformat": 4,
diff --git a/docs/tutorials/_toc.json b/docs/tutorials/_toc.json
index 3787f0a0638..593f802891a 100644
--- a/docs/tutorials/_toc.json
+++ b/docs/tutorials/_toc.json
@@ -62,7 +62,7 @@
               "url": "/docs/tutorials/simulate-neutron-scattering"
             },
             {
-              "title": "Simulate neutron scattering with an AQC-dynamics Qiskit Function",
+              "title": "Simulate neutron scattering with an AQC + Trotter dynamics Serverless workflow",
               "url": "/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function"
             },
             {
diff --git a/docs/tutorials/index.mdx b/docs/tutorials/index.mdx
index 636e7bd2968..745c01f55e4 100644
--- a/docs/tutorials/index.mdx
+++ b/docs/tutorials/index.mdx
@@ -51,7 +51,7 @@ These tutorials focus on estimating physically meaningful quantities, such as en
 
 * [Simulate neutron scattering in quantum materials with quantum circuits](/docs/tutorials/simulate-neutron-scattering)
 
-* [Simulate neutron scattering with an AQC-dynamics Qiskit Function](/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function)
+* [Simulate neutron scattering with an AQC + Trotter dynamics Serverless workflow](/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function)
 
 * [Krylov quantum diagonalization of lattice Hamiltonians](/docs/tutorials/krylov-quantum-diagonalization)
 
diff --git a/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function.ipynb b/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function.ipynb
index 5430b8f60b0..50e1a675ad1 100644
--- a/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function.ipynb
+++ b/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function.ipynb
@@ -6,13 +6,13 @@
    "metadata": {},
    "source": [
     "---\n",
-    "title: Simulate neutron scattering with an AQC-dynamics Qiskit Function\n",
-    "description: Compute the dynamical structure factor S(q, w) of the quantum magnet KCuF3 by running an AQC-compressed Trotter workflow as a Qiskit Function.\n",
+    "title: Simulate neutron scattering with an AQC + Trotter dynamics Serverless workflow\n",
+    "description: Compute the dynamical structure factor S(q, w) of the quantum magnet KCuF3 by running a Trotter workflow with AQC compression as a deployed function template.\n",
     "---\n",
     "\n",
     "{/* cspell:ignore Trotter Trotterization spinon spinons KCuF quimb DMRG magnon antiferromagnetic Suzuki fidelities isa COBYQA Gjjc qpoints viridis fontsize vmax vmin */}\n",
     "\n",
-    "# Simulate neutron scattering with an AQC-dynamics Qiskit Function\n",
+    "# Simulate neutron scattering with an AQC + Trotter dynamics Serverless workflow\n",
     "*Usage estimate: 18 minutes on a Heron r3 processor (NOTE: This is an estimate only. Your runtime might vary.)*"
    ]
   },
@@ -27,7 +27,7 @@
     "\n",
     "- How an inelastic neutron-scattering spectrum maps to the dynamical structure factor $S(q, \\omega)$ of a 1D quantum magnet.\n",
     "- How to prepare the KCuF$_3$ (isotropic Heisenberg) ground state with the density matrix renormalization group (DMRG) and matrix product state (MPS) fidelity maximization.\n",
-    "- How to run Trotter time-evolution, approximate quantum compilation (AQC) circuit compression, and mitigated execution as a single Qiskit Function call.\n",
+    "- How to run Trotter time-evolution, approximate quantum compilation (AQC) circuit compression, and mitigated execution as a single function call.\n",
     "- How to post-process the per-site $\\langle \\sigma_z \\rangle(t)$ time series into $S(q, \\omega)$ and identify the two-spinon continuum."
    ]
   },
@@ -70,7 +70,7 @@
     "\n",
     "So the experiment-specific work stays here in the notebook: ground-state preparation (PRE) and the $S(q, \\omega)$ post-processing (POST). The two quantum-heavy steps, compression and execution, run inside the function.\n",
     "\n",
-    "This tutorial is a companion to [Simulate neutron scattering in quantum materials with quantum circuits](/docs/tutorials/simulate-neutron-scattering), which builds the same experiment inline: the same KCuF$_3$ model, ground-state preparation, neutron kick, and post-processing, with the Trotter synthesis, AQC compression, and mitigated execution written out step by step. Read that tutorial to learn how AQC compression works. Read this one to run the same experiment through a deployed Qiskit Function: the quantum core becomes a single function call, and the multi-hour AQC compression runs inside the Serverless worker instead of on your machine, so you do not need an HPC system or an open kernel while it runs. Because the function is Hamiltonian-agnostic, the same call also drives other dynamics experiments."
+    "This tutorial is a companion to [Simulate neutron scattering in quantum materials with quantum circuits](/docs/tutorials/simulate-neutron-scattering), which builds the same experiment inline: the same KCuF$_3$ model, ground-state preparation, neutron kick, and post-processing, with the Trotter synthesis, AQC compression, and mitigated execution written out step by step. Read that tutorial to learn how AQC compression works. Read this one to run the same experiment through a deployed function template: the quantum core becomes a single function call, and the multi-hour AQC compression runs inside the Serverless worker instead of on your machine, so you do not need an HPC system or an open kernel while it runs. Because the function is Hamiltonian-agnostic, the same call also drives other dynamics experiments."
    ]
   },
   {
@@ -82,7 +82,7 @@
     "\n",
     "Before starting this tutorial, be sure you have the following:\n",
     "\n",
-    "- The function deployed to your IBM Quantum&reg; Serverless account. Run the companion function template first: [Deploy and run the AQC dynamics Qiskit Function](/docs/guides/function-template-aqc-trotter). That guide walks through getting the source files and uploading the function to your account. This tutorial only calls the deployed function.\n",
+    "- The function deployed to your IBM Quantum&reg; Serverless account. Run the companion function template first: [Deploy and run the AQC + Trotter dynamics function template](/docs/guides/function-template-aqc-trotter). That guide walks through getting the source files and uploading the function to your account. This tutorial only calls the deployed function.\n",
     "\n",
     "- IBM Quantum credentials saved for `QiskitServerless` (see the function template). Both examples below call the deployed function, so both need them.\n",
     "\n",
@@ -344,7 +344,7 @@
    "id": "setup-load-md",
    "metadata": {},
    "source": [
-    "### Load the Qiskit Function\n",
+    "### Load the function template\n",
     "\n",
     "Connect to IBM Quantum Serverless and load the deployed `aqc-dynamics-function`. Both examples below call the same `fn` handle, so the function is loaded once, here."
    ]
@@ -431,9 +431,9 @@
    "id": "small-s3-md",
    "metadata": {},
    "source": [
-    "### Steps 2 and 3: Compress and execute with the Qiskit Function\n",
+    "### Steps 2 and 3: Compress and execute with the function template\n",
     "\n",
-    "In a hand-written workflow these are two separate stages: optimize the circuits for hardware (Step 2) and execute them (Step 3). The Qiskit Function collapses both into one call. It performs Trotter synthesis, AQC compression, and hardware transpilation, then runs the circuits (here on the exact simulator, later with built-in error mitigation on hardware). The two tuning parameters are `aqc_segments` (the compression plan) and `aqc_options` (the MPS and optimizer settings). Each segment `{\"n_steps\": k, \"ansatz_steps\": m}` compresses `k` consecutive Trotter steps into an ansatz built from an `m`-step Trotter target, and any steps beyond `sum(n_steps)` run as plain Trotter. Early, low-entanglement steps compress well into a shallow (`ansatz_steps=1`) ansatz, so here we compress the first 3 steps into a 1-layer ansatz and the next 2 into a deeper 2-layer ansatz; the remaining 5 of the 10 Trotter steps run as plain Trotter. For `aqc_options` we mirror the original tutorial: MPS bond dimension `max_bond=32`, `cutoff=1e-8`, and an L-BFGS-B optimizer capped at 100 iterations.\n",
+    "In a hand-written workflow these are two separate stages: optimize the circuits for hardware (Step 2) and execute them (Step 3). The function template collapses both into one call. It performs Trotter synthesis, AQC compression, and hardware transpilation, then runs the circuits (here on the exact simulator, later with built-in error mitigation on hardware). The two tuning parameters are `aqc_segments` (the compression plan) and `aqc_options` (the MPS and optimizer settings). Each segment `{\"n_steps\": k, \"ansatz_steps\": m}` compresses `k` consecutive Trotter steps into an ansatz built from an `m`-step Trotter target, and any steps beyond `sum(n_steps)` run as plain Trotter. Early, low-entanglement steps compress well into a shallow (`ansatz_steps=1`) ansatz, so here we compress the first 3 steps into a 1-layer ansatz and the next 2 into a deeper 2-layer ansatz; the remaining 5 of the 10 Trotter steps run as plain Trotter. For `aqc_options` we mirror the original tutorial: MPS bond dimension `max_bond=32`, `cutoff=1e-8`, and an L-BFGS-B optimizer capped at 100 iterations.\n",
     "\n",
     "Call the function loaded in Setup. `backend=\"statevector\"` runs the exact reference path: no QPU time, with the circuits running on an exact statevector simulator inside the serverless worker (a saved Serverless account is still needed to call it). The `initial_state` carries the prepared ground state (including the kick); `observables` is omitted so the function measures the default per-site $Z$."
    ]
@@ -658,7 +658,7 @@
    "id": "large-s3-md",
    "metadata": {},
    "source": [
-    "### Steps 2 and 3: Compress and execute with the Qiskit Function\n",
+    "### Steps 2 and 3: Compress and execute with the function template\n",
     "\n",
     "The same single call as the simulator example, now with `backend_name` pointing at a real IBM Quantum processor, so the function transpiles and executes there. The compression plan varies the ansatz depth: the first 6 (low-entanglement) Trotter steps compress into a shallow 1-layer ansatz, the next 4 into a deeper 2-layer ansatz, and the remaining 10 of the 20 steps run as plain Trotter. `aqc_options` raises the MPS bond dimension to `max_bond=128` for the longer, more-entangled chain (matching the original), keeping the same L-BFGS-B optimizer capped at 100 iterations. The `estimator_options` turn on the built-in error mitigation: dynamical decoupling (XY4), gate twirling, and TREX measurement mitigation. The function's defaults already match the original tutorial for all of these except the TREX learning budget (`measure_noise_learning`), which is the only genuine difference. The whole block is still written out because a caller-supplied `estimator_options` replaces the function's defaults wholesale instead of merging into them, so omitting a key would fall back to the Qiskit Runtime default rather than the function's."
    ]
@@ -880,7 +880,7 @@
     "\n",
     "- Adapt this workflow to your own system: the function is Hamiltonian-agnostic, so a different `SparsePauliOp`, initial state, or set of observables runs the same PRE &rarr; FUNCTION &rarr; POST pipeline. See the full input/output contract in the [AQC Dynamics Template](https://github.com/qiskit-community/qiskit-function-templates/tree/main/physics/aqc_trotter).\n",
     "- Read the paper this benchmark comes from: Lee et al., [*Benchmarking quantum simulation with neutron-scattering experiments*](https://arxiv.org/abs/2603.15608) (arXiv:2603.15608).\n",
-    "- Compare with the [original \"Simulate neutron scattering\" tutorial](/docs/tutorials/simulate-neutron-scattering), the inline workflow this one ports onto a Qiskit Function.\n",
+    "- Compare with the [original \"Simulate neutron scattering\" tutorial](/docs/tutorials/simulate-neutron-scattering), the inline workflow this one ports onto a deployed function template.\n",
     "- Go deeper on the [error mitigation and suppression techniques](/docs/guides/error-mitigation-and-suppression-techniques) applied on the hardware run: dynamical decoupling, Pauli twirling, and TREX.\n",
     "\n",
     "</Admonition>"

From b2a24f12cbf95c36b9a57b59b56fcf5734ad48df Mon Sep 17 00:00:00 2001
From: Parth Danve <parth.danve@uconn.edu>
Date: Wed, 12 Aug 2026 16:34:18 -0400
Subject: [PATCH 5/7] rename+appendix

---
 .../function-template-aqc-trotter.ipynb       |   4 +-
 docs/tutorials/_toc.json                      |   2 +-
 docs/tutorials/index.mdx                      |   2 +-
 ...attering-with-a-serverless-workflow.ipynb} |  36 ++++++++++++++++--
 .../appendix-dsf-10.avif                      | Bin 0 -> 14465 bytes
 .../appendix-dsf-20.avif                      | Bin 0 -> 16574 bytes
 .../appendix-dsf-30.avif                      | Bin 0 -> 16131 bytes
 .../extracted-outputs/large-result-0.avif     | Bin
 .../extracted-outputs/large-result-1.avif     | Bin
 .../extracted-outputs/small-s4-code-0.avif    | Bin
 .../extracted-outputs/small-s4-code-1.avif    | Bin
 qiskit_bot.yaml                               |   2 +-
 scripts/config/notebook-testing.toml          |   2 +-
 13 files changed, 38 insertions(+), 10 deletions(-)
 rename docs/tutorials/{simulate-neutron-scattering-with-a-qiskit-function.ipynb => simulate-neutron-scattering-with-a-serverless-workflow.ipynb} (92%)
 create mode 100644 public/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/appendix-dsf-10.avif
 create mode 100644 public/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/appendix-dsf-20.avif
 create mode 100644 public/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/appendix-dsf-30.avif
 rename public/docs/images/tutorials/{simulate-neutron-scattering-with-a-qiskit-function => simulate-neutron-scattering-with-a-serverless-workflow}/extracted-outputs/large-result-0.avif (100%)
 rename public/docs/images/tutorials/{simulate-neutron-scattering-with-a-qiskit-function => simulate-neutron-scattering-with-a-serverless-workflow}/extracted-outputs/large-result-1.avif (100%)
 rename public/docs/images/tutorials/{simulate-neutron-scattering-with-a-qiskit-function => simulate-neutron-scattering-with-a-serverless-workflow}/extracted-outputs/small-s4-code-0.avif (100%)
 rename public/docs/images/tutorials/{simulate-neutron-scattering-with-a-qiskit-function => simulate-neutron-scattering-with-a-serverless-workflow}/extracted-outputs/small-s4-code-1.avif (100%)

diff --git a/docs/guides/function-template-aqc-trotter.ipynb b/docs/guides/function-template-aqc-trotter.ipynb
index a1292826f88..c1d7ba0d496 100644
--- a/docs/guides/function-template-aqc-trotter.ipynb
+++ b/docs/guides/function-template-aqc-trotter.ipynb
@@ -43,7 +43,7 @@
     "\n",
     "The template is published in the Qiskit Function templates repository, alongside the other application templates. See: [AQC Dynamics Template](https://github.com/qiskit-community/qiskit-function-templates/tree/main/physics/aqc_trotter). This notebook deploys it to your own IBM Quantum&reg; Serverless account. Run it once, and any notebook can then call the function with `serverless.load(\"aqc-dynamics-function\")`.\n",
     "\n",
-    "For a worked scientific example, see [Simulate neutron scattering with an AQC + Trotter dynamics Serverless workflow](/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function), which calls this function to compute the dynamical structure factor of KCuF$_3$. This notebook covers deployment and the input contract instead."
+    "For a worked scientific example, see [Simulate neutron scattering with an AQC + Trotter dynamics Serverless workflow](/docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow), which calls this function to compute the dynamical structure factor of KCuF$_3$. This notebook covers deployment and the input contract instead."
    ]
   },
   {
@@ -555,7 +555,7 @@
     "\n",
     "<Admonition type=\"tip\" title=\"Recommendations\">\n",
     "\n",
-    "- Work through [Simulate neutron scattering with an AQC + Trotter dynamics Serverless workflow](/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function), the companion example that calls this deployed function to compute the dynamical structure factor of KCuF$_3$.\n",
+    "- Work through [Simulate neutron scattering with an AQC + Trotter dynamics Serverless workflow](/docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow), the companion example that calls this deployed function to compute the dynamical structure factor of KCuF$_3$.\n",
     "- Read the [AQC Dynamics Function Template Github](https://github.com/qiskit-community/qiskit-function-templates/blob/main/physics/aqc_trotter/) for the complete input and output contract, further examples, and citation details.\n",
     "- Browse the [Qiskit Function templates repository](https://github.com/qiskit-community/qiskit-function-templates/tree/main/physics/aqc_trotter) for other application templates built the same way.\n",
     "- Read the [Qiskit Serverless guide](/docs/guides/serverless) for managing deployed functions.\n",
diff --git a/docs/tutorials/_toc.json b/docs/tutorials/_toc.json
index 593f802891a..be43d1d77fc 100644
--- a/docs/tutorials/_toc.json
+++ b/docs/tutorials/_toc.json
@@ -63,7 +63,7 @@
             },
             {
               "title": "Simulate neutron scattering with an AQC + Trotter dynamics Serverless workflow",
-              "url": "/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function"
+              "url": "/docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow"
             },
             {
               "title": "Krylov quantum diagonalization of lattice Hamiltonians",
diff --git a/docs/tutorials/index.mdx b/docs/tutorials/index.mdx
index 745c01f55e4..b9e9b052187 100644
--- a/docs/tutorials/index.mdx
+++ b/docs/tutorials/index.mdx
@@ -51,7 +51,7 @@ These tutorials focus on estimating physically meaningful quantities, such as en
 
 * [Simulate neutron scattering in quantum materials with quantum circuits](/docs/tutorials/simulate-neutron-scattering)
 
-* [Simulate neutron scattering with an AQC + Trotter dynamics Serverless workflow](/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function)
+* [Simulate neutron scattering with an AQC + Trotter dynamics Serverless workflow](/docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow)
 
 * [Krylov quantum diagonalization of lattice Hamiltonians](/docs/tutorials/krylov-quantum-diagonalization)
 
diff --git a/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function.ipynb b/docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow.ipynb
similarity index 92%
rename from docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function.ipynb
rename to docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow.ipynb
index 50e1a675ad1..8f6591144ce 100644
--- a/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function.ipynb
+++ b/docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow.ipynb
@@ -541,7 +541,7 @@
     {
      "data": {
       "text/plain": [
-       "<Image src=\"/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/small-s4-code-0.avif\" alt=\"Output of the previous code cell\" />"
+       "<Image src=\"/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/extracted-outputs/small-s4-code-0.avif\" alt=\"Output of the previous code cell\" />"
       ]
      },
      "metadata": {},
@@ -550,7 +550,7 @@
     {
      "data": {
       "text/plain": [
-       "<Image src=\"/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/small-s4-code-1.avif\" alt=\"Output of the previous code cell\" />"
+       "<Image src=\"/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/extracted-outputs/small-s4-code-1.avif\" alt=\"Output of the previous code cell\" />"
       ]
      },
      "metadata": {},
@@ -828,7 +828,7 @@
     {
      "data": {
       "text/plain": [
-       "<Image src=\"/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/large-result-0.avif\" alt=\"Output of the previous code cell\" />"
+       "<Image src=\"/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/extracted-outputs/large-result-0.avif\" alt=\"Output of the previous code cell\" />"
       ]
      },
      "metadata": {},
@@ -837,7 +837,7 @@
     {
      "data": {
       "text/plain": [
-       "<Image src=\"/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/large-result-1.avif\" alt=\"Output of the previous code cell\" />"
+       "<Image src=\"/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/extracted-outputs/large-result-1.avif\" alt=\"Output of the previous code cell\" />"
       ]
      },
      "metadata": {},
@@ -869,6 +869,34 @@
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "appendix-md",
+   "metadata": {},
+   "source": [
+    "## Appendix: How the workflow scales\n",
+    "\n",
+    "The hardware example above runs a single chain length. The three spectra below come from earlier hardware runs of this same workflow on `ibm_pittsburgh` at 10, 20, and 30 sites, with every other input held fixed: 20 Trotter steps at `dt = 0.6`, the compression plan of 6 one-layer plus 4 two-layer segments, and `max_bond = 128`. These are recorded results, not output from the cells above.\n",
+    "\n",
+    "![Dynamical structure factor at 10 sites, a single sharp bright peak at q = pi near the lower bound](/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/appendix-dsf-10.avif \"10 qubits\")\n",
+    "\n",
+    "![Dynamical structure factor at 20 sites, spectral weight filling the band between the two dashed two-spinon bounds](/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/appendix-dsf-20.avif \"20 qubits\")\n",
+    "\n",
+    "![Dynamical structure factor at 30 sites, the continuum resolved more finely with fainter contrast and some weight outside the bounds](/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/appendix-dsf-30.avif \"30 qubits\")\n",
+    "\n",
+    "All three recover the two-spinon continuum, brightest at $q = \\pi$ and bounded by the dashed curves, so the physics holds at every size. What changes with chain length is a tradeoff rather than a straight improvement. Momentum resolution sharpens as $\\Delta q = 2\\pi / n$, so 30 sites map the shape of the continuum far more finely than 10 can. Signal quality moves the other way: longer chains mean deeper circuits, so noise accumulates, contrast fades, and spurious weight leaks outside the bounds.\n",
+    "\n",
+    "The two halves of the workflow scale differently in cost as well:\n",
+    "\n",
+    "| Qubits | Classical (build + AQC) | QPU usage |\n",
+    "|---|---|---|\n",
+    "| 10 | 4m 3s | 14m 21s |\n",
+    "| 20 | 24m 52s | 15m 58s |\n",
+    "| 30 | 230m 57s (about 3h 51m) | 17m 39s |\n",
+    "\n",
+    "Queue time is not counted in either column. The classical stage climbs steeply, roughly 6 times from 10 to 20 qubits and another 9 times to 30, dominated by the AQC fidelity optimization at `max_bond = 128`. QPU usage grows only about 1.2 times across the same range, because the circuit count and shot budget follow `t_steps` and the twirling settings rather than the qubit count. At 30 qubits the classical stage costs roughly 13 times the QPU time, which is the practical argument for rehearsing on `statevector` or `fake` before committing to hardware."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "nextsteps",
diff --git a/public/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/appendix-dsf-10.avif b/public/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/appendix-dsf-10.avif
new file mode 100644
index 0000000000000000000000000000000000000000..66e03e792b3476487a62d867a77cb6115e7c1397
GIT binary patch
literal 14465
zcmZ9zV~{9I6D>Nn_t^Fx+uCE>wr$(CZQHhO+cxh$=e+OTh+7fexpHM@RagG$sHj)~
z007v=j&8Pk&Su5{e}|QsG5tT-%1rOC4_IVn<f!)#|923W8d}=_2LS+V%?up>hyQ1V
z%`9yU{&9r(0Du7i|3QFb|5*S47IFZfe;C%g|1kgpf3yA$Hpc&-k^bvSnVDJt`zilM
zQT{qc{}%q8PtTc-{-28fOa6`gza-#4b$?v{+YX$qnWNP|CI2o_V`gh_`xl3s*&5jV
zwJ>_l^n!jMF#p7n%^Ylv{s900Ko5V@&;J4hTQgU)e;8m02#CMt|I5ezhx$A7e=)#+
zB5Zn2u7dy2PWILuR<=ea|Goq`49pB{IUL*^9F43TIR56CS(@qDyK(4Q+M52`Rva^1
ztN-;E2mlb+4+t0l2qX{)H2Cja<Yx9p|8?T;a(|00|8@}Y-;KZ-7&;jI+vC4oVd=Fp
z)N@1vfP%sxrKOb+0%5{LU`Vl)40UzY$DEmj0n}odNRl>KK;ydw>PAw&O#bEAX1C&<
zWfs}WUcjINb4Qj}jJ+J)ZRVYR87x)a>%#%`W56B#Z2e-`CEDTWvGwtUf2_5Jbz(zW
zQ6wP3wFM(4@A8JX!OIx}xtX1!bvmR3zo(g$?d}NnB_Zu}dPz|07QwR<To2$CpSrkd
z*#Fh5>s9~ZEb?T2KF?N(<)>_|q)Y}FJ2HxE^fk#vlcyB-x`z-ZBt#rCU9I1`^FD}+
zMKIH#Ub^>^S@-DF(fRg;-|jNKZM~})Xn5q(aZ()V(!(ti3c~)_?x3ViJbi9zud-@5
z(P6E-Xq6Vs;J0G1y^0(N!YJ~SQe^M`f{cJh9GJ5_0_&^LD;2F4F2PDS=h$`}3|*9+
z=?2idZ9jWhYz#Rn0y5u+(z}!lPdK4t%ytg(B!3cpIJ*2<UI=XJ#OWci#}=~~MvV52
zzp+UW&nOr*q_-xCHK~P(ZEqHcrscltc(7fKcu~K$w{8}Ksf(6d!A{05pKMCJR5xgv
z1O%x|_TuwXfZ&|!qX^azUl{Ifbn8xNieay-9pEu>mSH(Ty12XyCA=8e_(=`Wq3!?+
z5$+II(qV6dd$@~hw5}~QDOYQ=Nbkgd>|>IZ9jZiABWx_03l*pwna~t+7`8D|MHPNX
z4g=_lq0*HXnFRo~>E#*vTmLkwjB{k40yb{`HdZ<5yUvovd@slH+s<*7?p117z}383
zES}e^HJa^M0x-s;7i9b4!N-h&&>z$6W^8jY%lkCyJ?q`B+_VKA5&jf~yf2`tHIQgy
z#gDuuP-8&YmN|2}@s0?M4&uUFrbr1GrtN1I)<bE!M%$xWobKU$vJRf_!9|9kW5I@H
z>d#yM++zIByeJ!`XQIsGW%sv2w|DesHI^&lrcT^BNZ$^6PDflMkY~Omz*GW<*uKQC
zloACiOX(^H?$~Dc_41awSK-=>jL>U)vXfYL)=#%&^&WOuaQYO=EF<p>m4c@R=~kUY
zgYk>2GZOB@Z_XjJQA*uo1~&c_@`N(E*EgQ^S!=!90Q(1EyKsFAguKbKVRqoX-+gV>
z2+_Ss<0#VT@N+yEqK*r#J)MtvU*4IsPiR*rQ2h>m5K#KQ4tA9|ryBfM)7F;6gnKu<
zo;L9_Fv9^8f%>qO4qWD13}$s!9#|Lah)a^jaB=YZxN~5uSIVD13aZus#yLuET)m#i
zD1bGczA~rw;KiMIGUkjH4T_fbZy`65v3xI;O54|p5L^Atb4wqb?dafz(22E;u+9tG
zKcK;YfCTyVmLxuiLC;ctLGq=nIMhL!#xQ~{pe_w;9?tX5VzJOS1~lB&=x{3qLmGCG
zh92&hynUINpRbC+^E5Rfk_eJ}p#kh${%c#h!q;!M5u%NPMsQy4kDt?e@Op^ep;%h)
zSe@u1W%1PtgBxK=HBhb~-hUV*`uXK&<rM?3mY0ZKC0RCZk5>l3H%Xhx0&Ks0<+8Kq
zcEqt_Tj6C)O)8NUWLZFiX!nS8b@-T2ybYIi^TxG^J+{h3F8I*zyBH!^I`9P#i0Ii9
zAP!d&6?xA<XFzIP;%YR%;`yz?h(|ws6mvKxA3{oH8IF~n{fJM)ly8dKhf0eW^a7+w
z-X|PA3dnaS1@XM`QE}Gs;C?sR%$z#o1$|IR23p|>CpBxVJo?7CZz=T^O=m6ad)zb5
zqCxZK!%O%?5SD<U;hrC9zn88GI(d=rq-vgkI6=E*l%hsYgyU-8JI#*^mc%I4CwjU%
z*Ujk*DFA{X5=8?S>Z+;s1XHWV6;S`Q<n8EZCjMdX%tKYNlV`Zdekp|ud=Z`5#&BcB
zS*h0h0xn03G{}u*xq_5wQI^k1ZX9mU{#{Ci9WGnnRo!Ihy%fXKRf^zv!c^oxBzK=`
z&kCVCmd&aQ=YF}IDmbym?B-dCH*YbU!W1=?$lR0<Y6jQ_*j|&Y|0+#Ew}JXOHQq&n
zj|76>VqkCsL+2lO-=+bFFq=*!$PpehjH98Cj3arL7Po&`c2_g+ig3Ev4+W%yo<f7J
z*iH-v51yi%J>du;?tJu-AdPEP{85k?Ls^#SPN@Mj3-BC~wOq`2ZC(qmOKjF~)rGu6
z>GeX*27<(=GPXUZx5Lbn8oXrhD7T~h7MT&qSYp{#sWuP)8O8_aD747?@{XPAb4k$;
z78~!eA(0RC+3i+-Fn>sO5=o8tOc-%8DcI+pKxaSMaS|+k4Z6kAaS=*Cb+9}Kc`K$c
zM%v4I2*1gIu)!Za_4wy~sO)JHd}dfEofDMc!sQi_)GvQNUy1tgOu5Q=OXq}vnL~?5
zTfg`PgS?`S;Ma+9E_W1#i2RJ44oFqcgP2W}e;C!z0Gz~dp}Vo2#Yk#}Ol>Tij@`YZ
zR-YUs<dFo};rM$ebhVuuY!*AQ??8CDqX=LXIttcSj>M_(O|>{40*De#o}0J?8X>L*
zLhIGjHZ~CUXgh<4&qA@ySVIK>egNtYw=dc{SFLOs0KJ&J6IdREDoTn(Y859}Ty#4#
zuTgZ4K71#dQ|Se$)5{)=`v~np@S%voGS43<k=E<e7Y-;ElqhJofpGECL-wS6bzOsL
zNSZ2K!II2b>aRYAu5hExvaodMjjeaPo<QHojN}|1zDjqpWWcIJ>PO_|Cp$3THwq8@
zZ`nVW8k^-jJL3L~C|tNH%>Jsyc>xYfwiAnYHry*%VdMZocpgE;CnVWQ<7l(^^si@n
zVCe+VZqN9|$1=`{-4pC#T)hW;bL;W%2KjbCtuKo*ZLYUKJ6p~GAg64@vw3)Uu@!tI
z*kfzdeZa6WewRHR+kriRJy%ft0UuJN1X@60eEQJ%ILfJ4&5e`p-V260t;^WHaJGjP
zw(+!|r$sn74Zx0z8`2XIXmcB9C~N}6mX?vY+NZ64=gQ~$dT#zyOG|l>H^o|4_|aAH
z3M3!uDOlZ3c?VHe5N0-_2o7KUm@sI|6hD;3dAT!VMOV)}K|)FnrTSaW!rTu%DuXxl
z40AxFRDM?;A}2+CbWPTRMIq(ca~HS$j|?ZWy}cGDA5tD^zjQzDGxrinmqD1!wh!(5
zu&>Ridl2!W%*SUnVtT=lHFPUpTk+yshKUfHsla}tSKDagbu!eSG!s&HAZ6bnS=^B$
zujAiUD_OcYZa*xT0!~b|cGuaV(s$zHY#V|wVnJ>hSyClXYogUPVk!NSJ}n$IJF{Xt
zfW_myaz0MHWeWb?ecO?M5h_Re>Fuwuv@6eT*@T0gzZ>@@{7ocs1chU+L_@hD*S<AJ
zl{sr~3JFxTnrXd|eV|z2&Y&odatxFkTXM7N@^~U@1D+d>2*p(9sCtST2SHviqCO1W
z2dRDbQ-c}e(bqjJ3NC2fp@UqTc2KZJQY2CaJxe1kabMgw9eTavw($d11@GD{f{=JC
zB{ly%SZI;foVBVLHA{6ue#Pl#<<6jTUEYQC;juzm{w9U!*h3j%jI;Mm%+^3h8_1oU
zS;f!+-eCEjkPnSPfA|5|oZr#xY#4srKuX&;61>(VibyTKzfteiy(}3L=Yz5q0$MW8
zhRll824A6!OfwmtHqUPcUd6j~ky4`TP*MNc@@U1lP6E1xl~5e`ey}zY!Vx&Y0~nQ;
z%~^BaM3S7VTW^!TrCN{$j<tRLeb9R7b%NnLKem+4f65;HL64g;l1pUAw`}MBmJ!ZP
z`@1tA-M3vP>9JAolUr=MeM0X+n5@f4mn4Bde~2YcJtVr`-@y<Yrxc<LaaAjzX6ey}
z;>PqUMN=5A_(hNvRAs3p5Kr-U0agh8xdDRELA6xZg9+&+)p{~;)l<0VAGHG+CEojd
zC)wvBf@M7%_}1o>2Fp}-vd=`&3qam>Wg%rE1NGK{2s%H78tWym!pFyTWBHEXg?@M_
z=jY|wCntp-b*r$0hVVUKM;0{0f!9z<H~MDFam5e|M3lY}R{WGOdqi4Hpk!_A92{~_
z8xC*mArYs{x0mX$o+Tyx^eq*Rcg0!#Ci;Cy9aQD0R#6V`DC;>kX$LdjzY-7nN6j{m
z5tPX2r$4E>aW^-i4k)u-KA~md-d#~Y4?ujxs2%+rMJsPIff{Uuyq*&xRI?=fVq7WN
zQi=rTAD5yh=3>mR1yfecb+6;Os0s3V%pWkj+QDcyWV`$bl8CI6P*|U8*S#s2<RVtD
zBqX_woMw)cO;FBbJ6dE0=`Nb!<@e1=aan*5gj5T`XK~fH3We_OqNNtI+$sLpTg3fx
z8cM5My*%XPcM8w!;qdO#B%!-J*Ff9O1n)U-y>l5xAE@0?;Pw#92e0J;E^O26Vrfa=
zJmQL8XN?Y1H6C!xkvh*t41=s2>806|&FL2#9b)g0VNAa=Neia~p#Ke#<W_5fYjEDL
z7VLfxmPoblrhwfpFO>VK8av4sejUS9XB0<k+gts%!X(ld2@o`=NHsBxP~(OSAB->;
z?E2}iYGDMmA4>zB>ZkrT^#upn5nXYnO=;cwv{3%IZDl$%1V`y-2W9cjOPl32hZRa4
zVHw4kZhV+aonHPd>MBQ8uL_YtjH?whQtzjeNhaVXF5YVNjT{DGd_Myy?A-_4(8~pH
z=2Wk~tx&xwi@Q~fNUd5*PE;_MY@=0}rXfiXGtdip{G@T=7&DR;8Qru_ew&f2sWmbJ
zUis~Z)*ZHlMacvygcu@vs%i~W0lx?c$xQ$(D!W?;STB}fnK#8?obE9)_Mno#mC5S?
z#2#W-MXunn*>n1x8-Aw2G#$c{YJlDH3XcX4SPEGUzpe&En8XgCN=IziM=~K3CBHgR
zX}Gubo|ld!c<IZqanX3#nA@Y(ea=Nnoud+-Mp*JnD9u=b!-Q~eE<flJ^tICN;Q+=)
z&a>Q=wpH;VtF6;<7Z|>TqZ*RsFKawKbnHTd=6i8m0>kBTK;QHC)KXCsWTY!oOdd<p
zL2l#zlDj0OFUSe{p$9^v`8p}35;gjfECW!P^u{^R?#nHH0%FT9`DO151sMTeq+THY
zF^{#Y=?83;I1^9Kqh3m|AFE~yIpM4&QOY3~%{%;p(a_G9aBi;$;M?f^KK8N#xix=I
zLDi-Sq}h6%LAWxfUGA|SMYn*^`*Stcte6wWsFz-_QJX4Q=Wgy<IO<5to*6tXAYgTP
z7iIy&)CReCsYG~FA9#G3C;^8=ovAf5yXq;WQv8z;G@`rPyMy|2Uo>$B8>U1k^e3Ak
z*!qOYH41#~)Dcvt-<WsB{a%~MfpPG3`&QsdsOkI~xDSBB6Kio4D$BH?e>kRChmxNx
zVbbSYs--K~76)?Qgsbitfp8*lB@`+djr$F_O)`%nWyGgb-^-vTV7M^~-9QTnui!eq
z4om%mURI#1eaJZZ0gG5<+vQGsf&;q#H>LD7{Qgdd_D&#|F-Ljvgj<L$Q!e%vg60mc
z^rJ@XJ>5ug@YO!8l<@vMnZkZ_jhD3J78EpeNSypBMJ(jz1rgP14#tGmOkPx9Up3jN
z+v$<)5b(yU`z5Hmbu)pRM)?>k4jlRxaJpF8%b)?FG!BV@lZcraIyv2Jp9Dp=)G8#F
z-dg6U(ayF}!MpDr-t7t(uqfy3qdLbgeF*+Aa>=X3so1dQtoCxxy<Z|MDLL`rOq%DF
z>#FCmGh(vJA>ws){b3#2Y8f<ldY-?T7I^Zr3?;kkPQu20P34xndG*%ne(JKod4;`(
z2h@KoVKk1&K3yTAfGVRfieCo=qLZkG^vOd>Vs0>SkEf?RkTD$ZN=M8f6l~X&RmEQ!
zOu(V1gRJA3y1easkU1x4GRrCtKYkkr5UNUS)<7`p;~8KxS7)h;VKw`wcQ|k}<`<9n
zdN4xWPOvr2oTa1F<2&-eG20EEBDMwhPpfW}aWkPGPq@F<q3OgR#z=6F4WlBei|+%0
z3le)Y8C_zhjg5pmlqLu1y)LwJ&%pB!Ni)NpcqX^r0e`O)o3keRKBAorhbqGQ7J&Dw
zkiV!!8a@4zf7}6h`U651K_cnhO<A;Oe?UO^qwfdK(g{hJy$gJ=B+dGponqLzN)0S)
zFW*SxXviiC*K_HyU4?GI?>zP42L&M+1IyTIR-+k^If{<nDkUK0CsjM-`6*vj4%?C*
zmt&Q0AJsKbioGs7Z@Kvfc7@47PfV^MM~_n|srU$11Sco6LBc<nia{YQ$pD6AyXa3;
zgo(BXtp;`6*r@WSE=l=IO3qQCz_TjB)*IP2@#-WF+`0R~w2-H1;|@fE)6a{CSKJX2
zs-CXGuLtVswmq_W`}py8%(|lpl3{dL->KbzFh05&m!EUtAYwM>wq?G)(1>q?%$SfY
zcL~Updx-FQ%O;K+R6+KXnr%3x!n1<3n*N<!=gD9FGHbUkJM4c#QxT)yeh?oL?Z)zb
zXSCqAtgTdwLy)MwI!NjA&@d_;4V2@hJgURR#lGuG%($@$DzCNb>#2uHLtq^-jK!(1
zjjj&5^L#+q33X*ajAWk06v%9=Ry;Jhi;d~H6x~{PMSN#xHs*0N?<|vvW6*!UpP%&b
z#}mB5Qq^FqxtgMX1?i=j*VSa>aGyK@NC3QWMJWkfCRi<~lraz+J05{pxTEcp9~5~T
zBmou3m%Zi8pZg3<(CNE+K~Sw^aNCStQ_%@&1RCd9iQvriYTxNNb%rm=$D;HO@Rh|w
z_fjRxkHZGog`4Bd`{u7)0#8+3__f>U_XRqdlnt76#5D$@uY^0h7Z&=|gghE+PSk|X
ztgM?vp&UzLmiPv-WsX`fg=)%m5-jK9LK-m<n*0o5MWCc>A_2(OpJJ7>Ezcgi>`D2J
z6$6dYk*yxfh#2PTe<qkdkTgnuVtkuhAhUvdd{G|^PfNiCPqcu0Jwgj_1*+%mNtM$3
zT`j1sVoDd)VhlSB<X%FFI0M8)^DK~Y@X<CN7%vz2acFbWFKF62&MfoZA!If3l~KK<
z)N^y!uT(U>fXdZ63Mrcq?Lx)sVuKRXhNKQQ8suwliE(I!pK@MoHZiKC^hrJb>5@_z
z0-vEFx7APO$qe6twCLJH^<^n^06m3dMdK+{#r>fXkt7k{pWDvlP@s_QmepVTX#Xi_
zK;sZ#&R7)ya8BMek3bgyOe+Uu6V`)<097ek9Dmckk=jU3Aw0U=4ALho!f-x6jPz7!
zl>`dMX#4F0^GOl%sE~($6GCT}^vqNEi9G(Rbzs}IWH(bPb8h!F;Mn;w_&$&)^5ASx
z$q&X96{k^NQ!U4@$L38kWf>%xg=tha1)g#OWqPl2>{ro_6Q#A6>VQS#gLxy_=AUzi
zKNF<2zc~(0p25z0n;^zEwJNPI+wQFs@yT7La(jfR)c{CrjqS&pQY~Jgu_G|i(YGo@
zC+y(Fh4khoZP4=<cdvl>Fzr~r(SKaC*2K;bbzOD0S8z|Zha-%Mt(XFqBa+Q)oa*;4
zCVy#3>nJ5XJC(f^>Wf;IEeGj4o@yf2wF5ajTc5;jxUdRQe69l*Ooc~|rj`oQkCT_0
zEKSgf=Y~FWG+f%zhcA@bDpVB3YcdMiU{u(*Nr3{Vr|Q9qnZ{!Ra(WiLxx)~n21sY3
zSg=*wDQj9Vo?fr`LJi)M@@50;vatIE`Q%DDozSh5%n~mbBIaX!T6CjpesA<7|F#tx
zE{Na3(Vg)VN-$~>JqPl~cf8e)zE8G8p$9JeNh=5+P9J6X63NT8NVd<|=qU~OS^uu~
z1608+cy>n-SB$w!tbRgT$ipjW5z+kcz7M(x<U_LJ7s10?1gnBd*3!x2wQ7<XsaeLD
zxW@WfS0%uioENa7{uScMxLXY!T*&LhyWgqnAn$jFiH*MjO2eWQ|3@+hFTZG0i#pT+
z(1&GrW{Gu1!>QiLBe>_yOldCO&g+L)#B8)+Fu*Ae_Q;KPrLj^A17^&uI{sm~kx58b
z*fJIZ(g;lXTO@)0*sm@j1+_X`I|99gVfOF}RH6y;YRGcWMc{f&c*wc}@IwYi>BorG
zA1W{W_18Wl@#x@&XG_aOSqE}`KSkDjvh7xRXz@oXe20K09*SUV8#dec<lD>cIW-SO
z|GQ#Ng>JQO!-c+@;O_aCw1hyHuLO7onG9yK=vQA#_|5co3@uK{BR6z29YL$*nQi;4
z5brf5LZv;@>n97)9=_G!t!SbK4Y|E8*110g&nq83>_MT<i}eM_(tZ})l&;Xzt~)@k
z5fMCg)XfZ=Gr}VFtiUV?2y^cyPZXAtQzrq;`ejbz3-LkIOrQHt7A8(Ph^|I}?6jhY
z4Wc-``eg5UygRM8JC|vPX=vcnhGUQigZ8movPbz|ck-|=IC$jMZeJJ-^^_ZRj$z7t
zF+0{Jb8kT1!H(4C()O9d>I*GyuADHONBSb9vujKZ;$rHa<pzOxR*yI!kOM2>4cOkx
z_%Ye&8qBM|F2Zz##0oc{`@8580<2Yrku|0%6kO&K#xX(<sJJiS*6Cv6!T{W=i)e2f
z&zr{uQ@S_`nC>aDr7ly_mAn)?kLz=?I<I`gy-uwuI=)cAd|&Vz+bN8|C<;Zb7NECP
zUzxTVfSkI^Zd~v;G{8+W0myL}*|9!y9BJX{?h7YX_Polu^2RE*2YeJ05Ww!v%aEIf
zaxkm~FTiC_@Zo8*7N>^S=(cjO3L^!lmlzdjo4^)?I@={(i63Pq?S-}FdfSTS>3RV+
zz@O3rOGP3(102%XBAEx_qBc%GrR(8(b4aTSjD}H{7g0#UDuloUVj2GuVZR+|9c{=@
z8QJgqjQAq^ht9M6kZGL})~DfjzDGN1j=IM73HaxwQ!p6O0DC%8_CGKz)IPM>E&&H?
z5*6;x!GO9}U$loadth!K1j*8Nd{5pjteF^tDY8YxjZa?)i^c8qQ^$t2PqRKi%Zy(e
znw>+z*J!8SKipjTU0uz)!arHHn7@(ENHFa=`RuyJ*qB4Kwsn;yHGw0`0#RXkPg&E7
zd&oBAfl6%P2$!3Ekah*7-Mhh;bs%LkS8}Kyu+$B$n^Dt5R(ZBb%v<Nipq>tgL8}x?
zncDBW*Ilw3{*+FC3fc*xP99x+xKQVAqJF9YUdn%CF!Ti@!YUlO1h@HW*?$;OM`J&f
zsosVd;lNT3ZsVx6cl@-Pe!#3>1@yk3@+DYBc}(=lH#C=yQ22YUY!a*X8QNJ#=bAjF
zK*xn%W`Fnm9yK-z8uu&I+>#_^nKBz<&+V8~y_HFH1tazmuCYG3JXiWfC_B|Y?$NR;
zjF~r^^%Z>m0VY++L@m*a2se2vsp#5Xc+s%Z(y;;7k~@0k|LzNDI?X(G?ym!>e$A8K
z@rvIstVT!X?NZ<ufvHxk!a0BgjASjhRtX0fC0t4;Me8}M9Cs0M(fT4H{tntYpCsmk
zErU<zO#BwhafJ1gAVr~43fPQg_~}1^A2Og+I}NN=$jQ%^?zu*Lr~Q%1G8~y}jx-DH
zp1IJKUu&rqf}j_s+iU{!GX*ef{%&8eEo_5)zd`h}Ws<?kFvuhv>5L+;{D(ik2uI&U
z3W?LEF6VL&({8|E*?afNpTHrS$2l1O8{U`GuR~z&ZizZoB<kL$Xmpk(3%$0dJze&_
zXv@Zis+@W5NPTx;ij+INAKB{FI4Enaw>3w1$AES14DKFuyVgpEI$<q~{o8Nw0yLbs
z=a;)dZDz*LAU9#K5_k$nv6$tJ`$z8`JECnO9>b&YuaOJxMGHUvTuC=A=l0ov3c=12
zo<vJ_BkDp9;1v4OO{$l*Pv~wz2yDDOd0OxMqcHfdT++s>2aqqoXQ7RLjErAhVWmJp
z2S5<d$Pvx6?nQW6faM^CxVt|elaVyIh)OO$6n&WPDbT(&!Pzi{h{V>3tv-FLJ3N?`
zWcd1-ceW@H9cVr3v5=`tdkM_UK<^qr8D%6vmygobjCcvDcyUeE?FmHWt#!DNP|OJt
z@k9o1vc3Bj()psd7K{={1YXX8DuaSHN`Aygh4w3`o*fQMF_~BQ9d(y1oY$4f9o4Yr
zVPl`_&n4?*!?o*jB=c=~;7*2_8?rb`&#FY!G4s3g0omxhcoT=yAl{s_e-OIO{B_iM
ztaro6nBi;O{EYsvH@()ZgC&~RsN?Y`AnV#LcegK>GBw7hDcclmM&VT!2Hsg3kSI-!
zER^A8zn+WO9+w|TxJP!U&hm^*1E-zOp`ASz_`PUskncf%tNM3cwyoX+@n(j^3ARji
z=_P?a>%lxO;mgsFI1D2`;H0IkMXo>Q4n8QL3<V^dGRE>7@RvqiCOKOQ<&)NjQ1JA3
zSTe(cUYw85<6>#OKq{Mr0Kkb1MAGgmRHWjGod=`0q*l2ycaO0Nmfs5HUEmRvdJ?Oc
zbp|*Qm%i#NYv<NhI&KeQGDI^+SdY7D#kBl(d=+<CcB6nCZScZcYBKr7;hC|Gc-TXw
zzMP_!(YB1K`>9%a#?jJgwBSC%^V~$38)5qp1tfM1;HQ%XHntL@iZkCJ-u5<SynG5J
zq9j>`_Bv))wf4N1Q&?wJipo2}hMmFFa}dB>_&sFN<rTBDrQ+B<gPL6}hVU_CHyV&Q
z1+t%-FLloR9iWGWqbw>|d(Dv0VMnoSeo9ul(Kv$~EQqaX^i5-hYJIctE?5R^YhVBQ
z4k$RQe@h0kKc|l*9IMPD`Riv~epo%qe2e_Z01&F9p69?yWJdhqH&DUS-iC>0ra3F1
zA9W(hO*YEC-dIy4&5Mu<Z<R^wDE+?s3NwIj9dHe=(6;0u5d0?EpQz(BzAzx60CZ*9
zGWeN2h!Ixa%s1Y+$xY#jpUPJm@yPj>);CeSExG#Xmb=Z=HiWTcsV+yc86~J@eG<|w
z--5?X3N2?4ytH6Br*Nr^bY5S`NBMI;GP(s*IQib3)$c!-LoDy@TkHF!n4U$RW8^!6
zJNuY1y*)Udl%p#ZFsL5j;*VuF;~n+g?)RPL9iqRy&h7x<UDICRZ;*tSuN)i5l+^IZ
zUSS%PX{$<^EM7c2t%ampl&!itrK~&v#|5jkIsK!Vt-z%gsf;Wn*Hg-Dx>a3b<I~!z
z+Pujte&>NaBMzYux}tQwF_1+Z{m72+b+oa#n|XCwzJTTsukjOme;|iLCz9CNBn7aX
zOqWoTcT7B>Y_^}CR0N@?cqj2zUBwY(!Q8DA2;Kxn__W2B)~c=2fQzvD-H#zD(dN^j
zL!1bZ_LW{vvvAu{9)(wY)$vNMr2I<WZaPD}E(cFGcr-?g@l6fDZ09|gnRV}xjSDV5
zGGSZaomR8DOJ=x<kwl;ENs-9etChj|aEixN>D0wa5`K_H?r_?sir#5KMOsyUW2A94
zK!8IvzvvUZ-x(G|h7{M{_aKJCHWB}|hSn>^Bh*ErRCa9u@(XHKRh|qMo^?ht$bf7u
zoJxC)qnLrOj^;QNJxeR`tq^~!^qr8xve^@&2{@%d6(-YHDw8QOWUYn;{stwwQpl=H
z>$RRXt*eL&&~7wT__u{t%fPxq<cauWJn!<^AEKwSGDTcAum1W7Ct0-<l<i*BZ`LCe
zuXoyXvKRgNt^~&?)YCJdWRdwtjc`&Z$Svo<5!4XyDR4?n(?W)@E*msC9Efw2l@4kZ
zT6%!oB*g0`Z4<L0(k?*gn-3Au7c}&XO^@uP@<<>kAjO$|-I}m*lTY*(KqINC`bbP-
z(MF|CxVB$!t~kIuQq+R(9>6xQ&I0tRNHd})>8NZr;1fU=)!*#955<C5?9yMz>5BUE
ziu`N}TAX9dRfgywZL)7C3EoeJXu5PmF54XRp+R2*^`cUt?#Q7qyKi7VmvEOmHV*QM
za*DOT1#ZO=EtE|hnl>1BVL{sQPIwpmFni^DP7LecZ@Oj5Xnv88X8*nu{mduiqBY?B
zrCl*SnY|JB_vd@enY<n~<tOVXQhq3wZ%v}2Zf4_cDDfeE!zX|d>>tKyNV`?rl8V2a
z8+RzY0Y;kNEOwtrHy%|OxTM;XD`Ll@KIXbILppNXyBMYLE})K?E+zPF-npTttp-+z
zRDqVZ!;36&wm(eWw{)>~p>w}TNX-83<<M|*6-?-prQh_N$RP_0u-11kqUX9_d0Z(@
zEw~L6s^K9)My3D8l#MY<Lg*<ywkYI*LD@b4(1{VN_H3)`MBNboiVZi=5ncg`KK#61
z%Katht_`~B9OiKW(V{3<%ydB49Y^jAogO*Ap&;-hu?6SxHbr25mZ+7LF-L86ZLxjs
zVYH+Cq2N)(Hv^lSupio{h`K&l7ePI`VSK&n14$yCw!Y6UC_uXO^Zuz{hWiK$J6LOH
zk58dcKYn+P{Q=d{w?Ly#01%Ym%lPPh@rC5+oj5HfERJ(uenzzVOfac1B#9q!?fXrE
z=#3G!anLN*zh8felNpz8Qx^!t8{cS8yL;V0N+uKt+cvXjg0HHR6{4@-NyP9LkybX|
zm4^2lsUudb6%7@p@PtcOSgE2E^AbaH2<%eil{$8wmUA8F3w<!n>?ET@@^n{?C4-Y<
z|C?DICN#Hd2%XXZCEslY21TjnZncmPE+wCEPQ2lcx+<oOG+zh2I$$eX{K1P%gAUu)
z4u3=Pu8J<|^IrVSST`xuC4F2{27}9L*#OMD@w2tEkvT8|bHeQhvOTiL8nHjs9A1*C
zXzME%c;tjwxro$r+4XBjw$6r-pY3?;Ewve0ePL6oPzZ^JOl&I0$3eZni$deP@@}Sz
znXwehL(ffWAnrB*zKs=RwkaneWl}oFB7E^N>fr|qf-x-v6&HRY5Id>TmM(Iw8{s7Y
zuwwCplet~%WdDHYW=i7*>?O70t}lpC-<qC#B3qT}skiy|4rg6?7D4``@UC-MtPm{V
zWM;?Q+94QY=FvSfeyp~YZX^`Qml)hB4t`>I8;noEP<u*06Fc_A9ez-=4y!N|Pqc}u
z=o2$3&@1`jAq+jodW+vKh7-N=;;>G^<uSX+7Kg(i8Oo5q7|p;LUQT6ehxCJtpklM;
zo7_R^zW2{CAp}POOHft&ZiK0ZXzP{E8fdTlbIuJK6_5Ytj*P~)B6TEr`PAB?ZZK6+
z4cX446a!Dhk09zv%nxgq@)_)8{@$Y3&y7N|oJ$^!z=eKOa9T;~r0i*V^^i#2p|v~*
zoPyBgA4@h&;F%&p?D?uhzw)B-tj)Y^iq=b9^q_=<D9g2{&zjrcSBMf(IKaZTmlH-c
zgO@ToKgg4KbW3B8c`40=^MW4^oPdllk$kl6^sCd+D|)z(!!9me`@EIcpe3N#_3D)J
zg^>xJ#H|rsZ53F_#V-xl1Xw&F&ZKN>L~$csMvmSW4Qq`=K-S~ZNRbfEIiG+jk6@1*
zrG#f?BAaU?J`2`rLGEu1pB}L8%fLg<M-G7lt7E<=;5d1eyy)UTb*naLqSm3SPK^*9
zQ=f2;yYvr1dorU>3<M|AARs6)$wBZ!#`q>o%u`tRm9|3_^t|AMcd<rDkuvtv8d4zK
zbmh<k_=vk@gUcxzaeU^@(fSw@Q<zG;`p<<3S@TD&L`&FPz4sJqLf|_um!`cHcY;{p
zypT24XFNQR3tMTbIwB}5M8Un(ZPPkZowXjd#+^=LItm5D`id)a>PEX@B);(7!<6FQ
zprL_ym%GKnK37HFxsG}3$8k?^ZdaLN*781Klv+o5=?@@(Lh-yaU+03;a;?J3WGG~p
zDK;fA`GK=gZ%bw<5~>_3K;J{<52}?+X|3&J5lyxuUQS6bI8ai%!h-|#^@J6kh>NJ!
zsSD|n4{N<If5)`AuYqR)LXk37kPJpKBtb8tBHaE;C@GhAlMf?JT6ClA?qLIdT51w6
z6qD(?SRm^KqW4>L74}nzb)=27aJ$C{XsR<^SPr^n-gK>qF=4XvQ!XQ(-|@^qrnE1t
zz#Z`gYriaV776hxko0T{#~ncy;rd#&`-T<0{O#)(TSnnpx{;^ky4&Z8>jLEMbqH>c
z{>8wGm{!Nk=q~S*{j0v}(p;Sb$M@Qt_Yg86^M1uV_<GoRV*IMpON7w(q7VXb=yjWg
z#HH%K1pm(rG6$Ss9uK3qN97YCgN96%cI78UIh#6&r+on_{jby~*Nw-7e!&syV>M8=
zcMap~QE~I1Y;s^;y&E>|ucOPgJxDk8&8b3cdp;8}WF=s2X}%<NCWC65pX3@|@PZ%T
z!g}!*FPObEjZGPrIuTVr%Efns+*=^*V)y|*e`)6>n;7z;H0~<-z9LrEF&G_|=!zs-
zv`S;H-<ver_xFzp@0YdFESXieT=mlRgLIOgge*YWw~0>S02?@Ik_n(}S>uuY)*7i-
zj`eF^Gywerb3-=Qqu~HIRW>~?`_SMyLQQDo9vQ!3UZ~3}8&D6uyGiu#Infy_ZuWHm
zYBu()Q$o{h8H(twt*pWEQQFmIkM`4A#w;yvwpli`h>js%Tm@`ZwB(}e>lHns0Ry#H
zjwc=G5w`4oblFg5UkP!_KC6%lYA%=y#hCgG1(|%nQak{^gXJ`y1pVTSZ7SB47%ZM$
z-wKwvhm%}88e8WJCnnQvjuJ|=A}1ko3-Pqhs_EEs9BrM9Q>(zx4|+r4A-QUR(>;E0
z^n|2=2#x_Gk)(3jo)^bNd6hk)=33I)wEPKm`1yUfa=DQKa7C85^^G~UWsih_12ju?
zb`UP7UWz>a=#AFiaGt#DTKfR@Hhhcgc?vz3pT`0|TVA~%U$9YBc|t+41{hRBP5Lhf
zYh<)P63UAXRQ{wd>{)MXClK)%V@)wolubx!$w8RLJPZ^Q4q6XtY!+xX6jjYUvW0+*
zjn5NBY17qBpU@5NsY<tDa2AKKnU9PjF#~+C*soJ^rK<vzx+dah@6<|A5)?NBDQNbE
zl?t%!$CI5eE!l%JN^(YzeIzJu?(Z}>qG=ijjcwgpxm-MeL-&8@Dzq-i9+gwP9yBWD
zAdn;EP2`O386SEapZtD7%@y}UccVw9r!Wjmz#@$wlU+QvupbwAK?u4#y$k;?LjQcW
zsyV{7D`(ehE|N2aUWj}m_m%-<e7lYvZk#@l*b+=3K?6QPHMl!0e;FK6%_*O=%sNon
zzzA>V2s?o*puksstF!q4LGISkK;|+a0wdR|)a0TB&|MXD*-~$h7>gW-Zn4c&;9I=+
zo(Ly*BpvCQ%l$eFR8&g_X#*u;|COKo6nJZvvWIM7bE-1V&HKg<nmep^qR47lz9wvL
zINhTJS#iD>;HRlP%~W#h*c+<g1V9P^_e@Wg_X>jNFfR2OmUb2Fd=t|ByXgnL%Rc2R
zR&2>$s<nul8Q;suTbcPyGMbX9*$CoLZy;?@3rH98@uX#ir8Ek*Dc|Q*Qr+p;PQ$?r
zgYkJ2o7?w9x?n1udws#KnMt`CsiVxAn`Zk9>N+FHsEF%zu+_~3G!4yBxHelK$W&e)
zV~lm`$-y}cCb}0Ve6wP{JyR6A(1gFG2yBLy2WY=lMl2-$nMs%al+l3en3y<vWw!g)
zHMW@skcVi=?H%!B#MqMK+Q;bPHtZ-{rL8BSoJp<)piwA`QS+r=A!&C15@jE?5d>J7
zjXc2>;(^`~=CW*lV8zAh-3(-iY9YN+yfKYSvKHgX)Hc1R3d6V{>gZPa*5_e^4^*hj
z6UyO(wMOfs?M?2~DRDDLfgU1teitT|M^X#WRp^sdSL(EJzC3@A%PDy?pH5gzC#629
z<WBWrG#*y!(y5m|d|+e&!Bx}IEc9WdS*QSa3wzP?(a?7LLMo5}q_6jF>S=8`I26X?
zf#sxDxBA9Nh9ovk{|9`B&pypR?bPgcFqbL0er7SHot;!9`Yw`;y%L2RyWkodk|q%$
z08EYR(;!6<N0ZLGkhbqvzJPhAvsXr9YZDiIAbB&l1jO;XL}bYv3hK)u)4AX+%E&+;
zAr5~C*|vR89md}){qMJM75ap{>=EWxgIUj}O#IE?_3~3wL5B2}s7Yp{E7zp;5vd+z
z4k4U;oe%Omy>F*~Op*VnhuI{yHiPx_w!<;cY#Y3z^8o(w6i*t~4sO9Fjgqb!<PI49
z-kvTn0HW@46t1FDRZ820ID&5nYD??1%~et5W>zEm@QLrFh>YTi#Pw`~jyT_mZVgP7
z!2hXjrH9}lrucpHjilL^7H5c5d{#U0VGgg(MjG3NU#+ST>6Qn5W%%eE?YPd6-#FPc
z`aYyZ%WN1A;GjsAO#7Cf^!Waz!B!#e;Rpv~J&t*cz>m}E2ai=8Xz182Wooj@Z)3st
zLg196jeIf$;$8udDzFtRYlUZIfC;yqHmaQoE&(s$%|?p_oce9vYzgy(rXzD>vm#ni
zdrxoVX_mV_6w@uQP3IewRrxuH8CR&60{U`MRNH*qsq&G}xD<zHWv?NY&=vtY#=Y2w
zI>?GB77)!{Gr*!95jXjXOqEWC>#r5a0#r@giY^-f<sB7ts+B{h^N0u;7OChtcGi7o
zpTniR0tPm!pxW=S?7}Q{Le+sVpkg1{w#nJ<;6fmU`L0vCFAgcAi=5Ebqi+Mm0-dx+
zhLj%Z5z;plk&SnjW!L=eUByJRuPoTSCj?<sA)6XS61)``QVH7m0%C-B%cYQ+tsB>Z
zGFwqr9Dvg0vKH@?&UY<aw$ZHMxwv^ilhu5xI_%WVg(rqB(Xr6ANso^(gRFUV|I)Ie
zfIxkkuul8=vqJv$6bDW<G@O5i^997Q*!{-QzCOs?L@8)!0ASGaSOmT#Mzx&;-WEbQ
zXl!i49xSKy_Y$?#GFxuh?f08l)gi#dhB0H0%O_kLWY&*lB`#sP^YS||RE65`YVO&>
ztia$(;8oQMD*^a|dmE=col&X}<IjtF#!wgza)>MHHBf1Buy}VikB{O`L@bge<tQmD
z0pgx?|Ay!pwh;3Wo*M*^cYmY`lYkCM>YG+Z54On%Zgl;iD9^j$J{)8Qp(A=)=_m{p
zG$?ib@seLvRPGjjven+z&4~c$)w##=ZjAQ48i5XH&7&m=q7V+RTg3w>M_viMe8Xly
zyV&xxV__Ars0XAf%dc6gn_Qv`)z;svI8ks(?wpd~NKcASjTt&zH+P%Tc1B1fDI1jw
zQ0GxlIeUn{nUbT2yz=hRFlw^`qNYg+`pla6_U$O^?~O~Q_$aqno+ursn(=NO5QQU;
zEpH-OfZ8sz7o90|BDqVwim|FU?sTFh40e&FR&dMpskOqYDl5fg6h_3kVx0L_87*2L
zyTi1Ne(9E&5Bki%<_=xR12P>Ks((rP;yAczoBTb(Mj#sO2Th@~6@}PRczD;~CDgTs
zQgKm=b?kp?v49yaDggz)DxeiA-RBZ!RPZRx@Mipe04(_Gkb|=t4~!EagFrccpxCS`
zq+1<#rnYh&6|G6rMd?@VQLyPz4_?$HRXOy697C^Pu!>|WSQz$ob2IDwiOJS!^s(w4
zAvLH|mFElDWb!*o>S+aQ5jzH-_uW`Sy4jtL20=q5r?pi^<zG$?Q2DU_Nry7alNfVK
zQS-2bg<M#$h8o*xMksj-qhR!2aJ^nm3(`eKjW(C=<daYZ(tA#!)soY>t^XD#73D*G
zye%3I`gU>3ReF`X!BR6Y1tkVT7s38MU_W2IMm%g3nr-N1<97GFQ*#Gs8=)r2NJDrp
z57}9jK~-Y7@V^ar5S~?tz?ze4{vLCgO6GLP-TE#@-8$OKJ-3>lvuRm}8KIl_m^pYb
zneux1zJtX($^(a(g``JeoNG$M!@Zl~M|F}rVc(b~&la_$e8Jco-=lBqkSswwz_aG=
zSb<LcLz5%RLp0MQbrQ=K=&z$GABJs5$+xZAtF!a;V)32g&9Oy=I^FAF`Sp5ob`r)&
zXA(9*)>D>-#$Q~aGBn6NBM-Z(=PopuZ;fczyjw~kxgruOW@xiZ`2j7|=@|;Ogzit2
zOQK91-<ZwH-tuXS%a1~CF{)$!{x}d0-eYx9U*6lX_MB^Jj~!ofcgoQRRU}tF)AR$3
zPF)?ed}<xQxc|CI-J^hb5X^*COw>`Les}Kc(N@;k6)X9EsDM$POX+fWML&RhyX1b#
z8V;QHSWqSv!Y+$vfWZm$DAM^yz@O4({IzF5B&>B3c%f=FN;SIaB_vAma~5I4ts@bG
zxv+nmCAS})m5iwZdC#?zRjHgmK}<@O{Rirv)GsJDOf1<7lf75BnqtN9NU{!fAn?^N
zfx<$wD((3xU<JW*Q-*haKQ~>Bbt`>ny1c$|ap&6Z;N$?E7=P#Z)C6$1WBdjAo2UGF
zD>ljxW>;zEWW(mYZx!J**-%~N$aR$){ynK8Jp$`|SFtAmg>`4((+k=)-(jaHPr@}K
zP5z6rgw2)8sSQkMv~zl=NY?2r0m_=oSV|@6zBVeg$scCk-B-&$qw5bJ#E;M#J$?}A
z#j?q5i_>$@X`8lR%aK}@eF~PpaZit~d(GZ1_A7*xbXL|#Rs$kBDOdV7YMbpT?_Zr5
z_&+)^87N-{X2f0ye7?u&vlSEVUW`;!W4gUmZ-1}Hy0>p#F5kFijac!(9SfCetOLnT
z%V*vuj}BxHIN!g@G_e2lby89Qlz%-40KgD40Fb}vuNn;i^Y8OlF_-(V4(~s`S}xH4
QqCgM1!2iSlO6&ms59p+&;Q#;t

literal 0
HcmV?d00001

diff --git a/public/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/appendix-dsf-20.avif b/public/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/appendix-dsf-20.avif
new file mode 100644
index 0000000000000000000000000000000000000000..aa13514f37adaf93400dc0f6778aae1684cf282f
GIT binary patch
literal 16574
zcmZAeW2`9A(glhx+qP}nwr$(CZQJ%<+{?CY+qUmM=e+OT<j$nKMvcPkSxKk=^Z)<=
zz%g_2bTD+YGz0jzY%R?g|ATEU4gdLorM9LnhX3LJEkX+u8>jz4000L|W0(KK|7XQ4
zZS0Nz<A?|V00RL22LX=%&jJAOnF0X)hY_IrKL$YPU)FyZhyOn#_s^BJw6y#0qx?6D
z`p+@_Z{fdwhHeau|Ec(Y$$ydmFA4acI#G}R9S6?A(#7^aCI8*xkEMf?!#^Bl>0oUC
z&%zkGF$xEQ!2BnUV(IK)`X2xQ0QC4T{o)@$aIo~S{0{>R0Ri#P{QvTC{zLui{J$9B
ze<B=)t{%ewp<SKqxNIFv&HsB7;xe{0cHnaMbapYdb>{k)V`*b)=;X;|Xyah<-?8Fa
zI@tc-e1QM}fdhen0f0b4fI!3k^`fwJGX38q{%!ZK*yi5^0sngtIAarM<Nwb1-&FX%
zZA}bakN}{dFv;lYrA0tkun?G*d}Ja#JdChrr(ghe*(Ouvj2F=b?tprb)UMKyUD_R2
z{c<g1+Bl1u)M4()3(Ig;;(9FovaZ5ptNQ%8fPRg6<6i7uO}ZsIUA(tHp9xNM*RfCS
z$*N0*#Cf)1B$eFX@i+Oo!y&iw(sj><RpIw_QuDlAz`mvBT+glu>pi3ScEcJ0e3LSk
zwoC@T`wV;=Ki$Nitu7WgYOn*<>{Qjr0TV{Y@Jzp_c<2gM6W{g`B1J?<!e{D?+IByN
z@URJIo3twSUvnCsTsynozX>|rXLjuNR6<OS-8)aqV%&Rq<s(2ipE{gX^+;weES%KW
zOeQ<+43=y&!<YhBjd#{iLO__sf745yyk1cd&`Cn_S4Lq2l>21kG^3>18RlKuPr{%}
z^Kv`^`gWY=j>^m+$HYMv`ceCq)8L6F_02dgAfA;@<BrBwzN$)q%^_d}=m<Rn`<&+K
zI0tygA&j-Kl{=yMN!6o#c6u^eYsz3xm<wf?X7J<w-1>A?k0=Q639avMScYTu3dQtR
zWaA&jYl<KrlnUp$Lc02rCk+;FM~-D;VYcWc(sTQzqDnjJtJ$$d8&2U-A{jFVG(DFb
zLGxpaQlXXcR=vLb8CTlHhN0e&oaLQln!<jDF*-oG^4A%JXO*s=&rGHODkRoTxTTeE
zQ3(8+6oSJkycZc<eWZHmV8(dwx_i41oPtDSrMU{PKvjHXzbCQVWt!9-8{Fj2(Pe|-
zLv~Ea!!qujfYLB9-SMC?$c(!iQiS#ylUsH;`w-ln;c`(4v%~0Glwq`{<G*R2wa*=6
z?r?e+JhD5#%+-ndAnHc&5Ym*Q?<OR@3e-3Wn2~=yUOw7A&FOV@-X)V?aLimE#-|^6
z($Q<;;4o0%$<Y?VNHHRS-}&UYdzfIxdyI18suTq>8?H4iVR<Z~iI_$|Ahct{tRE23
z$Fikvv@^uqFQn>2{+i6D_^Ugb(|YB+{Wx0sbr>9EKqAOkm3!vBL3P@Q9ex{-GJ(U#
z^<3BKwZAYQnBiq?fzATxHcv;Fc~J&$8?M=&<{i$MH~`Zwk}DNIu12c<YDbN&hgENG
z1rwSmN04MT;Uqyjh%0w_Fjq|7H2YJ|X`j=wfDzvsn%=hWp2ga<1XO5;_6`0L$^$C5
zHk4RnY`KE)(>_F~JW|4l&oz`nU%OBr2A>`Qs-^(*GsD>^6t+E1;;c{7UC7EBqD#)8
zFKd>H=cNX}W~a~Q^N3ppbJ^R9r(ZBS_lR&vc&fAZ;zrG}PxN&0{4MWjZvWJ8<qQcj
za`PU)Itue=;k<&>zLILSO>=efERdyejHA!0>T0tH<!DXnhhStqo|`@o22iX@U6GGi
z{G}Le2qEu_UHTDK6WZ$)Q#>BX;6qV<|I=WCbx?TB$6l4FrV**kfhq<;W<MgBb5}pO
z(X3G}|2Blp9nLz{ZaMU@k~V7{yIH6FI|3wSsjKC919otKsA@Ho2S{MAQ*gasYh<bP
z%j&Tk0}8q&at$r<wX0eoY8(zO))r${-r{iw?+7SeKj@4yib_lw5(alfel5TKEn{le
za!+mzQHy;!Xg1!^-fD7*-6AB-Tw8<{p|e*e*74j--`sY<A=gDg#}96d>a+?v3i5=7
zJpBu66~5$7uh;~Rnw}<ucP)(qM{vgGN%i!q@{u2Nw4~`?fOIu5FYSa1w1+<>mB@=t
zXs|c-y<_FE|7em37z1L~+?HvR_Bs1809so3gr?Y|UYOzY^OTW!8_#>@hbAgUBkv(X
zX?oMt+!Bs<`e%taEBwH3pRN(0`K%Nft>krYvvp?zZF15m7u%ZkvR<+o;c}_ykc@|j
z!v9w?i|9JE<VH4+>@9N3*ge4|3#LdE3fNw{pl}^$A=j3#6!PVz*=o6b@11-9P#8^7
zXEqs%N#mY}--~xbwST^5pk2((8KOn|9{oK#18m`yQJP9Rw;g59lF|lRoE<6u3h&ng
z(x^KuP%?db*E=(O-^eVup?I+Akkl<ym|mPM#^En;(iWExN7)rzmLkD<?<>RRO@-gm
zke#cdoj9jk*=jiHFK6m2EmuIMao980@E)aC{ql`$OiCDx1hVR1)ztmX7~s}C56E{I
zaX3MZ2Dyk!=;+ockHA95Q1Tn9&KjLfc~Lr}q;6jo=ChIOS|=E7Bt6>*(2?TF*(O<5
z?}0mc$)oZ~6~>_AF{TOwChKmTr)7Y0&mjHuT}Dd4?JH;sA0}gH&*+roA^Too1-Hmi
z>C#(o=Gi4yQKW2X9lf<FW(;a$*0aWxS%C$Gj*V8{foF7__}w`N_V?5}EjY(aYqNv=
zffHoT^&_?>n;=!Fqe5(->pao!FTF1P4S%N-J>ZHCqmh24YZ*c|geYz;!ymUKWlc%p
z{OJa8Fj=O3KzAq?nDi0XzJ-2Y+w-(sQ#LyDqfK`yDqcyhBDyz%g>8aO*!8t=GT7+$
z;xbS77b(I!V<At~quc}?6SQ-_c%#}y(hlZUtllD8Vf^4lh5d3Bp8=t&R-QbKmAJ5v
za<~}%VcJx}d7h>i?3=EN{JIx)iRt6ezH0BWJQU=Y?%OtR3T=%3WJqgz?ZnONtPvGo
zKun@tQaia1P7UR2@(9!!S7Nkhie2wEy}f;Ww6Rt;2tXoSBsqMjfHRb7@w;Y}MA2Sj
z2;zmWQ8E)T1{-#D!4r*@vK--n(6NX+jJ0{O))v1r-!GJXSJ7qUoLTU^oO1!s(n2d%
zpW;VBkXP;TgLo(K?bMEP@Iyvywbi!I6q~Q>26U|7!WbtACe#uh?eF4d&dVM@$2Xfk
z2>BY44fcb!WbY=aLT|l}mmw1BbKE7KvLFp?<PP2<Gkt9Au$6i$KKgG|f!u~Dzd`gV
zEJO0Zd-7yFg`6~;5`W*hv+#y|PHtI`4pmK*F|aNTseBx6k;@MTcBnWakb9D%0e_6n
z{R2g;#=JNl;;~K@`ek?(_A5oM3!vAM*hDTuflHy0?%C3I;#yW_QYcEH=1wtYwGC86
zeYmrUyyas4GjiV*kUL&hTXY5hn4&n-VBr*_GCLkJ=7xzUavc9Ql|oq22nkWLjLY4n
zHHf8K5FFcw{s2Sq@%7W|7m@444Fvh<nN(c4cox=ebVOoe{O-2(;t4i1m@rw;0zdA9
zYbIt5iou&_j+~hE5^XyFE)Chw)h@@wDyMqwAF^`HjW}IVK_VQp0ILYSIbq;QIM>b@
zvADvE6=*y1)ZyaYSZ^C&{$+N)M33vsS>93HCHbiVH%)veKhVW0M&MdJj5pad${-t9
za9)=(1NWhpPQ|qTh8=M^p3S<yuLH6}8)fAUI*(p;$I{x$OAL^nP}BxH7MqN5pT3KP
zXDjrQ?1!WsQ%1)D!~GwslnR3wTLeBQrqfhxKOyaon%BuA8@^@k0-}2>2DmMym`kVt
z%Citfa5!YgNnP7c*hiJy8-gPYc~c<zVeUI70+qk-k=S4<iVr(N+^UqT$bTq*r3X0(
z4VLu)FcO7A=yWxpE^e4%enh`a-jLD2`h5#<)hL7YukVX%8IyzX<0pUc2-6gGd`iOb
zXPAaw?=_ZvT&Wm-eV?@f;BhB^wj6FMK6FXI>Grj(5`Vz47PV;=<6FJ3%!ZK=KuP$!
zL^({v4KqD(g6G_r8!jPbyny5`(ppDS(FZ$tt&iK_F5)v>-47;f?ku*{ruu$1>~JAM
zKYFD$6Lb>K<1s$1L%J9-|CsZ}6D3vl;e<?^36qtne`s~Ck@=v06vu-_-5(ev-f>0X
z2PDv`;GBJ*ZYA^J38AB?%6b$)MKuT@_{A{13jF&@&nK^nE55M1RjT4#cYUd<Tb|$(
zO6Aav3OBC&6<AHJ(1~irj*B0se1DPusM9Dzoq?PIg6=W&u>@}@@<w6!bq}Z#$JVC~
zQ|n#=L<AU$(N8<E=mtq(6b1R<&b0qsuqWgY-PtV*8Abw<xS=6`dw+suCX^A_Y||3f
z8D!|q%+yXwy{hD`>#ZpseyCF0;SDhMz%H9K7(=5<l<|eQb&R2qP$t%Oi=H~L0XuDr
zO%!^Ypr`Z@&}gS#BH2)1W1wYGc$W3K83qx6Zgeq|3XjTV<#w{I9Ip=a32<{Rob{1i
z&$6kx^kj;~dbYzWDyeR`>%`SiTNT98_|xeFaLeDIvU>>d4ZbClH*(Ba7!1#jA*S%w
zm}&#fhaw^$z&U9{jrzdxh+J0Uc}wD~c8J{=^K}1-%RrqJ4uxd<THP5dJKlfGkwW7J
zV;ICCGl%3R$*jwl335n)Ne<%xrnjAdcZPzGBD1C2_`9kIqIh-fJrMXjl-;^`09H|#
zQleg`8~>GPi5BNN#juDxYP?sW6|b)phaHna^w_%PYU|e85jl7?&4{;RFV{9V#72XW
zAa+E5tl<oA7r*fm$a09ICPqO*VrDBO9^*yedA`Eu0w?=ehHI_f=iy8^Ray;O@<f4s
zK@K8n>?J~9^Cjc~kp!ALoFm^BSpy~4+NZCpEXn<{yH>paXRDNjIKE5%;Wuhd^COkD
z5}9h>J@7gtWHCNpGhH3WkH<NuaNEYJ5wLayO4qtU>ve%5#yiH!G4-wY*-dHVC-DGX
zv*V!YuWL2ab!3zVirCS1DvrS}|6Nk=a7$q>4GwsjltDy=-xK42c=%)b4AclR=5dz+
zt`lyi^<G154gRk?<sZ;@50Jsp>`4cOEXL~MW@&gqFuZ=#s9fs8R5-<@M*1HzgZlu6
z%t(a>LfNLuA%^DWZuVODnf7yv!H`d{Zl7KD+ZQChUL|{**BPZgBUIeTxflT>N7^HV
z)ZNdwf%+|dN`Bq4Ho{si`wNOqv!h=tbPX$PwkOi3mE^wT;Xt~+`M0YQ;sCTL4P>s3
zO04su7gJzg6-+m6k%tiPh9Q;i^5jwfyy6*pT1KdL7At#6F{YZKg4emF<^g^k1g2ov
zk05*3^j^OPoRJWCchch4M}FHS{CFyl7Zgoqk7m^IWWQj4CmIZQR8yO~R+V>|!0yLO
zx}?*UQr5lMbCRt|jz(b+VKQ@-tU{-~{4YQw$XF33YY0EKb;^ck@#$5GPBGsZZ{_Tr
zF-0K~{k{hEnc7AL;=3hA(*8uWQTw2;LI%*3Jq?p2#npI%WprFMeOAg-BbSg^F+Ip@
zi*teWn<Y6;WNR}Ncm-XBAhG4K<G}rJf2Z&y5(S-|Ae)P_Z(Nu)@eud|B$ZOAGu}{H
zYv3~s^jWo-HS~xQZ?#}!mk(W4Ma55MdEIf&I^m^P3@Lkw{E_aVQ$lP4)iK|MAA}%z
z%>-Y8s3Uty2VzSalyI2q4@%ntqg~>jhj=MdH;7|NYY&(#+RQ%>`5s{yZGfXw*Fm8%
z_#GgRvo+(-nd2>8Wd4A`;<dCEM`=;}ws<<P+rFa8In~$XI`&ruU(1e{a1WDUrJtNC
z<s>g1-0t3TbD%D;&5$|dsJvp&(V}yx^E``TPEj&f#+RN1Ydb@$(K~I2p>I8eSUl<H
zi^IUrl?<&(_S~%|nu_*NYQQ$={@)#h^!8jlchSW1v=tevdf~}~W_)c#B-MO+29<<&
zhggu!_)QeOtdCh05>tIVFa6p+X(+97a#swrLYYXNj3+?{0KX0ORifc4)PUB;H*h`e
zXr*{j!uf`BJZ(^3d^+Qs(3kO}Cck}4A+9js?*rTgoK_W{kEA!Z5lx3@#7rfS*@zZr
zgj!L+&H;|$a8ssTkjye=Z4YTnk%uesZNggLWW&DzV8_NUnSOowRhI57x~{U7ZExf{
z;P#P##A;2Nrn&J#jj|$apM1Q64ALF@)z$+^X;jgXCts#oUfa_oc3?yn%$nbk#FhS|
zN`UU{Yct=Tauu&#tl57|4~%UiG)w$WvW#s<snEJh`%pM?O|QbcVWP1Cxp~*{b`4EF
z(MKEc9bqmW0t~$h-~yWdUXpZGn2M#!Xfy0Yu@227g~wZ;%*(Z!RWJON!03%S_zVG2
z@TZzi-@ICE7uqHC#R;^(sdex-LFWt+)ft}cst3Ljk`L~z?OMninJoA_t3sR+$L&%8
zbRR1fJB;33kqU*(_(pkKwR^9nR0`7c?PI}CB2|3s9h6SP%iTQKaGm~l0gJU6gJMs?
zVQ|q*uAY5cWB#J7JGPxew^~eJL@uZF)P&~9biFz74|v#UIix5~W6@LK6F;H04#Fv}
z6O(#|`doqhZphK?HRbU}VRq2SaZo1$JG)S3+h*6!J(|$l8-_ZzQIC7mk}7M^<vpJa
zz;oZvh@ay9Jo4au$lGsx5ayjhW2Fh!<kW87BWt^qoO=oC4?bs;LqxiJ)8H0uPCd0F
zFvdfFh`?)>j5M0Wl6g2Jxa3(vcr!m?7qX~iN}weQYlnCw0hDkp_nY#ZXtRk|aTIDg
zd0jNO*^_GnY21|8Ks3gx+ZDMdngne-*=10S7ZRET6n(qm%S2`_1-Gzc#|_SVO0f@9
zJeW8+!24xmo&d0T$CCxyhaj+ZR>VV6xDrCPmF_JjuB47H`Y$<`R@i}hB{SIZFNFO}
zmkTRD$7*dQ#d1X#2Bdu_1{xzc%qm6RG#`!)k}d0w?UWBGjygh1PU^<BJ{Ee0XXoE!
zz$(fH`FCj$%#Vc9&6?PqKGJy%_OzM$+SxuJE!n6o=dr=bLQZsruXW@}77Jbo&0Oh^
zIsrZse-2cZJT!B_Hu&i-y!{#0PlT$1Baeiv?gKu4G)%s&Xty#FMpEb2oj8>+BJ%Pr
zwc|_!TRDDSnLshKnGwudwoAAfRe67=73hpB_^s~UV@`kg<orxP7{-S%)96Hfype$l
zs?DfPT59;l70rhw^5{RnJtTwZO<ASR&3Tw}(4`i|=sxDHPHf~ZCqnb*EX*OfuKbJP
z=Ci-wXektf(`J>mebGS8#%#j7yov-;=xnww!vnI}i?*e-rb<ibQBRcuzeoA>A9w!e
z0GL$ys{Z}z-dxubS$Xt<>@9*sxDmm7V2>eKg%WM85Akr<V%8CEj4HE)SH>)M%CbAB
zp4HC@N-AP^Ubl(d_-Uhg-05z+nw0<wH`a3l?w6{z0(ldcQdCC>1bv@ljxMK$S2{Hd
zwa$YyjR~`sE;A*t`uQ0ac%xa<OsyWcTJ1Z}*66i87;@;eLO*cEe=V9K`L{rmvGKML
z01PR^c@oAdE=$__<}ELr(Hh(fhME#)`>Fmq(0XkgWi(5a=aYA6`c}pYKXGN|5w*8W
zY@ZdUU(S=|rDawaGrio!&uozK1%(EU<Hlb0PiwhSY{10XL3Y;p;eVvcS)gP1U4;E<
zX?;9zr0p}qT^PgW)biQ$B>1!_X1hr`N<a5sm#SCqJ3EDu0?4J+^rl)RF<}LosR%AE
z4}A>zqysLD$+y4m{Sp*}s*(}DdBolvi!W^x5{BZ?<0z(+FlvfAD%YLYa2q?n3Ih&)
zkDy+JooUH>$9HAv@hoh|dKhn&k3i=u(q7KQsF*+ebP0%A0mAZW^)5Vvxn7P(*Ec$A
z3$b{*Wz`xy1{3d7m!z?=j~uF>%21cE4O`_TU+5|JsZi5jB-?m*A1iS1t-2sqXY8H?
zEX|P>taq)H6jH%WC-48WELBt|=ja;Q-p$+3?_WOFG_WsF^O7<n-K?3d7J`zC6y5uj
z_=ilO**9|vI6bN!4k7wtewX4R$N~V=GjlkyICQT}8N=j(1Wr}K+yz@$0_hiWuCbf!
z7wzs=;;L%4(Z@;E&G2D*iZCl^Dj*^Z1-QI2<s-nxSNl!>`Lc{UQP(b|i&rQ^EBgu^
z#wJkx9YT{(DYyi^yD4?cZ;y*Xb6{bCRou0TZLC1IiM^C>q%GGAUq*5u%#fAaL0r|X
zUM`i(k>@;x5)oHT8SJjJm$LzFF!BeYm%$JMsyv$vKgkmee1Zv>ZK-jMt|Keu0m4mf
ziS@1`bQ&Qzru3u%=kPZJD$SOUY%tV}^UI@gfI{<gBtdWYMFEVkSUTfe-N|Da%2!G9
zce$`vBpO3sU8!xUd_f9q$!{n5Y`<EC#>&-`C#nT|iZ41?s3%A$OsD3WTW>j4or4pJ
zIwqa9?#3*!=Pc}ggRNG+kWAI)_B(Dv86?fm8D`wmFT}_NLcV0)sIuC9D9D0qtxLT;
zRK_xnnwCioyhMQagB%{o<HLGw)JIXt9U=XZ!5_0;_dW*<w*>t!APaWL`!WKM$J$7p
zC!*lty%4Oh38;J;(=a1h<}{Epf2Jy%ykB7NNJtDv7udL%44twa7dk(CtY6zl)1r|t
zu!aGy^?Ppx%1IE8H!ErL&k0L7E(nI(p$Aqk1##yWjrR<xlS0p{`p)7=DwBnFg2~|f
zB8NoytWp(<eDmO>!JsS$S%~Ied}V2O?$+5vQ|r9V?4IB;pU?PXGLg}2#B?=Tt?W0I
z??ux)+lWD>OLXvaehL6-1nML3%UPb~N^uGBY2i>iK!yX7aS`Hn6mFVVUmh3ZQ&Tfv
zM~3Z1@Oasi3}Bd_(wCE)2PLtQDsd}MYeQ<ZJU>{{3Y`MZI?Vo{N-qzz4{H}|A(px~
ziN8U)C13Sw_xvm+F@AA3G7Nr!Imh!l_{KF_9VN|XMUz{6@y)_nMKj1pD%p9=S9ToE
z{DU-SWKO%rz-yB_m{@QYq-X|c3f0o&IBKr)EEyerT^;Mf3%8bGd8oTIgH$U6gn?ei
zlrJXm(g!uwKPJ}zp1KMr(+WP=B&M*5KJGzat2NRxK*%s_5lOMO?Q1&B+hTUgx6FdH
zW@_j25HRyQIj9yZ!(SzI>O08ZA8m*g0m|C#Y;dv!5tdG7K$K`l;i}$=Eh7sk3>O{D
z8xOD=SpY|v($_8=ZD1E*2ZUq!ewr@|!tSpYEU-iF!(`SP#6!pk`;%~`0Ew}VHYAx{
zD8AD<OMQGU#G3Z^_N#Elt1;kU1WU0^>)*_w!-w;A*1OuckfcPR=~`KuJ)d!tD_6aR
zElh7l0yZE;lx$cle;$#_(@#=S50Vr_%N0Zm73me)Rt(3*d%J`6tHw!<dwho70Y+e0
zNJ|5ZM<H{gPvhZo2{eE5NzUBu-Hn%wskrH6^t1&$NHFWUsAE*yV_bEn|IX5^?78-5
z-as8i!`eb*PY5<oVNfMZ)qvwODL%3WO9lJ524C|Q!uQPf*zdd()k)u#3BX$_Yd%M0
zUTKzbTa5TpvyKuh4jl*C*bga7XeLyQMboUst!~L~QFGcy1}nim_a{{V$J}J@(g)3l
zMC+F<PHAT>R4^U^llF}juenc2PM1tw_8-KM5^Vy<nq565<gqY00G3sBJE9U>^3rKx
zuGzCwyC~_p0}nx7g)$F-Mnvyr$?=T;2vm&mR4ALg+hFJrgxu8I{~&UhS{%#i-~6>H
z40?dgkYZEu4lKM&vOCgSb#+`Hk0^$36!pM)`tIleE_|?*#xMb3Eo{5qj};hG7~Ph~
zfo>cq)iqt#as~xcNSfM5Ly`eQ1bG?s1-CKSvK+-u=!B=XlUMzXM$@)wrJcJ*cr!ma
zzpvU4Kq5ZI6$NPtDHO8I_GoJgkT(%ql)40m<_UjZY|A8Q@}dK?O{sIvWiKSFZ52ek
zQV|=>W`MYuuu9>;ing%Wk5pHezbwJkW|l8#t%Mh@@C^ZwxN%z78$>tJ_M*XoGb`Sk
zyC$c0!1?_4?Kbt_b8NXBo5lUON+)!>9|bIZ5)8l+1ZTfXB5RjyMo#NS*R7%@U0Ex1
zm8h;*C-`*+kxE1;5G@328wGd6d<%oiJ7f@mo(wDx=Y(lz0>hL^H|k0vNRlI&^R~D1
zT*p;10;F|}z6G(rV5%2SlbjBTN)t->Wu9GrFPZ=MB3nS&bR<M5*d7q_x;lz(YT`lH
zd%lnX#4TX>v&vI7dd2eESUs6*bkZ{1Fp#v!(^vJ8nxguT+%k1$OaPVeRgL#N;(J!d
zQO(&N@Ql9Bn-NG$=D;ks)^jo3OhoRaAP4^zG^`i(R*6#dk=mxv?Tl%4`^&J-X~y0O
z(pJb3bZpwWa+T067NX$gC3*THssOJa@l~f$B^x?9D63+p_0mW$B{GIAe!Y0iz@`0i
z?EGq7O6chJ@?b%3o!K_8aPEBQe>WN7fU-^Ju#W$-*6T2mw8+V*2n}S_zg?1&r%g4G
zZWa=vJQt$?r*VoHkhwVJ(=B0P*cGI?RtCISxN3g{27n0fl_FU1WUJjdc6d){P})PL
z+-cN)d#9>W8k->Q`PAbZ<94OMktS7U;>7!Q$~EHCzo*KXc`tQ0DJ||<tdWvjmX=fw
z9}*a4py6IlE`jF)NCW?wT;2IpKF)5_R-l2xlFYh#U5&=el_}3lG06n##}GUiSg=>!
z8K-`oKx_QK-ZMRq!V-+hQ;`r_1QwuA@~b&-?~Vnmx9s^dJX0eix}#SM8DCtAJ+)`8
zKv%iWc(Z0_^2gV$!8*T?X$a-8-Q9(a@4=-I^sFLKErj(%=O_bwAi9lXY~9=Ltv|#+
z$&WVn7^peJ2sE1;+!BsZh^%S~YH|yPQb@}Y%v8*^_F{Y#exx)kpZHryG<Q!n!f#VM
z9@{N{f*^+>oXL0ZM5U_03axc;n@-X2>eyaQGj1A^Jvv0V<_SMf<u4-5Zjox;KB@y@
zSsCDn6ne-0bW7V7D+UXWo+@`u=DDwy8wbTJrs)bDjQe~_mnRRUwiugi`8&6Y)@DuT
z1+R%DLWb+&C@aY(>(fh|Frk=D!!SXz@bjW_GcG8=y=f_d=I{7be{up3`|^2C1Xg5p
zDD?uh3~UbYwiK}XRsM}yLGx1&+>$L4OkFg`!sQ|YlaDAT8~!<nO{s%aYbNc_<)__7
zB+h$jQ%wYf`_owAeHH*|9!ojpzA-ErTr&wV_VG)-8_=^mSQ*0XLIP!Z-leylBpOwa
zx3wHIV9w<_Pt{NNhK_w_gkX(EB|gEObDyaVMln#Ujs4smAck_*s~zB{1a~?W%WAi&
zQ*!~7M<Wd<z{k+<p#fIxuoCO^FFS~}ncmTDS4AjBsV^S66Xs-fF3vsg74?kOm9#8E
z<s;CAe^7Pq*4QjG4<uO6sWodIjKBNvtyTurm)?^2t~?OzJ^r<P2L%9#p1Jj{{E*``
zILHL(VE%7Rb{vfRVl8ZUva44?@x7WndueG5YIp*0e?Q85$_=rDTw&bi%z9kZgK=qP
z%z~e$<Q=>Ru_7S430R7IB0j*e1_v*QRh*#3+Ru~sXV66cvx&T9Qrx=TIpJevfRy{j
zErG(N#~4)EA{0D(*tDC{^85EZAO*9|LunCX#Uf%WzG@a61r34~)O$#{=*pYZ<QFog
zbF?zBW(sex(?&G@SBGPOAS|59VZUc0Esiy=+7&Miu+NP{^i0AJppc&Al^+9lLy0m%
zeQ{e9$3nF)r_0jcz};R90DO}(Eg~vC*NM4yT2ecWTh8P31&`3?`YSbedZ5!GKsOM2
z>@qu&%*YJ%2KCq$dg){%k=k{`t_+aAfx$PQ>*KghrWPAaUg{)~Cv2%V-AGg&cu9fR
zX#k0Jw6Gx~rqkiiGpTEsa<!zs^(RGV=SRMq!|l<k*zmwzlZgh=tz)#eUN|a$N!hME
z#f!~+!*oiZiZ{zOFuK=H5ZFN1%3U3f55;RCVI?bTJ(~^!!FXi4k`7F_`F&?<S;Y3V
zm`>j_$dT!>__azp2eCxjzG~bZ!;m$@myb#UPv?ZB>@Z^9qpRVqOCCCgyELF#EGPsq
z)`*Qp*8!%<+4_E(tC7qlMjfOy216p(L?FHa3g7Y?%|*Y;fLzRE2RdP2S5N)eETa2z
zx@AJle7@QTJo=U#d=HU1wh-=G-WrVB*R_a`F(VUcdJQ)^S%fC!bP<pV7&uFGEV}dc
z@oSz<XUZANRUhkZ{o4AMRM#@d>}Ds-S_L0E%Acbl??Woj@P?nY%z*5~p&|Ob?fSO>
zJlBo#JB?#=K2rWus3|5!eE4T#bW_|r0fG;JSE!UN8k)<|ZYohnBwYCNN#ax^UJLP{
z>{=aPDaeV37?#l9272CR!5!>dv0)flCE60^m%`n)*B7Q9M@5c+)Y(h%eiac2w-DiT
z(3nHuVh}YZ8AYghy1__I3%V2&v53)@2&op?(%U*pz$Sl%g#{I`K`>*vm5@JalRco2
zQ7oPt_&I(fo8fOD%in_oU(2@DvP%{~B}2-)Ju_ulQDEt4pDqJJ0NB61`1dv5J&f?k
z@d`47<4-hMPwSs`QLf`5S>5_Z35c?#UJ;*O(BrO@>2}i9s0Bj$Oc;y@ypy3YzBT;Y
zBYW&5&HK8~pb~10NnRpb3Y`;u`BC}3basvge<?n88`@SKHMt`mHTtyMMKmzThCDm>
zkWjLQ7Gg?`($L?nluzVKbicvG`nyErZEVC&_l%9S&3%4Uog;JFVm}`3sF9tzb-Yf{
z3XQC-7sT*H@36<472_!9Bl@`4jW!~1loRUgjdnJM4rd3Qy_=u%e_V^<1U+)(?yKQ#
zV8NQLbue6-J>lt}UvAiuJ;@vl0u?G`GnUH8%xOosKp2P-WWz|UMSY-Go11i_|H@37
znVm>1zBf$Cl2bhKe{B?3y!z*7ZiE3an;UO9pSoaKxOw6Y7ZybKu5tzuA;{I@dN4M;
zEJo7|YiBpz6&iF)n6_3#mUU<gqYxgDAtdbs;auav3oGDkthD>P)?DV48Xt7#Tr0Eq
zK3#RbaGga5z!-NCQ90Vx_yr}Hh<+&PNYT!qpt$5ziK+7=U>`9oo}kgd?Z~wufhU-;
z#$qj;*cGlDcP6Aj1ub8-ZO{Bbt8oABz$oTTN4ijYKek6j?E)8H>HmH8j`lxyZ&%&n
zl1hjub!=5XXqIXm1oM<}5hbE?)DGQc;r?Rvu?28h(KhFhRMyvMO`dRsL;}TfYjPgO
zQk9*nr^M591op4u>-4O+grUL^!5`+5MtC)M(LPut94T=8?$~@hyGce5^DDfFUSMg5
z_yg&Gv0JczamcHC85ikPTyN&I;sKNS=EFBsXt$<3RmHb=&iKj-{2d++=(a9FQ7z3s
z<4L#_+OM;?F@Z}(cKyP!6&(jz9<p5A$`Xe74s^8hCGKP=+Nq~y{P7AFQg%Yg!U}d+
zeNe>LV`C=cZ*hqR+lhV`E*uo4eI7qa3nO$otqT<e`?Kth*%I<aA-aGF=pY3E{Vk|w
z;d-7AuBfIv3OI5S!7eGaDVpon4N$&BrP+ZUo3E0`AB+(|kc6pEw9=Ekjtev(^~a}3
zjn{T8-y!R#fvSxIISs@g1o<Qe<Yvba;ESN<*qBW&o=Y<R9W9waVsi+;i)%WsVDa<y
zw6DanwBW^f-_ar5-)0aqFjnvaM%oK#6t3NJW&2CHrk5lui2wcjQ+vQ7;OKs224f_X
zh*na`pm9Oi@zGawPqwKE@dAxe2N~>jPF+`Ugs5~`F}`}EqmWoIE>}fG&?WEf8F%ld
zd*M1~KO@TJBVMC<)w>w&tTji@$Y7Z4E}w3qScP;W2t=4>=dy(+7y(3p$e<$Uy#6!T
zFxFNuH0sB4G7Lt(#osFDk@!yMY*&0^oLCcvVX-6-*YtrGOs%vU50C{x8<pQZ$>OM7
zX|}xOPd7XAL9j-EmSjK0rKsfWTbtRo`n)efEEV@Sa(%pOMqpc8Qtr}*EH}1nnsUJe
z#6vR_F>r4QGZ(O)80ApAHPMg-;yf%H_36s4{Za5~*-Z)DPWZPsTVefLukh45wqsB>
ztaGN2;}FU!(9d=PiD=MeqTkBBHHk}mIs!5i-A&0~8F8!CyXdB?TjQ&In-jZ(P)Hf6
z?D5dbu8+K!TjHrQj-`c*JA#Lov?{Ay_u%jPlFteeI_8#C2J^o}sXhZIUEVe%d|Odr
zG)LY^RDUHr4IKi=nENOiIx@@A%7Z*L!GeP!mc7iR&BuPK0@z_dFM0eF`gCFH+J6EB
z3KguW4mGZ2{$lcAqoIv~jDBCt7dC`6kex)uh<C~z04%J6V|g-Qr}Jw+3dI>7ZyhP2
zO-B=4T0nu?moahciS?%g!a0LJ+)_e_t)CYb$jS;IG8oN0kJ|JBO~7<gqC)m6`^Vmo
zI+Dv&6gdlIDe#k1CAoML6>K9eFjYhx9Y!I~R3I8@aMtC(d{63#!R`&V?^Pn7y<bAH
z7Is}v-_DT%0|eR^j~f-Is*P?bflZ@QL#H%&0IoonUEy~Ui@*Ie(f3(baxr;ZZHQn6
zotx%?$r!Sgne`B}I!?FKS?_wpi{id3BqfW&uH6Qlau?C|%Qr=!uRf1Q2v3Rd(=H@D
zPUmV2Mles~+~vNiL;0Cj4TsYewV98w8KG}fn%FkXM7S3^(#=}<9hJ3HeNag%?7cO4
z)`WzAA9wB|u0!*dp0s#W2vww!+%8{G<D09^D+Ar;1Ke@VxXgwG-cTTdgcbd`4HeTl
z=32`GbZ6Hwu>NR?UG~50f<wGEIy^+^ws-rY+$E-_)nqi24_{I{+;bF?LR=qwDMesm
zlwZTHhs<L+e9ZAF&N`VmwTK@F5OiQcdjbAr@ESoq(3TcVSr{6|jT&Kvwde+B(!7Zp
zMwRRm;K*#q+~bH^F<7wB#si7}{x&YFmjecyX@i4EgSahQnJR-m)kTMWbt>pK6Ficl
zkc5VP=?%+;`jLkD<VpBNTsQp9m1*9!uiY^ZsTIa-%=dM(4wWB36%@Bu01aYsO0J5w
zhe}gv&sWiB!85ZBRaqa9poe(q)#Z6ZS^`5~&#Ahtl4Tp511NwyiuJh3e7_OuDM_J-
z%S7Ne0}X|HtF2<To+;t68hTJTZQr)siz7d<h^6eGF-}I6V?j6SeJdI8HsqK~Hzz&!
zQI~~ypz>|WS8}naN?>nH>CqMW`;LMKSzyEW5DdA%AtZjyaGF1HW<*U*@!8&J!LJO}
zo^Ho7!baoIp(o^`$8R#EF2sn2d)pUU@jHK%yn?92WV~e_X$^5Zg~mUwvM+{Wdv>7V
z#XWeuH-ADkHSeyM9(yM(s!?8p^rm$C-F36OV$doC*ixPBnD$iWOgav6%5bwEqer_M
zx1L3kzyLtnrma=dC-h<TPMLQYv&>jA$0a{TPaQsJ(W%^*<<+cL(uZZ}nwR>!hag=H
z@u#w@%f!u&%^y)7sn);IDxk^QN_*KNL`Taj(PC-akS<l3U``ht#k-ol_A7aD+8{!V
zJra_X96RlHi}GmNhZfh3GTQQS9Tc!EIgni55xRKcz*=v6SthP&mB-tQ*~57m%0D0x
znVHeN&nDn>PnlTL<(4gZf`A79_Vr{LU+|L9apgp3wBYyjgbDg6fjqGu343JQi3J&w
zL=9eUN}1_*J0iDiIlYj%4PgqjQYer^5vZg>=7uVp*e5))+yX!N=2odc(29MP?(c9A
z&TR-gQO!Gs6jD;3tLFlZ=ZUYMt%;}S&%3?{mLKLkAnA>uzJimfw-WuKVhST`7A03p
zDKI+5A6qJE|KW!1*Hh%9x+i85muGJ3k@{@g1dGFLru<vvauJ#~ra_$YUdpirO^a~9
zJ!W8!H&!ny!~B3w7TnT#53q$ZS~i>=P_m$=c?ZeeFJ)SroI*#7<JLu~8a~O!{iHRo
z1L;?Mg8=0LvCH_Nqkh*c10gH-3B?)BY#%CvhT#N$jyG1w9BU0WP8S#G^YuM>pX=Ld
z6ca33?s^aj22LOzi$M!-KrMOR!{=9trYS+mKnx;~9nro<hhM>LiAr5X@cwmJ6)SP=
z);Fw;d7|i9ZMQwtemhfQHJ_)6`16w+d5vDaliM3GZVLUM27XWx5Qnb9aaWNJ*JMLh
zIYWb`#iPtfn6sSyMB2Ofa_&|4utjdO{uJnd&UufHA954D6y@%$!CNkchqj_*7|D&k
zyucrN-a1p47GAn;R>r2w-M)(JhbEa9;&@ojZU&sOg(9RqDv(t;bY#>tJG^@i97Ad*
zeQZP1Qp>ThKM%k8LPR<9KxX^b6&Rjv2wT=Mr>pL!!`WQP7)(G*pc07SaZUq^z)F?V
z)a*NM1_e4BbJp*)1b<Z+T;Yk{E+yvOK~)Bn&<>BK&38#yCnW^72hasJ%%Q&uK{}(|
zhi~XRmw`T~Iu^;rW!m}6y53W7kQ~c#1uPR%iBTvoe-4gYSj%OyVwDys(X|Gz!#2AZ
zdCzqL39FIhr?;mla71_5QQV#?px7$Wc~=1+W&~E5SU7w{UDnL3K!p~r$uvn1j+nB=
zb8V27fOThE6!Vb)d7t_C3H&}?uxk-(*Ff!*i@<I}II`ljyF}t)3F^Ues%kt?P@;wA
z*H(-$3lz8E8hW;MhjgSTF`rSJrXmI90Uc1uYj<fmkfX05=JsiKY$7R={@N=pQkJ`H
zRHxL&V>Lo!RD7pHLyb1aIvy?*b+p(l2j=rkw9_bU+`$fUir-Fe1C>g<suW`%<gW}Q
z+1}a+<{#Uj1uzlVAT#RSK~eHWJ@zKeS2pQnB;pk*4tvZI{*wDzoD)R>hi<@*`(QxU
z&<#AblwA$JhY%h8zUpw+51MnjnL6zdV$rgH|9ni<0cux^j30`BTzis@Ph8BkhhfS`
zt)g5Ml1eajbx*Y#-6Wa^4Dib3?6`;Rs4}Z^_pdQ4j{QCT(c20OwIkLL*z(SN^|VP|
zLIpg?<Uw@KWMB;tA+8X<VA?DGbK-EelKk5FS=vR(*Xs2**p6?I_N}FZ8bfvhHN*By
zDqNreLi3G;`K0QD0Y_FRHI{D8Bw?>gftAE&Nhd(lQsiRA)|JDb#s|cV=d%5#`;VcD
zI(QB?Sm>}*aff$3d%TvZNftmxp%|_?EXK1neR3!aEy(Fy6Knxn0S9+6XdVM>^Z>FU
zbNfkuj4@S$+vl+QpEYYVf}2Nx2v#`Sey~amS6SZ?e9;~NXE+Z6>`KUwdABEKuMO^~
z?6U90-sM5muJrV&aNHtUm4^xgw*w0Mw<PL(t6qDVx$LN=V=&N#9|wLJBRBhuNI=}7
ztN!irfG*`SuM=HD9YWc?Xo^cjsr~^<PB!M<g_H)!3y0$nJyv*r%@o5p!N*TX*bXuZ
zQ$$-_O7e78rzdWDlQbs61LCjfdYXJA4pz$CleOo>Ln{z2TrI5Dsv4L{sK#y*2_Vq|
z`-qBAv4m5H*PM(~qa*Ux1nZBQ>0A^+o6s0<2#UKE8)jRM@t8|FGiJ%)<CuPktUxV@
zN5*@QZFz#m0$g%CjdbcZ?2;U1)hwags(A`&ER%y1E3G3eSuA*+M@Lzx`v|(C27Oo6
zg6!O*dH|5Wo2RM1Ki5|K29taUdX*iC9}t%%ohIE8s8sSdPzY!k>EC}mY1FwDWK3i%
zcL4(g5<DL@)t3Qekf3|l<Tnt@C+?ox6Vuc6M}N-HIW%#pMejXHVUgI;yY7Q;K)NVc
z2eog(an@@ZQU_4~w5J@nayRCRQ(OH&jqJiIrqvTLCMU(2Qj4pk*5Haqg>L={yK%qo
zUWx~f{(FA2=O52(5nEDtJx+|e))|MJV^i`~WDV=sD3Gq9Ru%bEA(Zd~5)5oCQ$hXt
z0;wJoaoHR7Ai=&FH>2*dJX<98GefP+5i}pUsfwfzN6}3v4N8Jo50v)$X#!NDzE&ZX
zoQ1EMTsiO98}@i)LnZaf#_CfBN)I*cVeOCt<SlTN6D77XQj*=f7vqH~gR;T5qYc{P
zvIKxP=|)yP?=%{PIShmzHje;dY3*OE9u5~jQ?DI2Thw9Q6tG#HU1bKUr8Ev;3Oy+A
zkDFSPZi#S`VWC=#eW&jJqYmgRVt%G|wluI?V4q$0LC1-2ll1f;VTWS36-w}8aWti9
zi6sqtHH1#IjxA@f%4YWvjwBQ|3z}NB(knMo46}B2VO>$<yul!2TAjpoV<RY)i9hH7
zBs<W<nXXoL8n&}vJeUWC<2rGda!}-p91EO%%|l}$^Y&(-n9;^dr+jOhz<qyXHi{?X
zFe<(fKFfEV^tY<m3-RFHzb+K3e@wK@SVvi{c~(MPWgq5o19hj$Sv0{$&for1Z&}_8
zO^e81EvqP3<{G-&=A->}t(QD-QQOf&2nZZ@J86l<Clfvd`NL5(45gjn8zsUFL;<IZ
z%i~zZ)z=!|Qbv;Y2o_ObvbJ`c(uo4gKC6gzGL(EI5NSj=JSHw85T<{P#m7K0#k6rW
z0q|SfvtWY(Y`*H+*Yha*hze)s@OM6O@oNcWY2OZZ^=qGt1p!;zMY6iUST|d^f_-y;
z+(5PR4h}Uv9DF-P+NXKy^th~Qvb$>aoU+qce@dPl-D=&wg-S<91UB<LgM;l#!XlAG
zm38_3qcY-(xo(cv-5zjlsATcC6=y^C(1W1M?kuiM<Ergr>WUe0L;V;-iL_nQuGJig
zJF9amn~cPIGfW&iScgxZrGFsU=5!t0cXLr@8r&06%F?%WlX8Zn+QILO$S13dLZUHA
z!S7_#?Q;M`KXn)m@l>Y;EbZ7AeTKMuP%q>q2=&n&Vib}yMI(-ScDyNDae4R$cFg+7
zHqL`V(%Y(Q@{J9(<(P|-|EVU~U@@D%iO!s4@Xz5nMT7Vs@%_OL@t%}YGQrBBrurJr
zK8R`Hx=<d|iQY=7IZw6zy%6z0zVits-dP}?N^Ll3^JUN8rnU>RW7>G;y&JsRxh&fF
zM%dEN*D@LRrZE>XDrsH9JmLA$zB`JB;PG;WqrS``%&!xI<(3?irJlHqFma0Z0^@`N
z2V->Q*Xe}Xot?@PMB!&YHw@4Sfaao=jweyiuR>K&z2;71D_bexehtgUS{DCO)t#-0
zBSQ0>)g?r)%;O%I37S3!AlviTNci`80RbLUJL8`MV<6b%uodj6cEJi2kTUG3=A6Qi
zkfEHynNQ4|#V4LCOw`TGUziUKY!?NK59w6G_9^>;)O=A_R#HVq!mgV~pjCVDk0A6#
zAB-;R!uB^2?eE^i;w+`7zXmIsXqd<pKo=h|6|$-wZqz1t@>IORudP)g3M)5`YkaZG
zw6flA0E17`k=>fi*3%Q#43}K^->}V0QF&H!&B%n9lWk>o^~jc3`&QMkZT71c%9ga;
z63i{0Y)~DhLsN`!njr#t$LB>sNoiTJZ%k;Xa~#zo#Z<`zv+VAHgmqH!F)4>*2h%8C
zmkxqcPF{ksWG@e2ObGO|_@O0!A%c4*@yjKn)H**5XP&>p%1G0_oRtIyO-Wz?hNRMo
z9W{L+v!@|UJZ2;iNk)(lQgbHS=mdTqT{{+fFn^^Y9Q&?E*af4wwiI;@FXwHSQ37z*
zKLc7(HWx<p0P9V}x)${D_R3BB=cKm;`jZ6U!3Inml(>dsCTu`A>o)!FGtm;Scm4>1
z`O$zipH$<sO)n;Cnb+3k!)|`$o@-pQc*>Sf985$tVk6jiLE0;B17dkDdDOdK{56<o
zl|mDTS>l9w<BRbpx$KhdUH((=(Q}x#+C>tk;G>agcSY&Av$U-xKNu<{ZzfNLkg!l@
z!25`!DCR1HscDUx?asXvErXni<z2K1nieGYJlh;-4~(i9;YsiWkiRlxT(zy8S0xH&
zzgyi@DakaCnAsmk7Zb7$+eWyGAXOYc^TYZLADl(VaIV3=pvdVZ+$#&jPt=N5_guw?
z9<njY76TWpH!&VC!sGMr7kfGZLr=`j*qARc0XKfU4vS4|>g5boJaEe}E$3UOkE}@?
z$DI1w0h9PPr>Mc*;7Ojx@3E!T10B$|Xr6W$s-ap_K45Nn3eMQMh-&|A8<QN=(`{i4
z95H{?LdW!*BO*voI*TOSVkJa*FVR{d1FEc~+%#tS04{E6(#|F0k{_@SH?dbMkXcUa
zIhnU7Uo$**%DWFQXzP+D22&OmW*9_Z(ud;tRuNG^XFmshfJuoN7^C<xl&##qc=O7@
z(158=pk5~h{Y{RQVp)WANO^S}DBA&uA+P*l_mFof^b5!fp?p|Kfa@WEJv{aC77yYv
zd!00asdI${A$Y<q*B>leFFP2v0<&8VWfw-JdpK#Wil=M9PGA+3(!mH%>w~T>XW_D&
z*YMcX#f8CfKtp^6p-%pM-6kKf9&4Oj@;CDz_3^7J8eqIe>fiWf$$wBrsy{!bSpr|t
z7wp6W;VVFG<IO`&(yhWs3zv;TX%ToNOT_~?wr2OaF&`T&{=nit&j9X(0k#7Pk{+i9
zpA0KTyj#!6<v#*K7l8JyBA0qm!pGr|TOJdWDOUYdO&~bI3%X+o6_#oI>m;#Xj~WOk
zzAd@6llqHu1*k7ETP|#Zi4510A7Y*A8M}Jxe{*hObU=`kkjDYG0P^Tr)%&Q>T9^34
z%orB*E!?}zZ7(ruy@esW8<cziSQ-F8dY5Lkr^vMF>_OhF%KH-5+)5={7rDFb#5RWA
zaO9E#J*gz}hQNy8OSs5|Dnxb|(d4Z#mDf34YjOsawRm9_M8KDu@&>{y^6C5dw>05C
z!-^GE!|0C{B!>OUNQO=JdW_uy*-Yx+=Yv_<iiemALIVGxL2xYauarw^_-l`&fvR3&
zX3ATA>xYd>3;C}c0VFxU1w?sHn<Z}UqiH+>R?c;Y{u#t$sCcvj!I*%puqbD;gME+m
zP0{l^MIs(CIR#l%;l~q|zd)NyhAU<Q-xy}cu;#Qvj3Mwn=#Sgjqw&zbi<EljIapVc
z5JBp>+lc@tfO<gKzzca}c5%T!d0zL+NeC^R8k%cWr?ZVp#_2oHLX8jJ2t}XG5?~>t
zp{W@-%<HD5#1&cRKqtdYzcXbNdi<!hJq|#d@_-S5Dd;7wlwCb-WaeGyULa(=XA1p3
zA@MeMnFo(E(3-YHAE5&CG4H8nY6T2fS|2gX^6tW;GjQI?emzy$0H&|T$Mr4yCJ4XL
zuI1U6^A4o#VRV5>#}RAK<Fn8d9PF)^WNv@RR%H!aaxlUUzt-^7D}Jj4+gYFaEwVzL
zgqSIv@cofwcOZR9+V?4wkxD#rZ;|-b%eNG`%2N}^OEyf_(F1}-4>8I{J(=g_T$>Ze
z`VGcw+4_M!l%lAr!fo;XN_Y>O)PO;s0@2L2#MJ8E)+%euQz*M`A(n5D@vL=>78F47
zK;nL6+iH`!Oel^(cnz0;->*C$6=o!vz)rl<<51}*uf}Ps*lX@KUkj-K!X(_U654Tc
z4&~031G{_wd3=0~Oq>ab+{gjAKP2v(=8tjsor-hF#Zc7UHU{tM7PaOfBgn~*MOPNQ
z+pLYjS_IowoLqee0%lRSy{eZ(H?ThcCo%{BkH}meDu9U<u@8bk;A!T3)m*O+GXu?x
zVL!t!$oHw?{l|bOAaO-IK`La|TD=bYP^Qb~g}=qS6U7@Y;Ge7=?0@2XSy=$8e;xz?
sV7Mg!$UpQ?$_{|_@A@aVFZi#B{~z;@D**bxD9~d8@c-fe>hl5q9|R?Wr2qf`

literal 0
HcmV?d00001

diff --git a/public/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/appendix-dsf-30.avif b/public/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/appendix-dsf-30.avif
new file mode 100644
index 0000000000000000000000000000000000000000..555f6fb2042f5d5aed04f2979af7f4b233646ccc
GIT binary patch
literal 16131
zcmZAeV{|UT)&&a3wr$(Cv12DsY}>Z&9oxx{lO5Z(ZQIG+=bZ2T?ijbm=w5TpRjaCd
z{pqe6HGzPD@XTC19Sq$p&4B(jTT3(M|6p57!+$<-iLI%N;eYsljo8A(#_4|$2*|<G
z*yaE5|5*u38++sbIATITpg_R?LBM1Gvp_(6l|Uf>VFaB2j{y?-*Xut_^Z%cb|L4kC
zTH5{hQT`i6|L2(gH}T(ihHgyE|H=4&$$ydmFA4mgJaLczy$*tdrHk!<QvN$douz}5
z!#^Bl>0oUC&%znHF^dL*!Tl$WX6fu;`X2xU1oH5&`}seB<Y4Jx`5y)p3JU6<`TzCD
z`w#VR<p0Hh{}bUdboCJZ5AEt?$7AbYYX0Aw2#>L)u>+5@r?ZQxtuxQRK9)9?hEAS5
zhBgis|1B%NrGxGN)fWT^7&H(B6bJ+?1Oz<n-&j<ZPNx5>#J}nOCENU~An<=*1i{$E
z+4#RT{#O-#5nB^O7Ze~E7;FkgMp-d1HXJ0DOwV5t9v((GGm~(@x*QX!^2Q68Lbo8@
zDC(DKs4ne}D}FhaF>TyMEE;fklm(@D%W>V7e!nln<SKjpc|d-Q`Qx7LUQD{AI$XTB
zKAwn<b=PoD>?x{BL?rpP;G~t^-v~DZdBdSMbJKNChtv@F0I9iNE)ZX`@~)?s#D6@a
z1$M$3fP9lO7B@}$zj_UP8$R45pRCU3xvFsk)$P>ODS;D4MhQ&6Cixf&)DmC!kRrvz
z$ik;<joNnJ2MKVAXBxH3_g=E=A6+{--@b_cx=(N0?W%^D9JzO#l*YLC@GC@saX<cb
zR?{P!KDThvST&jGurpY+%?x7+Tru8WMGFCAmHbIBaq@aWL&78r$y*+Q4^Ziqi_?md
z<z$+3X+I8wEy>OH1nS*(nmsHvgC3QHSm=^a8Gz&4u@6vo^b7SF{ccu#e%@~y&M22g
zf@yq#JG*RL$l;Q8+4d*<!U5LvtsOgKb^-B9i~pzt=RJe$vnho?=7dj9o1w68o9i)H
zumx#E<F>iIVjGBc=9l@+5OgK`)LC+IxL}e3DUr=MHA8Br<U^vh+67sA&B6#9qn7_9
zxS$%q_gOd}{>X6V3~RX7v*rz!ry?1V0uPe!>03!x14MIu3Rsq`;%4kmpaGDh3}@DD
z_*b}kv9g!~sgftC8e`tqv$yD{q;ok``emtmyw5a4a-I56<xHdIQtywK+hG!EW)rFV
zQ<)8PUJn}p&vlqTM3?r3_uH|cc0Uh;vp9_8r|ihJSOfd@4u_8~EKjkuj+GuR?SWFe
zWjOmH7i6@U{si!9<4SxR)M|3gi2Ox$DbH*pT`)#b4EgC1LIf1VH0~cf-;=q+i<4Oq
ze$5InJY;tR&C8V-O6S_x6p4-F9tZdhP+i-19r&R)^1oD&T?b1fM@Rl#FBhYnl2~T6
zTdQhYeQAcc_9IMtZCKWgHb$lTtB%U(**oM>Uqd)%I-?K0YIIjmjjmYFG@5WXKwlwE
zirJ(709}3++cg}OOPm?yfe@DwqMyq!5melG+48?M1_IV2)5AOq{oNh@mJ5Tho*@{M
zhG08I@}v?b3p-79iwHqNr!I%AUYTt>;?QyN(r=&XQUWfTOu>=YW+Hm<sR-W~u!I)y
zPwq@MLFWH<6iLK#C`Y2b?5|MfU52v}f7;(N#FJJR44vVsdm63|!0JVyAZqn-vlR$3
zXPidv$DkVBAE9Stwu?>iBC;|d>Ge0EQMrxf;tbdh3@xR(?rg*}&YGHDc-RoaPkhO?
z`KiPd=6B<Xy<+eINq4c`7@-ENE%9Xd<r6mzU=k7GIyN$YF|*{UxWaav1b{;Ut>m7U
z2`Ybn(?`O@Sy6IWwau~gAM^B9A*9vB%1l9mS@+cw$*^yN#^g@F0n9dj3-<>6nZ6yt
zDJiW=dt3%&8w8odiDAzGnJP?9!iWx4*|?O){#o>=fV0MV>JT;4SNI-%DF*!}Yyu}7
zUqc{Q@1)*EK@^?+0)eRNQ=z~(6M=ilqW3|fd)f6z(zt8r%5+AKlJN4DA*2*hko+Re
z6x;heIrG*|M9N!Cqbzwb!bcVbV@1Dvx(f=N{6-ePuG?zONTwbT)-pKK0woi_7IpaC
z+ln~^HWi*VzRo?m;ewf+2+9}4fM`$Iq?XTF!6q|t3k4g!0fRbs(CLC>lRVLoz~R31
zhD0N85S2?GM!zNxUM0Ol=HIJMhHTw8&vLf_YZ89Wf?YU9DC4ua2Vt^1(N5DdYHcq=
zRO6s$aFRE_MH}Z?8?f>)_-pjqK&|`|0xZowHVT%ATnN=NQZ_{`-H3Ppx;XSXnJaiq
zhKi#%7#?OUV8v<%82iaUHIQb78a(VfIi31ki;Jh5r4Lg;uc=;4O=$z!Hd39M6Diot
z;zlx@JF{}7Xkm@<NtpXIvfPxQeVvHA3^Z9wJI(Z;m&c(b8_tA1O`bJ7J3$^qfQL9w
z$r0rgT(NnhjjvUppPn7*391Vt?<+1*tBoMYB;0tKCSkwd1dh+@sD=<cvbjyLgDir^
z9e1Em=Lpx-rWNjh;VI+)g36t_1zDW{ca2@(ata3qHwqnTF0%B`J1FdN{=J{@Fy6EZ
z5^Z8{5>X&)_js5ki=Sx6k3!YxM7%53cRnLRsCfYxuF_OE4Bjo!P+^!mRt9?F`?Of2
z$-`otAJ09?(T)36P*vl#ZywQeM3Y--xr)SL2rdwp2GK1kbeo=-Wkz><7T#NH5~vGe
zLWuEkAtI9?l)Cftkihbo()Ho*g3~SCG$+(q%cbXtkNbQE+w7R;cPD7IImnI=QIK_i
z8s7iflCNYlgrN|Wo^Y~wIOOF(nym<2Ru@<qY-P2QU_!+#Obe)|6iDJ@G2$f0a1UfI
ziA>z=ST&Ricq4G$$SpO<S6Sl%D;XfC&FAAM^d55Ov%=7{UDW1Z7h{NS>h-A}HWfr`
zc6?+?V$|;_-3@m#XZvB6`Exf;R4)hwRCeI84-5km=G5CCc3=vKb&bwZnSif2Nm@(J
zfWpQvFj#c7v*uYNWn!hP>OU<>CunljRCu!J^C*^nD{$=qa@`9=Iplka*|;~Wclzjt
zt6<6B;N%8A$X^k-{{2-zZcd6>*(Q}1GmVuUxcm(lN5@V@1E*@C8g6&TR5At0O<)f$
zPAmVxEdyeq$G>c{cRVF;aHTwLfbUUe8E(wx`4Z;Qz>6b80X&}?DCEOT=G)Jrl_RE~
zfo+m^TE+dake=ka<=TuKSk1{$+8#=@!8<7>oYNf1HNi_V53Bbu^^9s<#@eolKH#Vz
zoD^iO`ATg?Qh#ztg@d8G&Us&qJ3WR-OUdcYgOM2+!ol}9zd2NsK9kq>hAu!K)@-0K
zbxI=MC`<^`){z>2MgF6JP89G9r*fiH=t?~o(`c?bRf9LwIx3#9_AOc`3pLFd|KQ{|
z4%lCmrZcELKGAfoMata{aah}HI>I1ZqFRKb!u=ZF#g*dF-$%Ne296VhynPIm9^|jT
zYpQPVfw2+=_Ex4IOHmDdG#E?w;h1I0kH_}@Rv+AKAmHRIzNjppm|Uh7nq^<|SyTLZ
zzh(Z^cGOUcAXOm}EHecQI!xre^TUsIq`P9^+56st9OEp<v7_Hf>C>Wut{s->nS>@k
z$5XzMr*(Yhis=q(C{~13KxUSc<*VgIQRE?N&xvL-z4Z?xOna$WOk;kQDGH_56W<Al
z1Sv~|GW6CH(VQ7C-@Y+&auE6+E?mJJy&IidRsK@<owRPMZH$VZ+{U0Y{P+m%xc<Up
z$<D2;a-22Xl(@}UsmHaMgf+voB=Uvlu-@jAW}b0`f$|MMGt9YI{Zok1G%8B0fSQ=n
zp9SA*K+C_qZKvrX7g7J~Csz|{{<nY7#h6`$mj&`c_%Py+x20yLfRTN&em#MWmdhpc
z!<MbnPPr{i-QtP9s@YoEENv3jfDl%-9;)@M_@gh{a6~dhh`Q%QzhuoHThNTB&{bK?
z^KvBCmS}QNlRK`P2{~^iyULFCy@CK}dUBTlIeS3sn_>hC<7IHMrT~&)zCa(IIqh69
z-5-esTtU6!B7m<M>If>dQf1|=!kJFXg(#p7545cRGr+8jqR3Nx(l;v2JkB0U-t(U2
z{y5{XVqlfh=N(}YoP&nNK*Uz~OrZFN3z1id{IWh*Q|Ik1J!ueBGDEe;GH+46wI6&8
zaWD>RcW>;4`PLoB?XJT8NNp<~uxw^=V6t;>wW!syW&<E0kq#nmPs%i?%fxa=K;LfH
z4H&W|2%!=Lr<mkTEP0+e4-&y3GwWrRf-9!raj#@o4cjK=OKy)xYnrG<d!Z!XO{{CR
z7>c|Lgx?G5$2PmzKa`m`mN3L!FDc;$sn9fv(P+h4kbkU0HxJOJ;UVtvv}GQJGpFan
zkBa{@NuMgoBLrL0+RhAn9RhVfA1y^1r5NnJ5^dfaL89O0T^q{QM$y%$4udiL`Umq-
z^lywoFEu4#n3Xm1Q8fYgd><ytbJ+etEt3h_qjevEOu(A-n2MD@QV-)Onwg|4fkf*%
zgGPAbTPf}|g%f+)ovbv@wM4!MN_uQ_PP>e8Z;YHjGG072Z^t-eLZ~4Ad6)8#3E4Vx
zpi?$y8`Ei=0Kz`zPg00c-8#$cUtY_KS95z#{o=I;f5V09PIDfm99eSl2d6jOBCq-$
z(*asZz+LM}&$<?dl1=~q7seFzI6b<sIqG70k!Om)oqF~XZIpovx>Q-kD4Jw;Cc`T5
zWK&#E*D;R-o`iVq^jeY2@v2VxJvq-8pJ9{QSQ0|+r*$;jEelZj+bTwB{ng9iz%yqr
zCFl=oyggpxLu5RSsyptN!UUP~-?1^0kliU5T!{&8pR7eGmNU!j+zo`}H`aSDg8&PV
zpFt%79wo4hzoz+ydR#(-#hjbr+DLHlQUkYbLi18)QJ7JQ3Pds4K7Whs@O}$F7=lDX
zEa37N@*fy4x*N>1d~aV}%xz$0`1;mIvB`m75!JYLnXO~Kt1fL3v4$oR&C)w`DYQ+J
zg*cl><&mq-OG!d_IjY_GmvIgG_HUwr9ACKaeQsK^<-97(i(-EwnMDLBYQw9Jqw>$%
z)yC6?9XN6u&+mMMZFOgG3I)x~2{1KIH51Nmc=s2ZGd|sK@DEq}9EKNGUOZC)`$gQ~
zOYkM=A`W$qYo&QEHADs^=?8ghF~O#>Sz0s@PG7Ce8WJJrYrfjh2m3jex+Co#_SCq(
z<?|YL*kb!Up(Y0y#tJ7C>Uw4p)wZZZng_ltOOZ_zs^Dn#QW!26m=}}q%|PM69=9(w
zFk3!2b$o)Y3vCYez<k)}SSh_A$RM%=MpvQl5iep=3GSW!q0FJdC1c?le9ve&Cw_rj
zl~@LO@VTPK3kmSz#O`hj`=etnv;}l^k>)yXU$jg86iqg;5sHf>IT;t!PRI%mh<h@i
zT}Y3zt;OY3y<w00DXDV%`DF;`E~iOmhQdO92_{?KgbupWVDH1Q$C`RXCAx1!SOrCJ
zq>p;fKV4kJ<gS84#T(O`UwU<_qpHjC4Lm%tPYRjy=I|Y|2PTpry<##=Ae?-qmwvST
zSq-3G=*D_Yn6Y9c?_xnt>mDoL=>g<0=liQ<ScDj*$iI*3=iUG*Znt@x2+{W4Hqsmd
zd9y2TElas6E31B+=KgAM#4H<;zs;-H%!OE$<2Q570lssQUR4EUNsC{);ng4z_g2z`
z**s?wCa8Q@M`bes4atX&_k^VEpVyMB;=J0G5<6iBOWp7dE6KRkvfTO=JhflO)(zWv
ziq@OSmrX<$!T)hRCiSemq-@>8$nWn1Pn7ZdSULG6>}-(i2S8o~PYjEw&j`%H1rTd?
z+NR$2`Q%%$k(wdA^1Bc2x+F&p4PriSZ!Eb*gFgMKS8mR8gWhu1LVK7AK+z-%0;ZiK
z=Q{=A(TfKSuMIw;;2)W}&ZzAY8}qdd;*&hSLyCr6Qn2bhcMr0j(+ySXQMRef#^ngz
z3>mzoM!^Ti>uVl^G8ebs;=H39^us?TZUWMa`dGchNRde6!ilS*kXqe*^9L4QeM@7u
zn+V)O{_F-f0D%nD>(vmO({p^@FA^jEj(LV-ASwZEM(6olNi!0E4yt|WK(G|?>4m5Y
zMuIn2jtpB(p?kLb`DWSh&XfhUP4c7)@MyLvfWTu9Yo{Hyq(QhH*N7Id9u;K4YDPBi
z_rq5LCng}UB1KD_dUWf#SiDl>qIarNJx1JOuBtZL3VlZw$t(Z62Uff})iCr>yPEim
zIm^B1H_n9Hkza#R(OPn|8W9an4=L4c5u2y}2%a!=+sO5izO4WP5_eLk&k|{fbOF_1
z)zV)vvYjSXBEll@W4<+0?rH_yjVQ%$q4iEJLp-xo?meY$z%?Un>YP@ohmPje@dtG`
ze11VE8a^UlK>1u63>9U#LF3Fs7a9Vx#a8L;Sc>vU8}U##0{)~`HKroD<OQ^%4C|(}
zdE+H-0_hs{eB|EMhU0GYpD13*+m&r=9;bCB5-k)xogh$Fj3qt8>ApD;DlEEly`smU
zs(^V-{l&th0TjNt$3Jrs3Xz6CC~x&#NrGxJ)1*SKe)Z}q5L!8-Tprca?szGcrw*u$
z%J`^2RF$1@&WRrS;FkFaBctLDmYx#$x|5jg6!M*Tn}`fwu2_5i(lGES-fBNLLfr>O
zIP>wM<lR7^@%7yN&Jnct{ig~H8Q~=b$WM;5I=K|OrD;(hks0f1^DIzGOtc5p5%ZN3
z{fx<OJP|QnN;suVMmDOw1Go>q{xvb!R5*w6<f~3Gml&~XTh2~Kv$8n>-FSSACg>nk
zPb+RnQysLlLen|XGF><-A#a9;5AJFYh~2UqKfm5{?y?1Q+ehw!V+|75O4pU{m$_jK
z$&yY?zV2+YtOPcy^7-Q?5I7A2{?s&%q=a-a%$zR&$O9d=Kns`3%XwJ~iTK9tzwAbA
zzjRW^)1_7%sF_Jrcy8@s7Oa<*-CM(ZVnEcG$Dr^CX4=9<h#J5(`%~0$W5x|9$kk-;
zRem6Yq(kdI-g%me&P^*f&t-5$u4R6qv=|j7gk~E=D#Te9M1ruPN+-};NJ-(E6Ritc
zq+DrTq{l%@?$rjeDC5sqLwsR-F1f~<FX&UEL<tMN^1ETBBqwiMD}|JD#*c-^qG&@s
zRjd24wu7bV2y!3nKM#o}Es8_>x;$}$|9m@R*h>}tni`T3!=rvg#P@ZM!DcF8dWAst
z+I&$qgQqzJe{|<_e&h8eU2O-f*xWx40qngPG;7sTTs@WX3kDHy{mSKmP2=*+8>yiv
zC0<5bFBRBQxqZ|Z6DSDKEnEsfIe}Y5*H=F+#3eo~Ns-|~ps_Krr|_hIrcEnZv-AC2
z$xki~?0NIN^PVR1ug=Ago?CaBHIFkD>6T92@eRXM*nCTsc0R!?lcQs(CM=+=g4TOA
zw1g$HN5p-G!b`gQ;x#b>m&!;Ua3b3cXbqR&{locujKBWm_>LRgT-jdwi8)z0WGC77
z9luTmD22_=-KF831@IB&vSzdQyoF9su8yH?vk!3cSe{|kG}a%eAwzOBt{9cctduf=
z=q$n)@2v*HRz`eAH@n~!HNsq2ZAFWNu7K1j(`Ya{T)9UYd&j&)Irn79IpN8mur6Wk
z-wf(8Dv*|W8p%6!kpcpp{l;^mOQ+ldoZH|v^t;Z@OgDG00pn;PFWbesc?xC+HQ>+X
z2h_LE(nY#ohr5^=yqxe9TnyB7_(heW78ZXaHQCV{k9m`-Lsz{|NSmR%LV^9Jh)ra*
z2rAmkyf<%X%l&@ZWr`R8{ni+xQ_849LY^l<r(>T>v@Hl|Dya!IzJOz7kqv?E_s@}O
z60crf6GRc$<=Tm>s7qbMgv}OAwp{s<u;G)T!kn06^>{vOB6lN-(vfu<AjJ9R<*}d~
zW$d2}z`yT}yjluZ4VK{4&7}8f*$JcloT*ZO%HYai9SPC~M>XF9Wc}zYY-{AC#zKn<
zVgjO-1NB_ol%^#<6L?$=#BvB2V^E8Xm+ukJtR4+f&zO$0nf2N0!yLtNb|)<>_3NT`
z4zLtz6bxm?cp#~Y_^n`$E0WyN)cjoCgygrtL3&YlQWNrJUX_;Sp3`aruJd_5CY@F2
z$)cwDyC<8FB<ZA-k^cS8z|=R%;J=j&2Lbi3RoljRiHkF!$ICdn$M>IGY^((uYgbwy
zHy)-7BaqXwgbVZD(n7w9`|>~ST{2Ah?_?ozt(&)lM8v&*!}XWUYQ=4#!Yl}wnYV)=
zz7ISpU-Q=6jKH9tu+}eflR_=_^P*6P<`w-SByVCmZuc)=l>^jCChYHUOk`_IE`2Xi
z+ZgoH+^n!Oo*@t!ub`+%UbBz!+Iw4t(#i12C`jed8&Am^a+rbzLmnjYG!2Lj-8gSu
zJ5~2<ooYb9(7f^@<}*)d5YsR)%H9{HWO*ciW2-<^!E>89&~4dU)qe`DB|IUli?K{?
zWmN{!jyq@-=~#_gdVRhgC(=+MTE{ND4cY4+Q5tlAH?>N8-+$YI1pMWy)B+Ba7}@X8
z<FaAmi1|?q2i+UMHQtc<RsqAsGQx=metR258p;e*lh5>FYth3QB&*!*wBwr7-I!{D
z{#hA5xWO{Er;6T$cLK%a2N#5iY_rFn_n>Zxq-Q0sOsQfJ;RCh$ncArt_gBICG8A8O
z`6}0l956|;W^G4@N(z}Ad9Kn3B*MgKV_IyaX~<%hnPDR&xjzi*^zxaW_#m^wY>=uj
zpm21WW)NBBHr?`Y?8l*PG%IOXGzI!p1+phu;Z9QnvH;yOXFXB(=L5{UqPX!wLmOG&
z_Xa~J97=we@K%MEG%mWRM1uw$5vl7-kDs-jCJT8oMpuq<R7RwBJB9QWnMSc5$X<&$
zEi2J*Y;B}*A@qNEv((G+<*dReaB^B@bW&|W$Za}a3onW_5royRQlUO~rZn`-Bv{g%
zqFa0!FP^F(x#GlId>q!1)nozI$4|*#)1+NbTbb8cWbv;!!jWh=Xq@Yy8?MZDmp&g&
z>e}AzTVx}zsQ1s@)bn)%BP;NHv*e(jchiFzrVfCmE9k4jbqA@0?e72JgyvXfDFeJF
zI}CsmOtyUi{VzBO2G-XOy8dD;*l(k+7}l{*Y=edu<^J)Sr%nQ&zs;-FEEx%ny9Bh0
z1?}hdbbGkhnudo>qlV0KgJ<n}I`2%CZg0h;jz6SS^P$2hbS+N0d1zBXo7BOuiSyM>
zG_i?*+OM2g??e35*|R-%f@FcCKQUfy?21*df~3U7hg`!klx~J6=-;mOo~Ta-Khj+|
z=3A_v8d%dj7~(`<)=+nmU5GVr$R{<Z>}rZflB{6=MBlfot7ss--Qsu<*|?9SqQQ43
z;~WC(fw*Ys8weuyQZiZFqC+4Z9pPsTn~dQeAz6eaZ{7S`RWzHNVJ8Ye?P6mv04}z7
zh)SB_6lN9kd90;R2+RDgE@Cpwv!y?dO<j-#T4Hhm-C1v%nC5+kBj2{ju0MX{B>Aih
zvCd75^&Uf>|F(7oGjHxW;@rksJLk~Wh;`=802CPVLuK2Bm6Dp2VLADn2O1U;@-E9(
zX=|ANl1@}i`SHS3M$V=cWlK8CAW@16RXEyHZf(U83S}((Q;vqeSECa_*I3D_Kj>4O
zGtoA0gxG_OkY{MUg9r?SLi~4f>GJdn=@}ecMo(?AfI0(yQbGVy^6dtrn<X4G{E<=Z
z7f!!ojK+J+PE=yl6yi5B*@j0eBVyfY0w8Dq7!4;ZY_LNaxm#&T(+~{o@Uv>mvhA2f
zR#hf#7M4U4G&*%PTQA8B{x#_yhq{M9bc?ENlE6ur3QUtrvmZx~lu}(yhj?kNT<A=H
z%h;1!50@JDho4nbae#g^1W7_rWl0{Yof`iX{{1E*hZ#|wDu1v_uaqA_G8G<%0_ivE
z!^H`qH(f8*Vk>2bZ1`}W78rMyxEGbQUY5aamgRZ3qdynV?g3K{?+s4FP&15GlwjAF
z1X1kxNZ6u-#^gEhx7K_3ye$IV{=S_tJ$kf3lV$t86g(Ihr-ivEP6Ra84|2K|+jseN
z*CxFHDI7XZaxbLjHHkC6NAYjHpb>6q3%|$$F<Rs=8j$r--LJws%}ol2Cm+2oq_-?q
z*jp~ySpOrY#H^BnaIy({DR+Ye`ls`NcJ{s$4a%`TBfS8Ony3Udr}I2xZD4j>b`*H%
zNwR~i>%m5?)jg#b#|&mxEz7o3?0N`$LaBOLE&2J#KHcdqJS;MbFu?8|ZrIK4n+lat
zT6f`6SlkyXk2YAxPWNQgsK2yx_EWQOR;Mp1JxdLo_j0XYBrb>a{9UgjkK=1zpTs-L
z@ta%qW#D;ZgZ5WnoJ28RUTh_lVgOk>qQrw6spRyC>@Qq)nA24V%473tfuCJPcTkB7
zbXw)S_Xz9wxxnY8lM$Tts%-*R$$oGy_vYdk_;^YYLs<Pgiqw_aPy?6<KN&FNP<E;*
z<lGj8Oz$?Z%`)^~i(R{O(1EWYH9)3LeiC$MQ$7Q8Uoo>V;|pKLav#{s<oVg)l>}E%
zS=Ch4-Td9?fv|#qrW5)Mo+M5WFdY4B<-JeE@-ektMjphBQRLfGWmdr0H9Kg?(bhev
z3X&Ka<>w72X;e0lbkqnFO~sRNky%$$7Xw42g~>uflLcd2oJN;AT+E3UPvUD<UleD(
zt?DfH$-~oDr}T<vznEN`kd|Rk<F*C}MTf&p01YjJa{yk4G3fRk*rQ;erf&ZBnk*1E
z#Xji0N?!jIKonM$dYgzD+A`)_0lcD1x&WelZo&&upC5imS%7}0Z9ZrEpUZR?hTh*@
z7t8q(Qeku@X^X|P?z4V5BWPy{W@36SGbi!g8|d(g{%iRwN|?_rn@dE}c^tz@U3$BL
zz39c**6P@yW0Yg88q@i=ROQ(I+TMLpUR_-rId<s3K%o)wZ~!Dj{Q6U6<=c+?uDTxv
zmK;~*?ad>9gV^oQmrFHgMk;ca%*I_vp{G%*r=WG)EJnTRhuZs;7tgE+L);OHOJf^M
z8!CRkx%<~6SZ=f94z?#bL+rV{t)~%*C8_4})rbhRW^CzH-W|i_W#Dj$Ie%hU6tDw{
ziF%(lhYlMC{o+-eh)Z;w#z-?P7CEy*x3on~i6oV6ugO$j=2l=4d#^;ckEXsT$oHFU
ztFxH{uYC;3SqEnyg4y%#C^NdL_KB&!do#C<zYEFe1<H7NZ9kwIvh?@s_`4%X$;YFM
z4~+RSnrAvv;!viyI2gYHIg7!c{k>$mDB<5K2=`YwE60;Nu%Z`QYRUg+)a^F9t-)~u
zICvg4MXR>d0-phjG&2s?=2t$Fvk@~e69T<Gwk&-BauaL1d~LgP=Z1IwW<2j(WF+bs
z+;5dLTNCSLxsc^k9P7tm_i1fH)%ClrKuHaG1H7UmW5B@Aiy%!gmetJG1Ue^MCmufg
zo}j%N(b%9{-2q{peVg6J(R4sRRo1F4mnKjtbjR8<<Xrmb2i5H{RIYQvIO<O1MHjH<
zJyn^M*YY1|*R2u)y~5Qxy0>!=zm${%==RG`d9_|z7|-=giM&@cgt=IU1kuAw*8JM5
z<}ahAFA_=j>jmW$e~z8Gt_{V-eM0l6YZSI+CLmkT%0-eb2NIVjocYrm<uE|y>E46U
zm}Dr<0^}R<L2=QG(-zkKs_5(=(ZB$9`!1&2b~j|QZp2OV0|;w<KkY7-*~+ALl%}LJ
z<^r}b@Qctm?0aZB>_hz<`-hpHDZnMVh?WY(bZj@u!vtxF2My@W7NFB;h<r(P5?PS^
zieOrK5n|gg58_U(XzDkZ?3LT+O`A7(reCr1>q!_X$j@W+K-aAZLNX(}v{Qm8?1jfy
z!dh?U99oZQmyMRi%?2vZJO@+gcq;;Il1WSRrIF~VaPcVNdClH7sFm?^!VA+CRo41-
z7QiaO)n1mslKDDP6S=+5F{`7EIbmdhujMlwg-=En>a)EB$l@Hnoz#KyeWMWrG<uM9
zW6d%!l%8#ivEnn|8XUMTV->VeHJE5H6r)tZ$nV?8uEySLZUPKph%r+a#231U2De2D
z^kU?6t1FAQ@e2060jzb|GrnufN?#y=4==>J8|A?5LMq7n%$(?cUuA41`LBk}U<E)5
z(BcojZOyLU;)D|0uGVWtKg=>8cNYz_1?dbjh9#lwUJR3YLXk~xf~FhX6z^GQG>Fa4
zt87&6{Ksm-WK}p~s`|jLbZcK?eBFG?)osJns^k?7%@2dp8n<8*>Nl$~3eG#S%to-P
zvg+Ai_OH)kIje>`ZzU#6T_WEjpU~Iml&Q-lYcBaLs0A?1=e{-FPpB{x)S)b_NCsj(
zhbjGyX!d?VBF*%wvhv&C*>MV@tRY~7Q9JIqzIO7>v?m%=;U84vKG}g5q~#gP&`jlo
zIJf$fhIt{PxmrdZu^zve-Ex5PW3VM24Ii@CJBb`pKChuJ(96X7GGvP@+|hnz=ZZu)
zYk$0}tv}jc4boBKU^GvO#gqDw1#948ueab3z)|0B88-W`C{ue{0}jrnoLbZsBYDV8
zD3vstE&q^taSvF#T7%4)+RVJDA|A~@^Rm3XCa7M=o;J5EmK&@PD1$ao8qK&JN|Qu8
z6ju962_=VNqE;e3cN!04#I1&}&5h3R{wi{EaOf<%@HBI#qeUQYYRyJ1jjq(f69O4J
zV_IdTL7TGl>`XP$d2ggt#uB25kZo&y!YQNx2=&ul!*#Ota)X>u6q#y_;EMRE(Uoju
z3*|3|SL$?oA(4gPRNrd*U46!Tf?{Ns7c{1!40x&l%(pnXZU?i2Kp2;@22wPcF?cod
z%Jp%%8y$b^sjsCWuIg!Jh&eIAry&pTE3-Q{po0JW$_|TSnnFe$-(5Srq2ZK?h}wn2
z#AZm~cmFWTnyrT4S!FQ2-y36(YzGo1Q62P4?*surxErgSDqqfix@`kU<KTg;S(oi7
zk5>6VVt$zagt)svuaa~77kpNyKY44tmx+eMtJpg5hI%bLGDHJhgB38y7<KSCt4PWe
z<a?U#2Z6Orf77>L_d31i`2CtfMkK&Vvl``JREmHXTP}A$ZNPUa*W^+s+uBEMLCDjq
zj0W4vbf!-SGWDL^5xcVb@s!UzNUwU(oX{1GUa(FoM11jv{0S$N;SqQBZ`JWbA|6L^
zc23K{Res*UOCFgmu<>~s>TFp`2+|MxBtrzjlA#}YLH7O&Qar1xUXsiTeYGaJ`&vXL
zdaEhppt|;sYLj8RRF|Go3T=3~zMs5CG{Y64jxYaMK)73v|HZ%WN7J2d$L&~(;7OJf
z$$5z!mjCV0?M9IhnCTcZJ(xSvSIp4Yxi0he%^oevs%|fP0urjLq~=+o%`Mm8{EQ<o
zkPX6fTJ(J`LOtHyn^z5-BEZPcPF1_*BZQ3?nqfwKqdnqxm;NegZQ-}XexF=KQ{ZW!
zOj{x^hrjyuK?&<}sL}!p9r2M|<=i^CU3~!yP=}U++>5Wwux1#jw2<HpwAMxfH!?)O
z1iDcIL^N3${X}c@dlz)~);$}4#K5S2H3l2=fUh%;?Qp^o6YFbKEU-rh+Ls|XmiAPN
zV<o2<CytVSD%YE;OQc_@A>AM(u<T)`EGkG$o-64c*P_k?B@MFh3w0VK!%Ud4&o4(J
z92lA$2S?NM=3e&p+~~35++?=z=Gt^CwEh6kZW;msHv!RP3OG5`M^6i5=*1;22|S#i
zBni!eeLGezGi8G22Gn`2_~QOGywV{G5#l^4{ZeYMv37+>3EJis_VC1{OirbPMcz+A
zYCbsalh>3VkJ610{e{;N`+j1NH{_uP0c+BEH}*JVAw;rPOh&csq43-mD)dpoUJiyc
zk5volBoc9;@ug3}GxzjFEAkMq>UXM(=#|fDhk!h!5lWnMp#*gp=P#R_ecWRD&@WYY
zvl8S=6lwE@Ch2!1Lb^_do|r#kKCIhq*eQSE1o4^`KU_q-^tI8tpi{%caeMgv!SL@e
zp4)|o;vv>KM`pK#Dt(HCj1ZIMyT(_QF}ltik!-wWz*XvLC%K~G+^7E*UxYdjG#;K^
zq*KvYEC$rNb1PkQ8^cw-sPri%G^lwtd7O$OS|1b{{@h3Nh3{>F6`xFA<7T5O7@AK!
zg!o~BYa~2_%}qG^ms;!0VZD^(*PG44*Ir)y4kh(<{{+I9R_k=&jO!UIFQZEv(*A(L
zClN1A6l`AKaPBr6b5}2u$8ux|*cvSfafBikd?szx;f2$R)LEXR=q!?+pQX5J@<bk)
zU3K&GH7Fpn^6XWYs`qeK`6IFatV*L3YsJY28){jM4)3k^SC=lyXYG!(p{35`*np(w
z!^Ft6ED;!2j(7k@A}U{~#>C(e9Vpc-4>DxlFA8p?j1`${nttBi-;O|j!!2}t#gSS*
znk`LhJm(v%>a?jx4mg&mVNxfg)pQ```Z-*B(Z4UnS7n6)`_K8<&OC_AF!@`i!%rp8
zy`l(>ZM#TNnxyoJ&J<+<vz50vQs@sub*g2?M8SbeCSK?GG7Xc)e91PC9&-S-rKCc%
z-JpKH>EaqW8V_97DA`Wv8bYq$S*RdG_~p<=YKPq)C4`R5ju~S^qT+->jC90>M;0he
zt^q=PQJ{|@OkaLJ=Ma{Pz<&wDQ$f;2i(C5T`$-UAMT5@!RBpZz&vZZ=Ro#)grt%rw
z?y*$T4ipUUyCMk_wq4yq9mER|f0xLqqg<+0;b6ax&i;I=T<Z%-Ghy>#Q%mAdQ+P|a
zK=u|!*?}k3T3F|LcGlxAFHvUclQpNGY==lovr}8C5JdRw-1LNgu#+-4{KOu+9JYFn
zkWndX8Xdi3i1NvWlkJPQm7l_K`e>L&PWT?&T*Sv(if4NX=`HK@_@vA)_VPB9B&vyv
z*ZRX>F-xq*Q;N;Ax82z}Qd+W2(Yo5DEo0O>d1k_E$4>U1j9&0(apNyz7U!JqeAqO{
z1}V_=JSn?!GocEt^g+C)&OVOAoov>kwB^cHZ`l*|`r=s719@s3Pfpdjg6)bxqTw9b
z{yn0>R<)ooUGPcu%HELOzjK$6=<s=z%d=F2h7m+|M9y)xaP8}{1-C&_Yg~NJk0J!o
zr@azQ6U;%wNaD!iZ{1$;gVW(AyU1)y{LZACkV8GtVR0;IXh}eqSXE$6UUkPrj&Nd$
zAIRn;MS?iXrGhdaO?t9Zd3`jC$DbxtugFQ%D(j0F%fan8ozfg*hne6C$SQiNc*HB5
z>4g?nga2~J9V{QnBVQyd45yH}rLvTxy{jqGl@W7GW$Xs#AdsKO*!wGw_S)o?ATdv*
z`tVd~q!O!n@)x(@IE<G=^qrs3)0*H)angyZpH({>ie#svaca-|y|tSX%G-0lfQ4OC
z&GJhZuGy_&iQqWWu**&5hJxp^90ms3PfnLFz3&EZvj%)}h)QT4p{|fJ96YM?%gN#N
zn+%RK6r!Syc+g%%J5B6l(;LsTfL9F@fe`kPb7r1vNcdb*0D^kpj|iIXwKmbj1hRWJ
znTX0+tY9_R6~qw&UDQeB2UO1|EKJlU`tr&$^irnqc;i}v5WU;zto@ga+iRPyd8kQN
zzlwD2J4&>&>tkEfuS&LFH`9eRIhn(5<W*#1I*J7#`~WTA+9go{S7csV6SiJ+ZIq>8
z*y7<jad&kOBcc=L+A=87guzT^_Gil9A&F>~TX4^_WQ{+!Rx_Uwtu%%E?$G+og%Qqp
zc=Bl&cxQVQa1nYqLxnsyX%&y-2<&gfCcX#9qp`ZgqZ9n$$ovI6DMurArO%(zpSaAH
z9pziEmxA_k&1E-UYl(_H^Ct|>wS*cQ6Opqa&5E)9lXZ)AM6$tWK8cZ(0#Q&MwMOEb
zG3omo12z^!JkE8;(;z$Tz1^y-T8AJC_$j7mBgNA-d=kL62qwv5Js2ANm(Pd;q20^{
za9#G$lnZLCqI(TBW(00D-&u42j*NE?>vZp((u5v+(Ap1S(yPv5LBMNcKaYIEz<qR<
z5P>}UqpCHxsI@8FF<y~P=}U5<=f<iw<ePL{M{C(1#0Jt86~I+TR+@z#m}vE4kDf_8
zit5zdc>MLbo^z|b=ySFexJX^(e}#&>_>+|owU2Oa=s0P=zU>~#GA8P`v(=!%e;>hZ
zol2o{PeIYFkf@tljeM=G+%;2tL5zYoIc{hS*sL~_tiBQZ_uJoH%o}YT%;Cx^BRe1m
z6$s+P=eyo{fulO|MW#{GJK^+;S3vbjC)OYy{~(*bZEK_yz!6a)XlexSpO+>0^y69~
zc2~+_EEDbg)u`2JOt1-4qEhhs5h5ICTV}~X)YjJ$T5=hXN@_nrZ-4TaMW<r-v0=y=
zG=$zts{}2eC?b@z=2_uov;|7mW5Z6`NNEnJfhziVWl#N7=vBrZ)WYN5GU@WG#Gg9^
z7bb4#(nmSetksw0*_Kt77NUf)hhui6atG-qI(l5oxWCQsyN!YYD_B#{>*G;q|7i|z
zP>2iZm~#bS)_`oNA(DE<-~Z$A(l-nFm87EEjnaa$H-T?U2X;;z*rQn(<~_PsDW%Dz
z+4UPhD3?b*a=8WSL-5pwY<7)0zgdRbU!NG%@Ygy5udV<@lE;PMO2*iG$l@|p@8jL}
zH?rZO8+ZH29GO^Eiy00|61CiTQB`jNzDAgNu85&Gp;xJNtLO6?a=eBk6XClIHE#@+
z57@TSt*3qAtgOrvs6Pf%Z-rp}{JzmDcOuBxfh_Ddn%I?vwNMa@?C&=U?$+hzIXC?#
z-|8$YT{^9H4lJOb)eWcnoMpk-h;ByIVKGf`n%gv^i9!p9^5JC>;NkNzEwi!<nPiB8
zwv|4L`#AITqYRDf0$P}L5M8rGQE0s1KObRgB7uU6GaV#f*=l9rhrCBCB!c`^19s&A
z&g|~MVTm!asBo(Md(`_@Mc(gjc!jEaNS}LbVk#qQbuSvuY<Kx6c>U4ngTvw^-+Pp;
zIJlzfUV$$@Hv=#+CBY%gPJ$3%_~e!x(Da}CYj=?Q^5wrwO?jaJa{+|tKE6q%&x+Ml
zcpe-A!(7yiH)=So_y;-_n)8xB6v-ZbP|~Ky`p-6IhQmI`e4C+K?-*A>js+(@h|rY+
zvd_M&y7h**1~fbKejj#FJ^@(j^Cb;y1yU$A2BYBpN7I|dCSJHxo`m&4M?p>G1@8`&
z?yzjcyM*$`L9f2w!Uq1H+HlA+oP(IT`Z~%abEbV8(_}2EPF<PPBuV!RbcgsFjW=7|
zb_Frgpl`#UgA;JVYFdzUja>|7McXr(w1C<@C!Kofw>z2=wnv^k6Lf&L{rpMV-&+>0
z<<GLNy6DNObLxY!8JLU5hcuzZ(bO?!N~?+EF7m}34+eHq4wf>rUABdNJR=CGSo~;?
zJpO>E^!71+{bzkM_<_4e+)~@FW8X8ZQM>5mtFr15G@KK1&Do;dycuhIH?WqIFJOF|
zKhwdQ!V|jz_rO5@QM#Jre=VX%L&+(yb<~9uW>(qT^iw8gFI#Gln{xvKQ8l;t>+`W#
zpgCi!lSoAcT9SCy?NZF0B=7TZx5NVh3e)#LF!MZ{bJq_bL~JSM`6=2&qSs+cQ;TZB
zFK{jI!-8#nZioNQdiCx_=0oN*BD3eKo9>gPx1gPT9gvUpS+9(6lelDX#KmpV1VLlU
zPgT{dsyi=1zJ}!U6-Z&&XA*JmBamY$zq8I-%{9PcDwMFzX9-WZBe<fAIzJ~PnY~DU
zW?Y`|T<R}2Vc)S`e4_RUle}F1zV%&SN`T%7|5ZVu1LwMWL)T1w!lSVQ-7gDhR63QT
zSyKq<G0p`Bm#e@5coVPCQduTHz0zEeGgBQ*kub^dtS-|zeNTQR30Z^BtT}{?u~sHA
zy3#HmjY{TYPnj54k#Y{|_Ox#HIL9VDEoB9+!j^oP*=IBmEZkWnZSheP62BMF{`R0E
z(6lF>?km(Lg<~P&)4z{m%C9<wsNIVAZP4&<15RHDKV;mEf_~#5*UI-k*t}vxrV8_~
zo@^c1;bs?QSrJ$hJLv)OaMNQcNa9D57bmU=var+Doo$IPOQWt;@t07O<3e`x_)W3&
z@0JR;+8Hz37=XpKgvlb^Ph9<O&ODFK5Es?XDcjdfdt~F&mw<BO$5H6D*@69?5Qz{1
zZ;Zfz+Q1R=co1rh_PcdLZ8WtS!v0J(ruGT8x~$s6iMIwAgs3u`1ZTU6os-PyDH^?M
zDL0GrPLGzx8^L|br<PMV#=VPCURz2_Wn^=JA@pSkW466{fb}F(5c_UK8fyCUn-5%`
z&mN7s+B+I;@cR1s#!ZsHS&}3=%RXh(jumc+$)%+0L_6KyUB#w!NsRt<_x@V2F!L{*
zE$OtjxDKM#mXh~ZA!%bOK?vzuPt1MP>&OhB-{1}5pB(r&4U3UwyePhUE;NelN2PDN
zo?0QkX{6BvFN)qW0lgH7zHB;Q;${#eqo@<2j)nGE@O2os4>7b^f8VlkOavfwpmedD
z0VSkE=fsOL2rf&1eC-I8@3G&66&?CVgd8I%^zO_LMZBTt3<W9PCYy$rfm+I%6kG3l
z5C@IL(Y!K$cgZ=*b5opr70_AoR2Hb2F7{f%`0_PJZLZ<rA&-i{xAW7T@E=0~mx`I>
zuTBd9NHx-SnNxVA*dkV^Zh}OfE>I*({iM|^RBP?6CW+*T7vM5ye#Yr=b2=(PV^6>h
zK?P!7a2l&$iw}5=#AwwSGS8FgxQ?MKV_ElPJA+z-qlb91A7FsBrg}wzaRt=$em$Rt
zm?(L?pci9}vR+b;9@a&4Ijz@{@Y?ZRLT`S(yv4g8o!J+(WtoQ7l{R`Rj`fflJv9vP
zQP6tcEYMJAfX{^+gmj_hKqo+vLy-W+uS82%)cq~Yhal&nJJ2aEkt7qiv2PI-e)fGY
zx-+<E;xuN6S30dNx7G%!i|&76HO3gHbHCd|_5v?7-oJi*Sn(I>Od|5P;G0}PZ1SEC
zJj}+QBc+8mUr8CkfnysM=)&v+KCpO|spc2K$Vn_+V#x;f^iN$K5)x4BLcLS3lI_O6
z;R-}i&Q4={e|f}gv-!lfOBip9WMVYz4X@tf&c6(&>ihO<B{MAt^PEM4Q*4l<vLdha
zssqi#u0)AvjFNG+=#!(>w+CnnRkDTsL1&x=HK>PCn1M)a{mVYcdv>lub4v+cPHVZ0
z9^@5_^4YsbiK-N9!)FuJh$<b7=h_Wa{6MD8F}pu>K$<aHe+o`#9^Br&P*IsF<??qx
zzJX0<p0~D0bF4<9d4CTw7vVy~HvCOo64xnoRIKJpfh~taq3w9w95USt0^FHhu5klk
zGi8igEgKa~pP=%!vIko&d|WNkjtbAL1UHFM)rTGDEQ03BcUrMf$sv%Qj$ecY2`^9*
zXv1>?(`R|73x1}VF)nh+VKXB8QHmf(93VpiriV^A<%-y^_CsxHU)9!C%+NXh>vP@{
zw#8L#RBE{^4^fbDv;>4PPhQ?H?MG+1_E#}q9g)6f&>Z}!olO@WI^mOUTS%kKE+%;F
z4<(iyQmrf2WqV~u274#&f%dw$*+Q^=aXtCy`_tzMUx>8AfjVeq9(Evv<;IPY)<PGu
zG}^KS$43%Ez<AM^HL`e;z-D-TCno_y0<c1!Jv$J-QIA7&IPApk$DQb&28WmSXAskl
zm<qMFS$>B=*n?#m)U4U@X~iiqjIYHNuJ7y4k?^GKo!a)?&jU3u_%>EY)9SA_%hYoZ
zpZpb(5x$o`EEleg)$P|?*J6~GTH!%HH?>cIane3DT}BqI#CCgYJ=>R1V>&5=#G%(C
zyQwi4GI-E)7S^7li@zYgcHYIczIYc-gGI_KUSgcaaUky|l<j%kLt6e^iTp=Iii529
zp>!sYc3~ga!zU`w@A`*S?n2nlXG^Q*+g9D5dot?4u@zZyb7Wp3aq}`S{3oKp7eEH0
z7jG*#z495cU@{y>2a@mXdi3I}_(e^$Zq$t!l5p%6vy;rCY%*A&;;z*EDh|*E>-VCB
zXS^y*G6&c@?t};;U1OSmd!y4;Gx895$CvLzocWYoURWSIhunq1Q$>DIA0|v0A6Y#Y
zkkF?}J)ut$rPd#^7zjGcEW^j?l582iGe}@5E9rB6DT+oIRSh)@zs^oUxoOfWV@3~F
z-3GC|p3H7Jh@=R7F|G5|oR!-6z%GU{i&t2E=3MXg3R_dVKzEU*B4Holv@qD_$bL2o
zR?jUD1<Yq7Q)eRpXJm}R{KuXnyEtTrtPSj?mOX|OL1G3ZIkNwf@#8-MS8H~_E|XZ1
zp*=&T@sI4{#CPZZbP?Km5n@$}CiK)1z_1vvr0_xSH>6R4jwFeju<7jJVfS6o4|Dg-
zMzRC!x;I|4LIw09FCNFskQq;LS|!c1`keISMvH5kjwj3H2x#`Fy?Lykl9KcQr|`ws
z)A2EU9|8tk0pPR$=Us1I*Uuvl_tF}b5c|U6<POck<oo1=mOayPK;4XBF^}4_qEo+$
zYJCqCD3~dKfHw?C6;r4Y#&?gtLY8dGjU3l|wR^~y%b^e*A$DTw=`pU2VHkO4?ib_n
zc2ctec?1R{qARp|JT0XNNz~apr+vENoD*6)Q8XNsaonU%E{hst(iWlwTaPrS0*s(6
zT{EIunk?s|kBOK+Ig04+kNbs#fa(6pT|Md63blGdTg|Eqy+z=4#~so*&0p1$#ltAj
zs(Hv@i}2@PzhozCXZ{qpD^`03ZWpF?_J5=MJ}%clCukgrim>v6*49ca3Tw0gx5Fl|
z<{&nFi+X->u7`+z!;CC$i3`)tT*sUj!B#;E?&<jgTPp2X9p&zgN1$!r_liVtV6w^v
zDe9$WKMUu&$J96->Ja~!5z=ftvb|G}%(?A6E-v}24akD$j4nPMpCj_VQ@Wg<h;^fU
zpY-R4o8KKV7#;SE*;!%#=pz}@x(7a8%o<iIPZr@A97D(s%eASPD@nWy`l!v;5d5YY
zre6${ZQ5Y^NZ<HXI8WV-Opz=gg>)YyW(YxKPKQe~!HBllIYvwMBA0c#^Onbv?uS+#
zq3u~)*W<5a8ycg8s+|;ALDVtCYnVB=f#9OmsXGWrX}%pwQpG3tNxU;L&SJFIKHltd
z5L{_VQ2eJ<hWw9GSpg=1g&nyUib&{j`fSBquNON5!;EP!!!OA9vHtDbfG;3%Stmgz
zWXD>g7Wd#+r_Hlqvv&uYH$uQaZ8XIH6y9=jKs5h6C?Mc)OCYd+=${@M2<PARPqUr>
aUnThe6w~=2{}%;$$OrvD{9lnb(EkI>Cb-Q2

literal 0
HcmV?d00001

diff --git a/public/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/large-result-0.avif b/public/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/extracted-outputs/large-result-0.avif
similarity index 100%
rename from public/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/large-result-0.avif
rename to public/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/extracted-outputs/large-result-0.avif
diff --git a/public/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/large-result-1.avif b/public/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/extracted-outputs/large-result-1.avif
similarity index 100%
rename from public/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/large-result-1.avif
rename to public/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/extracted-outputs/large-result-1.avif
diff --git a/public/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/small-s4-code-0.avif b/public/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/extracted-outputs/small-s4-code-0.avif
similarity index 100%
rename from public/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/small-s4-code-0.avif
rename to public/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/extracted-outputs/small-s4-code-0.avif
diff --git a/public/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/small-s4-code-1.avif b/public/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/extracted-outputs/small-s4-code-1.avif
similarity index 100%
rename from public/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/small-s4-code-1.avif
rename to public/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/extracted-outputs/small-s4-code-1.avif
diff --git a/qiskit_bot.yaml b/qiskit_bot.yaml
index df8a4a9c3f3..097ef95f7ff 100644
--- a/qiskit_bot.yaml
+++ b/qiskit_bot.yaml
@@ -833,7 +833,7 @@ notifications:
   "docs/tutorials/simulate-neutron-scattering":
     - "`@nathanearnestnoble`"
     - "@kevinsung"
-  "docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function":
+  "docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow":
     - "@pdd23001"
   "docs/tutorials/readout-error-mitigation-sampler":
     - "`@nathanearnestnoble`"
diff --git a/scripts/config/notebook-testing.toml b/scripts/config/notebook-testing.toml
index 09cd5061827..f6e5b24e06d 100644
--- a/scripts/config/notebook-testing.toml
+++ b/scripts/config/notebook-testing.toml
@@ -226,7 +226,7 @@ notebooks = [
   "docs/tutorials/compilation-methods-for-hamiltonian-simulation-circuits.ipynb",
   "docs/tutorials/solve-market-split-problem-with-iskay-quantum-optimizer.ipynb",
   "docs/tutorials/simulate-neutron-scattering.ipynb",
-  "docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function.ipynb",
+  "docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow.ipynb",
 
   # Don't test any learning notebooks
   "learning/courses/quantum-computing-in-practice/introduction.ipynb",

From 39b93335e05015b26faf01687fe2a50f278dea05 Mon Sep 17 00:00:00 2001
From: Parth Danve <parth.danve@uconn.edu>
Date: Wed, 12 Aug 2026 16:40:19 -0400
Subject: [PATCH 6/7] polish

---
 ...simulate-neutron-scattering-with-a-serverless-workflow.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow.ipynb b/docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow.ipynb
index 8f6591144ce..8f637cef2bf 100644
--- a/docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow.ipynb
+++ b/docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow.ipynb
@@ -894,7 +894,7 @@
     "| 20 | 24m 52s | 15m 58s |\n",
     "| 30 | 230m 57s (about 3h 51m) | 17m 39s |\n",
     "\n",
-    "Queue time is not counted in either column. The classical stage climbs steeply, roughly 6 times from 10 to 20 qubits and another 9 times to 30, dominated by the AQC fidelity optimization at `max_bond = 128`. QPU usage grows only about 1.2 times across the same range, because the circuit count and shot budget follow `t_steps` and the twirling settings rather than the qubit count. At 30 qubits the classical stage costs roughly 13 times the QPU time, which is the practical argument for rehearsing on `statevector` or `fake` before committing to hardware."
+    "Queue time is not counted in either column. The classical stage climbs steeply, roughly 6 times from 10 to 20 qubits and another 9 times to 30, dominated by the AQC fidelity optimization at `max_bond = 128`. QPU usage grows only about 1.2 times across the same range, because the circuit count and shot budget follow `t_steps` and the twirling settings rather than the qubit count."
    ]
   },
   {

From db3d4cda2eb4b0ed7a53fab55cb2aa2565c96de2 Mon Sep 17 00:00:00 2001
From: Parth Danve <parth.danve@uconn.edu>
Date: Thu, 13 Aug 2026 09:36:34 -0400
Subject: [PATCH 7/7] hardware output code

---
 docs/guides/function-template-aqc-trotter.ipynb | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/docs/guides/function-template-aqc-trotter.ipynb b/docs/guides/function-template-aqc-trotter.ipynb
index c1d7ba0d496..61060fca6d7 100644
--- a/docs/guides/function-template-aqc-trotter.ipynb
+++ b/docs/guides/function-template-aqc-trotter.ipynb
@@ -127,7 +127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 76,
    "id": "auth-code",
    "metadata": {},
    "outputs": [],
@@ -539,6 +539,8 @@
     "# Run this only once the status cell above reports DONE. result() blocks until\n",
     "# the job finishes, so calling it earlier just waits.\n",
     "result = job.result()\n",
+    "print(\"observable labels:\", result[\"observable_labels\"])\n",
+    "print(\"times:\", result[\"times\"])\n",
     "print(\n",
     "    \"AQC fidelities:\",\n",
     "    {k: round(v, 4) for k, v in result[\"metadata\"][\"aqc_fidelities\"].items()},\n",