- {
- “cells”: [
- {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“# Chapter 3: Multi-cell, single population network (with BioNet)n”, “n”, “In this tutorial, we will create a more complex network that contains multiple biophysical cells, but all of them having the same cell-type (we will cover hetergenous networks in the next tutorial). The network will contain recurrent connections, as well as external input that provide the next with stimululation.n”, “n”, “Note - scripts and files for running this tutorial can be found in the directory [sources/chapter03/](https://github.com/AllenInstitute/bmtk/tree/develop/docs/tutorial/sources/chapter03)n”, “n”, “requirements:n”, “* bmtkn”, “* NEURON 7.4+”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“## 1. Building the Networkn”, “n”, “First we will build our internal network, which consists of 100 different cells. All the cells are of the same type (we’ll show how to build a heterogeneous network in the next tutorial), however they all have a different location and y-axis rotation.n”, “n”, “#### nodes “
]
}, {
“cell_type”: “code”, “execution_count”: 1, “metadata”: {}, “outputs”: [], “source”: [
“import numpy as npn”, “from bmtk.builder.networks import NetworkBuildern”, “from bmtk.builder.auxi.node_params import positions_columinar, xiter_randomn”, “n”, “cortex = NetworkBuilder(‘mcortex’)n”, “cortex.add_nodes(n”, ” N=100,n”, ” pop_name=’Scnn1a’,n”, ” positions=positions_columinar(N=100, center=[0, 50.0, 0], max_radius=30.0, height=100.0),n”, ” rotation_angle_yaxis=xiter_random(N=100, min_x=0.0, max_x=2*np.pi),n”, ” rotation_angle_zaxis=3.646878266,n”, ” potental=’exc’,n”, ” model_type=’biophysical’,n”, ” model_template=’ctdb:Biophys1.hoc’,n”, ” model_processing=’aibs_perisomatic’,n”, ” dynamics_params=’472363762_fit.json’,n”, ” morphology=’Scnn1a_473845048_m.swc’n”, “)n”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“The parameter N is used to indicate the number of cells in our population. The positions of each cell is defined by the columinar built-in method, which will random place our cells in a column (users can define their own positions as shown here). The rotation_angel_yaxis is similarl defined by a built-in function that will randomly assign each cell a given y angle.n”, “n”, “One thing to note is that while yaxis is defined by a function which returns a lists of values, the zaxis is defined by a single value. This means that all cells will share the zaxis. we could alteratively give all cells the same y-axis rotation:n”, “
`python\n", " rotation_angle_yaxis=rotation_value\n", "`
n”, “or give all cells a unique z-rotation anglen”, “`python\n", " rotation_angle_zaxis=xiter_random(N=100, min_x=0.0, max_x=2*np.pi)\n", "`
n”, “and in general, it is at the discretion of the modeler to choose what parameters are unqiue to each cell, and what parameters are global to the cell-type.”]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“#### edgesn”, “n”, “Next we want to add recurrent edges. To create the connections we will use the built-in distance_connector function, which will assign the number of connections between two cells randomly (between range nsyn_min and nsysn_max) but weighted by distance. The other parameters, including the synaptic model (AMPA_ExcToExc) will be shared by all connections.n”, “n”, “To use this, or even customized, connection functions, we must pass in the name of our connection function using the "connection_rule" parameter, and the function parameters through "connection_params" as a dictionary, which will looks something like:n”, “
`python\n", " connection_rule=<name_of_function>\n", " connection_params={'param_arg1': val1, 'param_arg2': val2, ...}\n", "`
n”, “The connection_rule method isn’t explicitly called by the script. Rather when the build() method is called, the connection_rule will iterate through every source/target node pair, and use the rule and build a connection matrix.n”, “n”, “n”, “After building the connections based on our connection function, we will save the nodes and edges files into the network/ directory.”]
}, {
“cell_type”: “code”, “execution_count”: 2, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“<bmtk.builder.connection_map.ConnectionMap at 0x7f461499a780>”
]
}, “execution_count”: 2, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“from bmtk.builder.auxi.edge_connectors import distance_connectorn”, “n”, “cortex.add_edges(n”, ” source={‘pop_name’: ‘Scnn1a’}, target={‘pop_name’: ‘Scnn1a’},n”, ” connection_rule=distance_connector,n”, ” connection_params={‘d_weight_min’: 0.0, ‘d_weight_max’: 0.34, ‘d_max’: 50.0, ‘nsyn_min’: 0, ‘nsyn_max’: 10},n”, ” syn_weight=2.0e-04,n”, ” distance_range=[30.0, 150.0],n”, ” target_sections=[‘basal’, ‘apical’, ‘soma’],n”, ” delay=2.0,n”, ” dynamics_params=’AMPA_ExcToExc.json’,n”, ” model_template=’exp2syn’n”, “)n”, “n”
]
}, {
“cell_type”: “code”, “execution_count”: 3, “metadata”: {}, “outputs”: [], “source”: [
“cortex.build()n”, “cortex.save_nodes(output_dir=’sim_ch03/network’)n”, “cortex.save_edges(output_dir=’sim_ch03/network’)”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“### External networkn”, “n”, “After building our internal network, we will build the external thalamic network which will provide input (see previous tutorial for more detail). Our thalamic network will consist of 100 "filter" cells, which aren’t actual cells by just place holders for spike-trains.”
]
}, {
“cell_type”: “code”, “execution_count”: 4, “metadata”: {}, “outputs”: [], “source”: [
“thalamus = NetworkBuilder(‘mthalamus’)n”, “thalamus.add_nodes(n”, ” N=100,n”, ” pop_name=’tON’,n”, ” potential=’exc’,n”, ” model_type=’virtual’n”, “)”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“The external network doesn’t have recurrent connections. Rather all the cells are feedforward onto the internal network. To do this is in a separate script which must reload the saved mcortex cell files using the import function. Then we create an edge with the thalamus nodes as the sources and the cortext nodes as the targets. This time we use the built-in connect_random connection rule, which will randomly assign each thalamus –> cortex connection between 0 and 12 synaptic connections.”
]
}, {
“cell_type”: “code”, “execution_count”: 5, “metadata”: {}, “outputs”: [], “source”: [
“from bmtk.builder.auxi.edge_connectors import connect_randomn”, “n”, “thalamus.add_edges(n”, ” source=thalamus.nodes(), target=cortex.nodes(),n”, ” connection_rule=connect_random,n”, ” connection_params={‘nsyn_min’: 0, ‘nsyn_max’: 12},n”, ” syn_weight=1.0e-04,n”, ” distance_range=[0.0, 150.0],n”, ” target_sections=[‘basal’, ‘apical’],n”, ” delay=2.0,n”, ” dynamics_params=’AMPA_ExcToExc.json’,n”, ” model_template=’exp2syn’n”, “)n”, “n”, “thalamus.build()n”, “thalamus.save_nodes(output_dir=’sim_ch03/network’)n”, “thalamus.save_edges(output_dir=’sim_ch03/network’)”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“#### Spike Trainsn”, “n”, “We next need to create the individual spike trains for our thalamic filter cells. We will use a Poission distrubition to create a random distribution of spikes for our 300 hundred cells each firing at ~ 15 Hz over a 3 second window. Then we can save our spike trains as a [SONATA file](https://github.com/AllenInstitute/sonata/blob/master/docs/SONATA_DEVELOPER_GUIDE.md#spike-file) under sim_ch03/inputs directory. “
]
}, {
“cell_type”: “code”, “execution_count”: 6, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/html”: [
“<div>n”, “<style scoped>n”, ” .dataframe tbody tr th:only-of-type {n”, ” vertical-align: middle;n”, ” }n”, “n”, ” .dataframe tbody tr th {n”, ” vertical-align: top;n”, ” }n”, “n”, ” .dataframe thead th {n”, ” text-align: right;n”, ” }n”, “</style>n”, “<table border="1" class="dataframe">n”, ” <thead>n”, ” <tr style="text-align: right;">n”, ” <th></th>n”, ” <th>node_ids</th>n”, ” <th>timestamps</th>n”, ” <th>population</th>n”, ” </tr>n”, ” </thead>n”, ” <tbody>n”, ” <tr>n”, ” <th>0</th>n”, ” <td>0</td>n”, ” <td>120.051599</td>n”, ” <td>mthalamus</td>n”, ” </tr>n”, ” <tr>n”, ” <th>1</th>n”, ” <td>0</td>n”, ” <td>317.919543</td>n”, ” <td>mthalamus</td>n”, ” </tr>n”, ” <tr>n”, ” <th>2</th>n”, ” <td>0</td>n”, ” <td>404.393517</td>n”, ” <td>mthalamus</td>n”, ” </tr>n”, ” <tr>n”, ” <th>3</th>n”, ” <td>0</td>n”, ” <td>514.393950</td>n”, ” <td>mthalamus</td>n”, ” </tr>n”, ” <tr>n”, ” <th>4</th>n”, ” <td>0</td>n”, ” <td>538.898219</td>n”, ” <td>mthalamus</td>n”, ” </tr>n”, ” </tbody>n”, “</table>n”, “</div>”
], “text/plain”: [
” node_ids timestamps populationn”, “0 0 120.051599 mthalamusn”, “1 0 317.919543 mthalamusn”, “2 0 404.393517 mthalamusn”, “3 0 514.393950 mthalamusn”, “4 0 538.898219 mthalamus”
]
}, “execution_count”: 6, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“from bmtk.utils.reports.spike_trains import PoissonSpikeGeneratorn”, “n”, “psg = PoissonSpikeGenerator(population=’mthalamus’)n”, “psg.add(node_ids=range(100), # Have 10 nodes to match mthalamusn”, ” firing_rate=15.0, # 15 Hz, we can also pass in a nonhomoegenous function/arrayn”, ” times=(0.0, 3.0)) # Firing starts at 0 s up to 3 sn”, “psg.to_sonata(‘sim_ch03/inputs/mthalamus_spikes.h5’)n”, “n”, “# Let’s do a quick check that we have reasonable results. Should see somewhere on the order of 15*3*100 = 4500n”, “# spikesn”, “psg.to_dataframe().head()n”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“## 2. Setting up BioNetn”, “n”, “#### file structure.n”, “n”, “Before running a simulation, we will need to create the runtime environment, including parameter files, run-script and configuration files. You’ve already completed Chapter 02 tutorial you can just copy the files to sim_ch03 (just make sure not to overwrite the network and inputs directory).n”, “n”, “Or create them from scracth by either running the command:n”, “
`bash\n", "$ python -m bmtk.utils.sim_setup \\\n", " --report-vars v,cai \\ \n", " --network sim_ch03/network \\ \n", " --spikes-inputs mthalamus:sim_ch03/inputs/mthalamus_spikes.h5 \\\n", " --dt 0.1 \\\n", " --tstop 3000.0 \\ \n", " --include-examples \\\n", " --compile-mechanisms \\ \n", " bionet sim_ch03\n", "`
n”, “n”]
}, {
“cell_type”: “code”, “execution_count”: 7, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“config.jsonn”
]
}
], “source”: [
“from bmtk.utils.sim_setup import build_env_bionetn”, “n”, “build_env_bionet(n”, ” base_dir=’sim_ch03’,n”, ” config_file=’config.json’,n”, ” network_dir=’sim_ch03/network’,n”, ” tstop=3000.0, dt=0.1,n”, ” report_vars=[‘v’, ‘cai’], # Record membrane potential and calcium (default soma)n”, ” spikes_inputs=[(‘mthalamus’, # Name of population which spikes will be generated forn”, ” ‘sim_ch03/inputs/mthalamus_spikes.h5’)],n”, ” include_examples=True, # Copies components filesn”, ” compile_mechanisms=True # Will try to compile NEURON mechanismsn”, “)”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“It’s a good idea to check the configuration files sim_ch03/circuit_config.json and sim_ch03/simulation_config.json, especially to make sure that bmtk will know to use our generated spikes file (if you don’t see the below section in the simulation_config.json file go ahead and add it). n”, “n”, “
`json\n", "{\n", "\n", " \n", " \"inputs\": {\n", " \"tc_spikes\": {\n", " \"input_type\": \"spikes\",\n", " \"module\": \"csv\",\n", " \"input_file\": \"${BASE_DIR}/mthalamus_spikes.csv\",\n", " \"node_set\": \"mthalamus\"\n", " }\n", " }\n", "}\n", "`
”]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“## 3. Running the simulationn”, “n”, “Once our config file is setup we can run a simulation either through the command line:n”, “
`bash\n", "$ cd sim_ch03\n", "$ python run_bionet.py config.json\n", "`
n”, “n”, “or through the script”]
}, {
“cell_type”: “code”, “execution_count”: 8, “metadata”: {
“scrolled”: true
}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“2022-02-18 07:32:37,997 [INFO] Created log filen”
]
}, {
}, {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“2022-02-18 07:32:38,089 [INFO] Building cells.n”
]
}, {
}, {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“2022-02-18 07:32:46,048 [INFO] Building recurrent connectionsn”
]
}, {
“name”: “stderr”, “output_type”: “stream”, “text”: [
“INFO:NEURONIOUtils:Building recurrent connectionsn”
]
}, {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“2022-02-18 07:32:47,199 [INFO] Building virtual cell stimulations for mthalamus_spikesn”
]
}, {
“name”: “stderr”, “output_type”: “stream”, “text”: [
“INFO:NEURONIOUtils:Building virtual cell stimulations for mthalamus_spikesn”
]
}, {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“2022-02-18 07:32:56,580 [INFO] Running simulation for 3000.000 ms with the time step 0.100 msn”
]
}, {
“name”: “stderr”, “output_type”: “stream”, “text”: [
“INFO:NEURONIOUtils:Running simulation for 3000.000 ms with the time step 0.100 msn”
]
}, {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“2022-02-18 07:32:56,582 [INFO] Starting timestep: 0 at t_sim: 0.000 msn”
]
}, {
“name”: “stderr”, “output_type”: “stream”, “text”: [
“INFO:NEURONIOUtils:Starting timestep: 0 at t_sim: 0.000 msn”
]
}, {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“2022-02-18 07:32:56,582 [INFO] Block save every 5000 stepsn”
]
}, {
“name”: “stderr”, “output_type”: “stream”, “text”: [
“INFO:NEURONIOUtils:Block save every 5000 stepsn”
]
}, {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“2022-02-18 07:33:34,556 [INFO] step:5000 t_sim:500.00 msn”
]
}, {
“name”: “stderr”, “output_type”: “stream”, “text”: [
“INFO:NEURONIOUtils: step:5000 t_sim:500.00 msn”
]
}, {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“2022-02-18 07:34:10,838 [INFO] step:10000 t_sim:1000.00 msn”
]
}, {
“name”: “stderr”, “output_type”: “stream”, “text”: [
“INFO:NEURONIOUtils: step:10000 t_sim:1000.00 msn”
]
}, {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“2022-02-18 07:34:48,306 [INFO] step:15000 t_sim:1500.00 msn”
]
}, {
“name”: “stderr”, “output_type”: “stream”, “text”: [
“INFO:NEURONIOUtils: step:15000 t_sim:1500.00 msn”
]
}, {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“2022-02-18 07:35:29,660 [INFO] step:20000 t_sim:2000.00 msn”
]
}, {
“name”: “stderr”, “output_type”: “stream”, “text”: [
“INFO:NEURONIOUtils: step:20000 t_sim:2000.00 msn”
]
}, {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“2022-02-18 07:36:09,063 [INFO] step:25000 t_sim:2500.00 msn”
]
}, {
“name”: “stderr”, “output_type”: “stream”, “text”: [
“INFO:NEURONIOUtils: step:25000 t_sim:2500.00 msn”
]
}, {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“2022-02-18 07:36:47,843 [INFO] step:30000 t_sim:3000.00 msn”
]
}, {
“name”: “stderr”, “output_type”: “stream”, “text”: [
“INFO:NEURONIOUtils: step:30000 t_sim:3000.00 msn”
]
}, {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“2022-02-18 07:36:47,903 [INFO] Simulation completed in 3.0 minutes, 51.32 seconds n”
]
}, {
“name”: “stderr”, “output_type”: “stream”, “text”: [
“INFO:NEURONIOUtils:Simulation completed in 3.0 minutes, 51.32 seconds n”
]
}
], “source”: [
“from bmtk.simulator import bionetn”, “n”, “n”, “conf = bionet.Config.from_json(‘sim_ch03/config.json’)n”, “conf.build_env()n”, “net = bionet.BioNetwork.from_config(conf)n”, “sim = bionet.BioSimulator.from_config(conf, network=net)n”, “sim.run()”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“## 4. Analyzing the run.n”, “n”, “If successful, we should have our results in the output directory. We can use the analyzer to plot a raster of the spikes over time:”
]
}, {
“cell_type”: “code”, “execution_count”: 9, “metadata”: {
“scrolled”: true
}, “outputs”: [
- {
- “data”: {
“image/png”: 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n”, “text/plain”: [
“<Figure size 432x288 with 2 Axes>”
]
}, “metadata”: {
“needs_background”: “light”
}, “output_type”: “display_data”
}
], “source”: [
“from bmtk.analyzer.spike_trains import plot_rastern”, “n”, “n”, “_ = plot_raster(config_file=’sim_ch03/config.json’)”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“In our config file we used the cell_vars and node_id_selections parameters to save the calcium influx and membrane potential of selected cells. We can also use the analyzer to display these traces:”
]
}, {
“cell_type”: “code”, “execution_count”: 10, “metadata”: {}, “outputs”: [
- {
- “data”: {
“image/png”: 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n”, “text/plain”: [
“<Figure size 432x288 with 1 Axes>”
]
}, “metadata”: {
“needs_background”: “light”
}, “output_type”: “display_data”
}, {
- “data”: {
“image/png”: 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n”, “text/plain”: [
“<Figure size 432x288 with 1 Axes>”
]
}, “metadata”: {
“needs_background”: “light”
}, “output_type”: “display_data”
}
], “source”: [
“from bmtk.analyzer.compartment import plot_tracesn”, “n”, “_ = plot_traces(config_file=’sim_ch03/config.json’, report_name=’v_report’)n”, “_ = plot_traces(config_file=’sim_ch03/config.json’, report_name=’cai_report’)”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“## 5. Modifying the network”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“#### Customized node paramsn”, “n”, “When building our cortex nodes, we used some built-in functions to set certain parameters like positions and y-axis rotations:n”, “
`python\n", "cortex.add_nodes(N=100,\n", " pop_name='Scnn1a',\n", " positions=positions_columinar(N=100, center=[0, 50.0, 0], max_radius=30.0, height=100.0),\n", " rotation_angle_yaxis=xiter_random(N=100, min_x=0.0, max_x=2*np.pi),\n", " ...\n", "`
n”, “n”, “These functions will assign every cell a unique value in the positions and rotation_angle_yaxis parameters, unlike the pop_name parameter which will be the same for all 100 cells. We can verify by the following code:n”]
}, {
“cell_type”: “code”, “execution_count”: 11, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“cell 0: pop_name: Scnn1a, positions: [-7.76668485 0.38215656 -7.4268604 ], angle_yaxis: 0.43207320140931793n”, “cell 1: pop_name: Scnn1a, positions: [-0.45636819 84.90726294 -4.33892126], angle_yaxis: 5.501379786273044n”
]
}
], “source”: [
“cortex_nodes = list(cortex.nodes())n”, “n0 = cortex_nodes[0]n”, “n1 = cortex_nodes[1]n”, “print(‘cell 0: pop_name: {}, positions: {}, angle_yaxis: {}’.format(n0[‘pop_name’], n0[‘positions’], n0[‘rotation_angle_yaxis’]))n”, “print(‘cell 1: pop_name: {}, positions: {}, angle_yaxis: {}’.format(n1[‘pop_name’], n1[‘positions’], n1[‘rotation_angle_yaxis’]))n”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“The Network Builder contains a growing number of built-in functions. However for advanced networks a modeler will probably want to assign parameters using their own functions. To do so, a modeler only needs to passes in, or alternatively create a function that returns, a list of size N. When saving the network, each individual position will be saved in the nodes.h5 file assigned to each cell by gid.n”, “n”, “
`python\n", "def cortex_positions(N):\n", " # codex to create a list/numpy array of N (x, y, z) positions.\n", " return [...]\n", "\n", "cortex.add_nodes(N=100,\n", " positions=cortex_positions(100),\n", " ...\n", "`
n”, “n”, “or if we wanted we could give all cells the same position (The builder has no restrictions on this, however this may cause issues if you’re trying to create connections based on distance). When saving the network, the same position is assigned as a global cell-type property, and thus saved in the node_types.csv file.n”, “`python\n", "cortex.add_nodes(N=100,\n", " positions=np.ndarray([100.23, -50.67, 89.01]),\n", " ...\n", "`
n”, “n”, “We can use the same logic not just for positions and rotation_angle, but for any parameter we choose.”]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“#### Customized connector functionsn”, “n”, “When creating edges, we used the built-in distance_connector function to help create the connection matrix. There are a number of built-in connection functions, but we also allow modelers to create their own. To do so, the modeler must create a function that takes in a source, target, and a variable number of parameters, and pass back a natural number representing the number of connections.n”, “n”, “The Builder will iterate over that function passing in every source/target node pair (filtered by the source and target parameters in add_edges()). The source and target parameters are essentially dictionaries that can be used to fetch properties of the nodes. A typical example would look like:n”, “n”, “
`python\n", "def customized_connector(source, target, param1, param2, param3):\n", " if source.node_id == target.node_id:\n", " # necessary if we don't want autapses\n", " return 0\n", " source_pot = source['potential']\n", " target_pot = target['potential']\n", " # some code to determine number of connections\n", " return n_synapses\n", " \n", "...\n", "cortex.add_edges(source=<source_nodes>, target=<target_nodes>,\n", " connection_rule=customized_connector,\n", " connection_params={'param1': <p1>, 'param2': <p2>, 'param3': <p3>},\n", " ...\n", "`
”]
}, {
“cell_type”: “code”, “execution_count”: null, “metadata”: {}, “outputs”: [], “source”: []
}
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