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joshterrillParis778
joshterrill
and
Paris778
authored
fixed spelling (#186)
Co-authored-by: Paris778 <parissol7@gmail.com>
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‎crypto/example_model.ipynb‎

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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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],
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"source": [
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"# Install dependencies\n",
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"!pip install -q --upgrade numerapi pandas pyarrow matplotlib lightgbm scikit-learn scipy cloudpickle==3.1.1\n",
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"!pip install -q --upgrade numerapi pandas pyarrow matplotlib lightgbm scikit-learn scipy cloudpickle==2.2.1\n",
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"!pip install -q --no-deps numerai-tools"
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]
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},
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "3.10.18",
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"language": "python",
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"name": "python3"
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},
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.11"
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"version": "3.10.18"
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"orig_nbformat": 4
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‎numerai/example_model.ipynb‎

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},
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-12-14T21:23:24.577253Z",
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"start_time": "2025-12-14T21:23:24.420881Z"
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},
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"execution": {
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"iopub.execute_input": "2026-03-04T09:50:06.921017Z",
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"id": "Ekw8Z93ljC3v",
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}
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"outputId": "bdd16698-2ad0-4423-b090-c5ce55fe3053"
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},
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"source": [
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"!python --version"
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],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Python 3.11.11\r\n"
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"Python 3.12.11\r\n"
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]
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}
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],
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"execution_count": 1
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"source": [
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"!python --version"
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]
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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},
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"execution": {
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"iopub.execute_input": "2026-03-04T09:50:07.112715Z",
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"iopub.status.busy": "2026-03-04T09:50:07.112578Z",
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"iopub.status.idle": "2026-03-04T09:50:08.208975Z",
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"shell.execute_reply": "2026-03-04T09:50:08.208610Z"
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},
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"id": "yoy_wT1rhMqF",
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}
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"outputId": "e038b50f-1b61-4334-be62-28f4dc40a0a0"
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},
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"source": [
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"# Install dependencies\n",
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"!pip install -q --upgrade numerapi pandas pyarrow matplotlib lightgbm scikit-learn scipy cloudpickle==3.1.1"
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],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\r\n",
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"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m25.2\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m25.3\u001B[0m\r\n",
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"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\r\n"
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m26.0.1\u001b[0m\r\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\r\n"
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]
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}
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],
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"execution_count": 2
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"source": [
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"# Install dependencies\n",
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"!pip install -q --upgrade numerapi pandas pyarrow matplotlib lightgbm scikit-learn scipy cloudpickle==2.2.1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-12-14T21:28:28.979818Z",
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"start_time": "2025-12-14T21:23:25.629875Z"
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},
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 160
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"execution": {
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"iopub.execute_input": "2026-03-04T09:50:08.210682Z",
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"iopub.status.busy": "2026-03-04T09:50:08.210563Z",
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"shell.execute_reply": "2026-03-04T09:50:28.438971Z"
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"outputId": "d2274374-fd85-4189-f27b-d9d466cc63ca"
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"outputs": [
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"output_type": "stream",
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"text": [
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"2026-03-04 01:50:09,132 INFO numerapi.utils: target file already exists\n"
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]
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},
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"name": "stderr",
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"text": [
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"2026-03-04 01:50:09,133 INFO numerapi.utils: download complete\n"
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]
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},
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"2026-03-04 01:50:09,575 INFO numerapi.utils: target file already exists\n"
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]
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},
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"2026-03-04 01:50:09,577 INFO numerapi.utils: download complete\n"
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]
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},
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"text": [
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"[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001699 seconds.\n",
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"You can set `force_row_wise=true` to remove the overhead.\n",
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"And if memory is not enough, you can set `force_col_wise=true`.\n",
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"[LightGBM] [Info] Total Bins 210\n",
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"[LightGBM] [Info] Number of data points in the train set: 688182, number of used features: 42\n",
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"[LightGBM] [Info] Start training from score 0.499948\n"
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]
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}
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],
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"source": [
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"from numerapi import NumerAPI\n",
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"import pandas as pd\n",
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" files.download('example_model.pkl')\n",
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"except:\n",
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" pass"
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"outputs": [
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"text": [
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"2025-12-14 13:23:26,450 INFO numerapi.utils: target file already exists\n",
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"2025-12-14 13:23:26,452 INFO numerapi.utils: starting download\n",
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"v5.2/train.parquet: 7.67GB [04:40, 27.4MB/s] \n",
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"2025-12-14 13:28:07,043 INFO numerapi.utils: target file already exists\n",
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"2025-12-14 13:28:07,045 INFO numerapi.utils: download complete\n"
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]
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},
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001209 seconds.\n",
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"You can set `force_row_wise=true` to remove the overhead.\n",
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"And if memory is not enough, you can set `force_col_wise=true`.\n",
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"[LightGBM] [Info] Total Bins 210\n",
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"[LightGBM] [Info] Number of data points in the train set: 688184, number of used features: 42\n",
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"[LightGBM] [Info] Start training from score 0.499946\n"
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]
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}
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],
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"execution_count": 3
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]
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"execution_count": 3,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-12-14T21:28:28.982147Z",
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"start_time": "2025-12-14T21:28:28.980721Z"
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"source": "",
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"outputs": [],
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"execution_count": 3
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"source": []
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"metadata": {
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"colab": {
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"provenance": []
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"kernelspec": {
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"display_name": "3.12.8",
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"language": "python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.12"
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},
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"orig_nbformat": 4
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"version": "3.13.5"
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}
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"nbformat": 4,
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"nbformat_minor": 0

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