code: def data_dynotears_p1() -> Dict[str, np.ndarray]:
      """
      Training data for testing Dynamic Bayesian Networks. Return a time series with 50 time points, with 5 columns
      This data was simulated with te following configurations
      Configurations:
          - data points 50,
          - num. variables: 5,
          - p (lag amount): 1,
          - graph type (intra-slice graph): 'erdos-renyi',
          - graph type (inter-slice graph): 'erdos-renyi',
          - SEM type: 'linear-gauss',
          - weight range, intra-slice graph: (0.5, 2.0),
          - weight range, inter-slice graph: (0.3, 0.5),
          - expected degree, inter-slice graph: 3,
          - noise scale (gaussian noise): 1.0,
          - w decay: 1.1
      Returns:
          dictionary with keys W (intra-weights), A (inter-weights), X and Y (inputs of from_numpy_dynamic)
      """
      data = {
          "W": np.array(
              [
                  [0.0, -0.55, 0.0, 1.48, 0.0],
                  [0.0, 0.0, -0.99, 0.0, 0.0],
                  [0.0, 0.0, 0.0, -1.13, 0.0],
                  [0.0, 0.0, 0.0, 0.0, 0.0],
                  [0.0, -1.91, -0.64, -1.31, 0.0],
              ]
          ),
          "A": np.array(
              [
                  [-0.35, -0.32, -0.33, 0.37, 0.0],
                  [-0.41, -0.42, -0.36, 0.33, -0.35],
                  [0.46, 0.0, 0.44, -0.36, -0.38],
                  [0.0, 0.0, -0.45, 0.0, 0.43],
                  [0.31, 0.4, 0.0, 0.0, -0.44],
              ]
          ),
          "X": np.array(
              [
                  [-8.7, -3.1, -5.1, 2.8, -2.0],
                  [0.3, -5.8, 2.9, -12.6, 5.1],
                  [4.8, 17.4, -4.2, 17.6, -6.8],
                  [-12.5, -20.6, -1.9, -18.5, 8.3],
                  [14.4, 14.1, 7.7, 6.8, -3.0],
                  [-8.1, 0.1, -8.6, 9.0, -3.3],
                  [-3.3, -11.4, 1.3, -18.4, 8.0],
                  [9.4, 14.4, 3.7, 11.4, -6.5],
                  [-9.7, -7.0, -3.5, -3.2, -0.0],
                  [1.7, -2.7, 5.8, -14.8, 4.5],
                  [3.9, 17.2, -2.4, 19.2, -9.8],
                  [-12.5, -22.2, 1.2, -23.2, 7.8],
                  [17.2, 18.2, 9.2, 11.6, -6.4],
                  [-12.2, -7.9, -2.8, -2.6, -1.2],
                  [5.8, -4.9, 9.0, -12.6, 3.9],
                  [6.0, 13.5, 2.3, 15.1, -8.4],
                  [-10.1, -12.8, -1.4, -13.4, 4.5],
                  [9.7, 10.3, 5.5, 7.1, -3.5],
                  [-7.2, -1.4, -4.5, 0.2, -1.3],
                  [1.8, -3.7, 1.6, -4.8, 3.2],
                  [2.6, 6.7, -2.1, 9.7, -2.7],
                  [-4.6, -7.6, -1.7, -4.4, 2.6],
                  [4.6, -0.1, 4.5, -4.1, 1.9],
                  [3.3, 5.1, -0.7, 11.0, -4.1],
                  [-4.3, -12.7, -0.7, -9.5, 5.9],
                  [8.0, 6.0, 5.6, 0.8, -1.1],
                  [-2.4, 4.9, -3.9, 11.3, -4.7],
                  [-4.6, -12.0, -0.3, -13.4, 6.5],
                  [8.1, 15.9, -0.8, 16.0, -6.5],
                  [-11.5, -14.8, -5.0, -7.5, 4.4],
                  [10.0, 3.3, 7.1, -2.8, 1.7],
                  [-0.7, 3.8, -1.8, 9.2, -4.0],
                  [-1.6, -13.7, 5.3, -13.0, 4.7],
                  [11.0, 8.2, 7.7, 6.7, -4.0],
                  [-4.0, -2.4, -3.0, 1.8, -1.7],
                  [1.1, -4.8, 2.9, -5.9, 3.0],
                  [3.4, 7.5, -1.5, 9.0, -2.4],
                  [-6.2, -3.4, -4.8, -2.2, 1.0],
                  [0.8, -0.3, 1.8, -2.5, 1.0],
                  [0.4, 1.7, -1.6, 1.9, -0.5],
                  [-2.3, -4.5, 2.9, -7.4, 2.3],
                  [3.9, 9.3, 2.4, 4.1, -3.8],
                  [-4.8, -0.6, -3.6, 3.0, -2.1],
                  [-1.8, -7.5, 2.9, -13.2, 4.7],
                  [5.6, 19.0, -3.7, 18.1, -7.8],
                  [-13.5, -19.3, -2.7, -17.7, 7.5],
                  [15.5, 10.6, 9.9, 5.7, -3.1],
                  [-6.8, 3.3, -6.8, 9.6, -4.8],
                  [-5.2, -15.3, 3.9, -21.6, 7.9],
                  [11.1, 22.0, -0.3, 20.7, -8.4],
              ]
          ),
          "Y": np.array(
              [
                  [10.8, 14.6, 5.8, 8.1, -4.5],
                  [-8.7, -3.1, -5.1, 2.8, -2.0],
                  [0.3, -5.8, 2.9, -12.6, 5.1],
                  [4.8, 17.4, -4.2, 17.6, -6.8],
                  [-12.5, -20.6, -1.9, -18.5, 8.3],
                  [14.4, 14.1, 7.7, 6.8, -3.0],
                  [-8.1, 0.1, -8.6, 9.0, -3.3],
                  [-3.3, -11.4, 1.3, -18.4, 8.0],
                  [9.4, 14.4, 3.7, 11.4, -6.5],
                  [-9.7, -7.0, -3.5, -3.2, -0.0],
                  [1.7, -2.7, 5.8, -14.8, 4.5],
                  [3.9, 17.2, -2.4, 19.2, -9.8],
                  [-12.5, -22.2, 1.2, -23.2, 7.8],
                  [17.2, 18.2, 9.2, 11.6, -6.4],
                  [-12.2, -7.9, -2.8, -2.6, -1.2],
                  [5.8, -4.9, 9.0, -12.6, 3.9],
                  [6.0, 13.5, 2.3, 15.1, -8.4],
                  [-10.1, -12.8, -1.4, -13.4, 4.5],
                  [9.7, 10.3, 5.5, 7.1, -3.5],
                  [-7.2, -1.4, -4.5, 0.2, -1.3],
                  [1.8, -3.7, 1.6, -4.8, 3.2],
                  [2.6, 6.7, -2.1, 9.7, -2.7],
                  [-4.6, -7.6, -1.7, -4.4, 2.6],
                  [4.6, -0.1, 4.5, -4.1, 1.9],
                  [3.3, 5.1, -0.7, 11.0, -4.1],
                  [-4.3, -12.7, -0.7, -9.5, 5.9],
                  [8.0, 6.0, 5.6, 0.8, -1.1],
                  [-2.4, 4.9, -3.9, 11.3, -4.7],
                  [-4.6, -12.0, -0.3, -13.4, 6.5],
                  [8.1, 15.9, -0.8, 16.0, -6.5],
                  [-11.5, -14.8, -5.0, -7.5, 4.4],
                  [10.0, 3.3, 7.1, -2.8, 1.7],
                  [-0.7, 3.8, -1.8, 9.2, -4.0],
                  [-1.6, -13.7, 5.3, -13.0, 4.7],
                  [11.0, 8.2, 7.7, 6.7, -4.0],
                  [-4.0, -2.4, -3.0, 1.8, -1.7],
                  [1.1, -4.8, 2.9, -5.9, 3.0],
                  [3.4, 7.5, -1.5, 9.0, -2.4],
                  [-6.2, -3.4, -4.8, -2.2, 1.0],
                  [0.8, -0.3, 1.8, -2.5, 1.0],
                  [0.4, 1.7, -1.6, 1.9, -0.5],
                  [-2.3, -4.5, 2.9, -7.4, 2.3],
                  [3.9, 9.3, 2.4, 4.1, -3.8],
                  [-4.8, -0.6, -3.6, 3.0, -2.1],
                  [-1.8, -7.5, 2.9, -13.2, 4.7],
                  [5.6, 19.0, -3.7, 18.1, -7.8],
                  [-13.5, -19.3, -2.7, -17.7, 7.5],
                  [15.5, 10.6, 9.9, 5.7, -3.1],
                  [-6.8, 3.3, -6.8, 9.6, -4.8],
                  [-5.2, -15.3, 3.9, -21.6, 7.9],
              ]
          ),
      }
      return data
  
imports: import itertools
  from typing import Dict
  import numpy
  import pandas
  import pytest
  from pgmpy.models import BayesianNetwork
  from sklearn.datasets import load_iris
  from causalnex.discretiser import Discretiser
  from causalnex.network import BayesianNetwork
  from causalnex.structure import StructureModel
  from causalnex.structure.notears import from_pandas
label_desc: Use image node_id 21 for calling a global function with example usage: data_dynotears_p1() and returns: data