code: def random_reg_dataset(request):
      """
      Constructs a random regression dataset with 1d target.
      """
      set_seed()
      shape = request.param.get("shape", 10)
      size = request.param.get("size", 100)
      X, Y = make_regression(
          n_samples=2 * size, n_features=shape, n_informative=10
      )
      X_train, X_test, Y_train, Y_test = train_test_split(
          X, Y, test_size=0.5
      )
      Y_train, Y_test = Y_train[:, None].astype(np.float32), Y_test[
          :, None
      ].astype(np.float32)
      return (X_train, Y_train), (X_test, Y_test)
  
imports: import pytest
  import random
  import os
  import numpy
  from tensorflow import keras
  import tensorflow
  import torch
  import torch.nn
  from sklearn.datasets import make_classification, make_regression
  from sklearn.model_selection import train_test_split
  from alibi.explainers.similarity.backends.pytorch.base import _PytorchBackend
  from alibi.explainers.similarity.backends.tensorflow.base import _TensorFlowBackend
label_desc: Use image node_id 4 for calling a global function with example usage: random_reg_dataset(request) and returns: X_train,  Y_train,  X_test,  Y_test