code: def skorch_regressor():
      X, y = make_regression(100, 5, n_informative=3, random_state=0)
      X = X.astype(np.float32)
      y = y / np.std(y)
      y = y.reshape(-1, 1).astype(np.float32)
  
      X_df = pd.DataFrame(
          X, columns=["col" + str(i) for i in range(X.shape[1])]
      )
  
      class MyModule(nn.Module):
          def __init__(
              skorch_classifier_explainer,
              input_units=5,
              num_units=5,
              nonlin=nn.ReLU(),
          ):
              super(MyModule, skorch_classifier_explainer).__init__()
  
              skorch_classifier_explainer.dense0 = nn.Linear(
                  input_units, num_units
              )
              skorch_classifier_explainer.nonlin = nonlin
              skorch_classifier_explainer.dense1 = nn.Linear(
                  num_units, num_units
              )
              skorch_classifier_explainer.output = nn.Linear(
                  num_units, 1
              )
  
          def forward(skorch_classifier_explainer, X, **kwargs):
              X = skorch_classifier_explainer.nonlin(
                  skorch_classifier_explainer.dense0(X)
              )
              X = skorch_classifier_explainer.nonlin(
                  skorch_classifier_explainer.dense1(X)
              )
              X = skorch_classifier_explainer.output(X)
              return X
  
      model = NeuralNetRegressor(
          MyModule,
          max_epochs=20,
          lr=0.2,
          iterator_train__shuffle=True,
      )
  
      model.fit(X_df.values, y)
      return model, X_df, y
  
imports: import pytest
  import numpy
  import pandas
  from sklearn.datasets import make_classification, make_regression
  from torch import nn
  from skorch import NeuralNetClassifier, NeuralNetRegressor
  from explainerdashboard.explainers import RegressionExplainer, ClassifierExplainer
label_desc: Use image node_id 1 for calling a global function with example usage: skorch_regressor() and returns: model,  X_df,  y, X