code: def test_independent_predictions(hidden_layer_units):
      x = np.linspace(0.0, 100, 100)
      X = pd.DataFrame({"x": x})
      Y = pd.Series(x**2, name="y")
  
      reg = DAGRegressor(
          threshold=0.0,
          alpha=0.0,
          beta=0.5,
          fit_intercept=True,
          hidden_layer_units=hidden_layer_units,
          standardize=False,
      )
      reg.fit(X, Y)
  
      pred_alone = reg.predict(pd.DataFrame({"x": [10.0]}))
      pred_joint0 = reg.predict(pd.DataFrame({"x": [10.0, 0.0]}))
      pred_joint1 = reg.predict(
          pd.DataFrame({"x": [10.0] + x.tolist()})
      )
  
      assert np.isclose(pred_alone[0], pred_joint0[0])
      assert np.isclose(pred_alone[0], pred_joint1[0])
      assert np.isclose(pred_joint0[0], pred_joint1[0])
  
imports: import os
  import IPython
  import networkx
  import numpy
  import pandas
  import pytest
  import torch
  from sklearn.exceptions import NotFittedError
  from sklearn.gaussian_process.kernels import RBF
  from sklearn.model_selection import KFold, cross_val_score
  from causalnex.structure import data_generators
  from causalnex.structure.pytorch import DAGClassifier, DAGRegressor
label_desc: Use image node_id 1 for calling a global function with example usage: test_independent_predictions(hidden_layer_units) without return types