code: def test_X_dtype_prediction(standardize):
      """
      tests whether providing an int or float X returns the same prediction
      """
      training_data = pd.DataFrame(
          {
              "x": np.linspace(0, 500, num=500),
              "y": np.linspace(0, 500, num=500),
          }
      )
  
      reg = DAGRegressor(
          threshold=0.0,
          alpha=0.0001,
          beta=0.5,
          fit_intercept=True,
          hidden_layer_units=[10],
          standardize=standardize,
      )
  
      X = training_data.loc[:, ["x"]]
      y = training_data["y"]
  
      reg.fit(X, y)
  
      test_data_int = pd.DataFrame({"x": [0, 250, 500]})
  
      test_data_float = pd.DataFrame({"x": [0.0, 250.0, 500.0]})
  
      pred_int = reg.predict(test_data_int)
      pred_float = reg.predict(test_data_float)
  
      assert np.all(pred_float == pred_int)
  
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 2 for calling a global function with example usage: test_X_dtype_prediction(standardize) without return types