code: def test_mlp_regressor_dtypes_casting():
      mlp_64 = MLPRegressor(
          alpha=1e-5,
          hidden_layer_sizes=(5, 3),
          random_state=1,
          max_iter=50,
      )
      mlp_64.fit(X_digits[:300], y_digits[:300])
      pred_64 = mlp_64.predict(X_digits[300:])
  
      mlp_32 = MLPRegressor(
          alpha=1e-5,
          hidden_layer_sizes=(5, 3),
          random_state=1,
          max_iter=50,
      )
      mlp_32.fit(X_digits[:300].astype(np.float32), y_digits[:300])
      pred_32 = mlp_32.predict(X_digits[300:].astype(np.float32))
  
      assert_allclose(pred_64, pred_32, rtol=1e-04)
  
imports: import re
  import sys
  import warnings
  from io import StringIO
  import joblib
  import numpy
  import pytest
  from numpy.testing import assert_allclose, assert_almost_equal, assert_array_equal
  from sklearn.datasets import load_digits, load_iris, make_multilabel_classification, make_regression
  from sklearn.exceptions import ConvergenceWarning
  from sklearn.metrics import roc_auc_score
  from sklearn.neural_network import MLPClassifier, MLPRegressor
  from sklearn.preprocessing import LabelBinarizer, MinMaxScaler, scale
  from sklearn.utils._testing import ignore_warnings
  from sklearn.utils.fixes import CSR_CONTAINERS
label_desc: Use image node_id 32 for calling a global function with example usage: test_mlp_regressor_dtypes_casting() without return types