code: def test_mlp_param_dtypes(dtype, Estimator):
      # Checks if input dtype is used for network parameters
      # and predictions
      X, y = X_digits.astype(dtype), y_digits
      mlp = Estimator(
          alpha=1e-5,
          hidden_layer_sizes=(5, 3),
          random_state=1,
          max_iter=50,
      )
      mlp.fit(X[:300], y[:300])
      pred = mlp.predict(X[300:])
  
      assert all(
          [intercept.dtype == dtype for intercept in mlp.intercepts_]
      )
  
      assert all([coef.dtype == dtype for coef in mlp.coefs_])
  
      if Estimator == MLPRegressor:
          assert pred.dtype == dtype
  
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 33 for calling a global function with example usage: test_mlp_param_dtypes(dtype, Estimator) without return types