code: def test_float_double_arrays_fitting(self):
      X, y = Test.get_train_data(n_features=12, n_samples=300, nnz=0)
      learner_64 = LogisticRegression(random_state=32789, tol=1e-13)
      learner_64.fit(X, y)
      weights_64 = learner_64.weights
      self.assertEqual(weights_64.dtype, np.dtype("float64"))
  
      learner_32 = LogisticRegression(random_state=32789, tol=1e-13)
      X_32, y_32 = X.astype("float32"), y.astype("float32")
      learner_32.fit(X_32, y_32)
      weights_32 = learner_32.weights
      self.assertEqual(weights_32.dtype, np.dtype("float32"))
  
      np.testing.assert_array_almost_equal(
          weights_32, weights_64, decimal=5
      )
  
imports: import itertools
  import unittest
  import numpy
  from sklearn.metrics import roc_auc_score
  from tick.base.inference import InferenceTest
  from tick.linear_model import SimuLogReg, LogisticRegression
  from tick.simulation import weights_sparse_gauss
  from tick.preprocessing.features_binarizer import FeaturesBinarizer
  from tick.prox import ProxZero, ProxL1, ProxL2Sq, ProxElasticNet, ProxTV, ProxBinarsity
label_desc: Use image node_id 19 for calling the Test obj's underlying member method code with example usage: obj.test_float_double_arrays_fitting() without return types