code: def train_test_split(*arrays, test_size=None, train_size=None):
      """Split arrays or matrices into sequential train and test subsets
  
      Creates train/test splits over endogenous arrays an optional exogenous
      arrays. This is a wrapper of scikit-learn's ``train_test_split`` that
      does not shuffle.
  
      Parameters
      ----------
      *arrays : sequence of indexables with same length / shape[0]
          Allowed inputs are lists, numpy arrays, scipy-sparse
          matrices or pandas dataframes.
  
      test_size : float, int or None, optional (default=None)
          If float, should be between 0.0 and 1.0 and represent the proportion
          of the dataset to include in the test split. If int, represents the
          absolute number of test samples. If None, the value is set to the
          complement of the train size. If ``train_size`` is also None, it will
          be set to 0.25.
  
      train_size : float, int, or None, (default=None)
          If float, should be between 0.0 and 1.0 and represent the
          proportion of the dataset to include in the train split. If
          int, represents the absolute number of train samples. If None,
          the value is automatically set to the complement of the test size.
  
      Returns
      -------
      splitting : list, length=2 * len(arrays)
          List containing train-test split of inputs.
  
      Examples
      --------
      >>> import pmdarima as pm
      >>> from pmdarima.model_selection import train_test_split
      >>> y = pm.datasets.load_sunspots()
      >>> y_train, y_test = train_test_split(y, test_size=50)
      >>> y_test.shape
      (50,)
  
      The split is sequential:
  
      >>> import numpy as np
      >>> from numpy.testing import assert_array_equal
      >>> assert_array_equal(y, np.concatenate([y_train, y_test]))
      """
      return tts(
          *arrays,
          shuffle=False,
          stratify=None,
          test_size=test_size,
          train_size=train_size,
      )
  
imports: import abc
  import numpy
  from sklearn.base import BaseEstimator
  from sklearn.utils.validation import indexable
  from sklearn.model_selection import train_test_split
label_desc: Use image node_id 1 for calling a global function with example usage: train_test_split() and returns: tts