code: def check_consistent_shape(
      X_train, y_train, X_test, y_test, y_train_pred, y_test_pred
  ):
      """Internal shape to check input data shapes are consistent.
  
      Parameters
      ----------
      X_train : numpy array of shape (n_samples, n_features)
          The training samples.
  
      y_train : list or array of shape (n_samples,)
          The ground truth of training samples.
  
      X_test : numpy array of shape (n_samples, n_features)
          The test samples.
  
      y_test : list or array of shape (n_samples,)
          The ground truth of test samples.
  
      y_train_pred : numpy array of shape (n_samples, n_features)
          The predicted binary labels of the training samples.
  
      y_test_pred : numpy array of shape (n_samples, n_features)
          The predicted binary labels of the test samples.
  
      Returns
      -------
      X_train : numpy array of shape (n_samples, n_features)
          The training samples.
  
      y_train : list or array of shape (n_samples,)
          The ground truth of training samples.
  
      X_test : numpy array of shape (n_samples, n_features)
          The test samples.
  
      y_test : list or array of shape (n_samples,)
          The ground truth of test samples.
  
      y_train_pred : numpy array of shape (n_samples, n_features)
          The predicted binary labels of the training samples.
  
      y_test_pred : numpy array of shape (n_samples, n_features)
          The predicted binary labels of the test samples.
      """
  
      # check input data shapes are consistent
      X_train, y_train = check_X_y(X_train, y_train)
      X_test, y_test = check_X_y(X_test, y_test)
  
      y_test_pred = column_or_1d(y_test_pred)
      y_train_pred = column_or_1d(y_train_pred)
  
      check_consistent_length(y_train, y_train_pred)
      check_consistent_length(y_test, y_test_pred)
  
      if X_train.shape[1] != X_test.shape[1]:
          raise ValueError(
              "X_train {0} and X_test {1} have different number "
              "of features.".format(X_train.shape, X_test.shape)
          )
  
      return X_train, y_train, X_test, y_test, y_train_pred, y_test_pred
  
file_path: pyod/pyod/utils/data.py
imports: from __future__ import division
  from __future__ import print_function
  from warnings import warn
  import numpy
  from sklearn.datasets import make_blobs
  from sklearn.metrics import roc_auc_score
  from sklearn.model_selection import train_test_split
  from sklearn.utils import check_X_y
  from sklearn.utils import check_consistent_length
  from sklearn.utils import check_random_state
  from sklearn.utils import column_or_1d
  from .utility import check_parameter
  from .utility import precision_n_scores
label_desc: Use image node_id 4 for calling a global function with example usage: check_consistent_shape(X_train, y_train, X_test, y_test, y_train_pred, y_test_pred) and returns: X_train,  y_train,  X_test,  y_test,  y_train_pred,  y_test_pred