class_docstr: Transformer that returns a copy of the input array
  
  For usage examples, please see
  https://rasbt.github.io/mlxtend/user_guide/preprocessing/CopyTransformer/
docstr: Return a copy of the input array.
  
  Parameters
  ----------
  X : {array-like, sparse matrix}, shape = [n_samples, n_features]
      Training vectors, where n_samples is the number of samples and
      n_features is the number of features.
  y : array-like, shape = [n_samples] (default: None)
  
  Returns
  ---------
  X_copy : copy of the input X array.
code: def transform(self, X, y=None):
      """Return a copy of the input array.
  
      Parameters
      ----------
      X : {array-like, sparse matrix}, shape = [n_samples, n_features]
          Training vectors, where n_samples is the number of samples and
          n_features is the number of features.
      y : array-like, shape = [n_samples] (default: None)
  
      Returns
      ---------
      X_copy : copy of the input X array.
  
      """
      if isinstance(X, list):
          return np.asarray(X)
      elif isinstance(X, np.ndarray) or issparse(X):
          return X.copy()
      else:
          raise ValueError(
              "X must be a list or NumPy array"
              " or SciPy sparse array. Found %s" % type(X)
          )
  
file_path: mlxtend/mlxtend/preprocessing/copy_transformer.py
imports: import numpy
  from scipy.sparse import issparse
  from sklearn.base import BaseEstimator
label_desc: Use image node_id 2 for calling the CopyTransformer obj's underlying member method code with example usage: obj.transform(X, y) and returns: np, X