code: def check_X(X):
      """
      Validate the input array, and possible coerce to 2D.
  
      Calls :code:`sklearn.utils.check_array` on parameter X with the
      parameters suited for Adversarial Mitigation.
  
      Returns
      -------
      X : numpy.ndarray
          Cleaned data.
      """
      return check_array(
          X,
          accept_sparse=False,
          accept_large_sparse=False,
          dtype=float,
          allow_nd=True,
      ).astype(float)
  
imports: from math import ceil
  from time import time
  from sklearn.utils import check_scalar
  from ._constants import _IMPORT_ERROR_MESSAGE, _KWARG_ERROR_MESSAGE, _PREDICTION_FUNCTION_AMBIGUOUS, _PROGRESS_UPDATE, _CALLBACK_RETURNS_ERROR
  from ._backend_engine import BackendEngine
  from ._pytorch_engine import PytorchEngine
  from ._tensorflow_engine import TensorflowEngine
  from ._preprocessor import FloatTransformer, _get_type
  from sklearn.base import ClassifierMixin, RegressorMixin, BaseEstimator, TransformerMixin
  from sklearn.utils.validation import check_is_fitted, check_random_state, check_array
  from sklearn.exceptions import NotFittedError
  from numpy import zeros, argmax, arange
  import logging
label_desc: Use image node_id 1 for calling a global function with example usage: check_X(X) and returns: check_array