class_docstr: Counterfactual Reinforcement Learning Tabular. 
class_docstr_tok: ["Counterfactual","Reinforcement","Learning","Tabular","."]
docstr: Validates the input instances by checking the appropriate dimensions.
  
  Parameters
  ----------
  X
      Input instances.
docstr_tok: ["Validates","the","input","instances","by","checking","the","appropriate","dimensions",".","Parameters","--","--","--","--","--","X","Input","instances","."]
code: def _validate_input(self, X: np.ndarray):
      """
      Validates the input instances by checking the appropriate dimensions.
  
      Parameters
      ----------
      X
          Input instances.
      """
      if len(X.shape) != 2:
          raise ValueError(
              f"The input should be a 2D array. Found {len(X.shape)}D instead."
          )
  
      # Check if the number of features matches the expected one.
      if X.shape[1] != len(self.params["feature_names"]):
          raise ValueError(
              f"Unexpected number of features. The expected number "
              f"is {len(self.params['feature_names'])}, but the input has {X.shape[1]} features."
          )
  
      return X
  
imports: from functools import partial
  from itertools import count
  from typing import TYPE_CHECKING, Callable, Dict, List, Optional, Tuple, Union, cast
  import numpy
  from tqdm import tqdm
  from alibi.api.interfaces import Explainer, Explanation
  from alibi.explainers.backends.cfrl_tabular import get_conditional_vector, get_statistics, sample
  from alibi.explainers.cfrl_base import _PARAM_TYPES, CounterfactualRL, Postprocessing
  from alibi.utils.frameworks import has_pytorch, has_tensorflow
label_desc: Use image node_id 3 for calling the CounterfactualRLTabular obj's underlying member method code with example usage: obj._validate_input(X) and returns: X