class_docstr: Base class for forests of trees.
code: def _validate_X_predict(self, X):
      n_features = X.shape[1] if X.ndim == 2 else 1
      if self.n_columns != n_features:
          raise ValueError(
              "Number of features of the model must "
              "match the input. Model n_features is %s and "
              "input n_features is %s " % (self.n_columns, n_features)
          )
  
      return X
  
imports: from abc import abstractmethod
  from warnings import catch_warnings, simplefilter
  import numpy
  import pandas
  from joblib import Parallel, delayed
  from numpy import float64
  from sklearn.base import clone
  from sklearn.ensemble._forest import MAX_INT, BaseForest, _generate_sample_indices, _get_n_samples_bootstrap
  from sklearn.exceptions import DataConversionWarning
  from sklearn.utils import check_array, check_random_state, compute_sample_weight
  from sktime.transformations.panel.summarize import RandomIntervalFeatureExtractor
  from sktime.utils.random_state import set_random_state
  from sktime.utils.warnings import warn
label_desc: Use image node_id 6 for calling the BaseTimeSeriesForest obj's underlying member method code with example usage: obj._validate_X_predict(X) and returns: X