class_docstr: Abstract base class for deep learning time series classifiers.
  
  The base classifier provides a deep learning default method for
  _predict and _predict_proba, and provides a new abstract method for building a
  model.
  
  Parameters
  ----------
  batch_size : int, default = 40
      training batch size for the model
  
  Attributes
  ----------
  self.model_ - the fitted DL model
code: def _predict(self, X, **kwargs):
      probs = self._predict_proba(X, **kwargs)
      rng = check_random_state(self.random_state)
      return np.array(
          [
              self.classes_[
                  int(rng.choice(np.flatnonzero(prob == prob.max())))
              ]
              for prob in probs
          ]
      )
  
imports: from abc import ABC, abstractmethod
  import numpy
  from sklearn.preprocessing import LabelEncoder, OneHotEncoder
  from sklearn.utils import check_random_state
  from sktime.base._base import SERIALIZATION_FORMATS
  from sktime.classification.base import BaseClassifier
  from sktime.utils.validation._dependencies import _check_soft_dependencies
label_desc: Use image node_id 4 for calling the BaseDeepClassifier obj's underlying member method code with example usage: obj._predict(X) and returns: np