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
docstr: Convert y to required Keras format.
docstr_tok: ["Convert","y","to","required","Keras","format","."]
code: def convert_y_to_keras(self, y):
      """Convert y to required Keras format."""
      self.label_encoder = LabelEncoder()
      y = self.label_encoder.fit_transform(y)
      self.classes_ = self.label_encoder.classes_
      self.n_classes_ = len(self.classes_)
      y = y.reshape(len(y), 1)
      self.onehot_encoder = OneHotEncoder(
          sparse=False, categories="auto"
      )
      # categories='auto' to get rid of FutureWarning
      y = self.onehot_encoder.fit_transform(y)
      return y
  
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 6 for calling the BaseDeepClassifier obj's underlying member method code with example usage: obj.convert_y_to_keras(y) and returns: y