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: Find probability estimates for each class for all cases in X.
  
  Parameters
  ----------
  X : an np.ndarray of shape = (n_instances, n_dimensions, series_length)
      The training input samples.         input_checks: boolean
      whether to check the X parameter
  
  Returns
  -------
  output : array of shape = [n_instances, n_classes] of probabilities
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code: def _predict_proba(self, X, **kwargs):
      """Find probability estimates for each class for all cases in X.
  
      Parameters
      ----------
      X : an np.ndarray of shape = (n_instances, n_dimensions, series_length)
          The training input samples.         input_checks: boolean
          whether to check the X parameter
  
      Returns
      -------
      output : array of shape = [n_instances, n_classes] of probabilities
      """
      # Transpose to work correctly with keras
      X = X.transpose((0, 2, 1))
      probs = self.model_.predict(X, self.batch_size, **kwargs)
  
      # check if binary classification
      if probs.shape[1] == 1:
          # first column is probability of class 0 and second is of class 1
          probs = np.hstack([1 - probs, probs])
      probs = probs / probs.sum(axis=1, keepdims=1)
      return 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 5 for calling the BaseDeepClassifier obj's underlying member method code with example usage: obj._predict_proba(X) and returns: probs