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: Summary function to return the losses/metrics for model fit.
  
  Returns
  -------
  history: dict or None,
      Dictionary containing model's train/validation losses and metrics
docstr_tok: ["Summary","function","to","return","the","losses\/metrics","for","model","fit",".","Returns","--","--","--","-","history",":","dict","or","None",",","Dictionary","containing","model","'s","train\/validation","losses","and","metrics"]
code: def summary(self):
      """Summary function to return the losses/metrics for model fit.
  
      Returns
      -------
      history: dict or None,
          Dictionary containing model's train/validation losses and metrics
      """
      return self.history.history if self.history is not None else None
  
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 3 for calling the BaseDeepClassifier obj's underlying member method code with example usage: obj.summary() and returns: unknown