class_docstr: Abstract base class for deep learning networks.
class_docstr_tok: ["Abstract","base","class","for","deep","learning","networks","."]
docstr: Construct a network and return its input and output layers.
  
  Parameters
  ----------
  input_shape : tuple
      The shape of the data fed into the input layer
  
  Returns
  -------
  input_layer : a keras layer
  output_layer : a keras layer
docstr_tok: ["Construct","a","network","and","return","its","input","and","output","layers",".","Parameters","--","--","--","--","--","input_shape",":","tuple","The","shape","of","the","data","fed","into","the","input","layer","Returns","--","--","--","-","input_layer",":","a","keras","layer","output_layer",":","a","keras","layer"]
code: def build_network(self, input_shape, **kwargs):
      """Construct a network and return its input and output layers.
  
      Parameters
      ----------
      input_shape : tuple
          The shape of the data fed into the input layer
  
      Returns
      -------
      input_layer : a keras layer
      output_layer : a keras layer
      """
      ...
  
imports: from abc import ABC, abstractmethod
  import numpy
  import pandas
  from sktime.base import BaseObject
  from sktime.forecasting.base import BaseForecaster
  from sktime.utils.validation._dependencies import _check_soft_dependencies
label_desc: Use image node_id 1 for calling the BaseDeepNetwork obj's underlying member method code with example usage: obj.build_network(input_shape) without return types