class_docstr: InceptionTime adapted from the implementation from Fawaz et al.
  
  https://github.com/hfawaz/InceptionTime/blob/master/classifiers/
  inception.py
  
  Network originally defined in:
  
  @article{IsmailFawaz2019inceptionTime, Title                    = {
  InceptionTime: Finding AlexNet for Time Series Classification}, Author
                  = {Ismail Fawaz, Hassan and Lucas, Benjamin and
                  Forestier, Germain and Pelletier, Charlotte and Schmidt,
                  Daniel F. and Weber, Jonathan and Webb, Geoffrey I. and
                  Idoumghar, Lhassane and Muller, Pierre-Alain and
                  Petitjean, François}, journal                  = {
                  ArXiv}, Year                     = {2019} }
docstr: Construct a network and return its input and output layers.
  
  ----------
  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",".","--","--","--","--","--","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.
  
      ----------
      input_shape : tuple
          The shape of the data fed into the input layer
      Returns
      -------
      input_layer : a keras layer
      output_layer : a keras layer
      """
      from tensorflow import keras
  
      input_layer = keras.layers.Input(input_shape)
  
      x = input_layer
      input_res = input_layer
  
      for d in range(self.depth):
          x = self._inception_module(x)
  
          if self.use_residual and d % 3 == 2:
              x = self._shortcut_layer(input_res, x)
              input_res = x
  
      gap_layer = keras.layers.GlobalAveragePooling1D()(x)
  
      return input_layer, gap_layer
  
imports: from sktime.networks.base import BaseDeepNetwork
  from sktime.utils.validation._dependencies import _check_dl_dependencies
label_desc: Use image node_id 4 for calling the InceptionTimeNetwork obj's underlying member method code with example usage: obj.build_network(input_shape) and returns: input_layer,  gap_layer