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} }
code: def _inception_module(
      self, input_tensor, stride=1, activation="linear"
  ):
      from tensorflow import keras
  
      if self.use_bottleneck and int(input_tensor.shape[-1]) > 1:
          input_inception = keras.layers.Conv1D(
              filters=self.bottleneck_size,
              kernel_size=1,
              padding="same",
              activation=activation,
              use_bias=False,
          )(input_tensor)
      else:
          input_inception = input_tensor
  
      # kernel_size_s = [3, 5, 8, 11, 17]
      kernel_size_s = [self.kernel_size // (2**i) for i in range(3)]
  
      conv_list = []
  
      for i in range(len(kernel_size_s)):
          conv_list.append(
              keras.layers.Conv1D(
                  filters=self.n_filters,
                  kernel_size=kernel_size_s[i],
                  strides=stride,
                  padding="same",
                  activation=activation,
                  use_bias=False,
              )(input_inception)
          )
  
      max_pool_1 = keras.layers.MaxPool1D(
          pool_size=3, strides=stride, padding="same"
      )(input_tensor)
  
      conv_6 = keras.layers.Conv1D(
          filters=self.n_filters,
          kernel_size=1,
          padding="same",
          activation=activation,
          use_bias=False,
      )(max_pool_1)
  
      conv_list.append(conv_6)
  
      x = keras.layers.Concatenate(axis=2)(conv_list)
      x = keras.layers.BatchNormalization()(x)
      x = keras.layers.Activation(activation="relu")(x)
      return x
  
imports: from sktime.networks.base import BaseDeepNetwork
  from sktime.utils.validation._dependencies import _check_dl_dependencies
label_desc: Use image node_id 2 for calling the InceptionTimeNetwork obj's underlying member method code with example usage: obj._inception_module(input_tensor, stride, activation) and returns: x