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: Initialize Inception Time.
  
  ----------
  :param n_filters: int,
  :param use_residual: boolean,
  :param use_bottleneck: boolean,
  :param depth: int
  :param kernel_size: int, specifying the length of the 1D convolution
   window
  :param bottleneck_size: int,
  :param random_state: int, seed to any needed random actions
docstr_tok: ["Initialize","Inception","Time",".","--","--","--","--","--",":","param","n_filters",":","int",",",":","param","use_residual",":","boolean",",",":","param","use_bottleneck",":","boolean",",",":","param","depth",":","int",":","param","kernel_size",":","int",",","specifying","the","length","of","the","1D","convolution","window",":","param","bottleneck_size",":","int",",",":","param","random_state",":","int",",","seed","to","any","needed","random","actions"]
code: def __init__(
      self,
      n_filters=32,
      use_residual=True,
      use_bottleneck=True,
      bottleneck_size=32,
      depth=6,
      kernel_size=40,
      random_state=0,
  ):
      """Initialize Inception Time.
  
      ----------
      :param n_filters: int,
      :param use_residual: boolean,
      :param use_bottleneck: boolean,
      :param depth: int
      :param kernel_size: int, specifying the length of the 1D convolution
       window
      :param bottleneck_size: int,
      :param random_state: int, seed to any needed random actions
      """
      _check_dl_dependencies(severity="error")
      super().__init__()
  
      self.n_filters = n_filters
      self.use_residual = use_residual
      self.use_bottleneck = use_bottleneck
      self.depth = depth
      self.kernel_size = kernel_size
      self.bottleneck_size = bottleneck_size
      self.random_state = random_state
  
imports: from sktime.networks.base import BaseDeepNetwork
  from sktime.utils.validation._dependencies import _check_dl_dependencies
label_desc: Use image node_id 1 to create a new InceptionTimeNetwork object from inherited base classes: BaseDeepNetwork with example: obj = InceptionTimeNetwork(n_filters, use_residual, use_bottleneck, bottleneck_size, depth, kernel_size, random_state)