class_docstr: Some pruners may prune the input channel of the convolutional
  layers. While pruning the input channel of the convolutional layers,
  the layers that share the same input tensor should prune the same
  channels, and we say these layers that share the same input tensor/channel
  has the input channel dependency. If we only prune the input channel of one
  layer in the dependency set, there will be a shape conflict for the other
  layers in the same dependency set, which may trigger a runtime error.
  Here we judge whether the application will truncate the dependency by analyzing
  whether the number of channels before and after the operation has changed.
  If not, the input channel dependency will be passed to the following nodes.
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docstr: This model analyze the input channel dependencies between the conv
  layers in a model.
  
  Parameters
  ----------
  model : torch.nn.Module
      The model to be analyzed.
  data : torch.Tensor
      The example input data to trace the network architecture.
  traced_model : torch._C.Graph
      if we alreay has the traced graph of the target model, we donnot
      need to trace the model again.
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code: def __init__(self, model, dummy_input=None, traced_model=None):
      """
      This model analyze the input channel dependencies between the conv
      layers in a model.
  
      Parameters
      ----------
      model : torch.nn.Module
          The model to be analyzed.
      data : torch.Tensor
          The example input data to trace the network architecture.
      traced_model : torch._C.Graph
          if we alreay has the traced graph of the target model, we donnot
          need to trace the model again.
      """
      super(InputChannelDependency, self).__init__(
          model, dummy_input, traced_model
      )
  
imports: import csv
  import logging
label_desc: Use image node_id 1 to create a new InputChannelDependency object from inherited base classes: ChannelDependency with example: obj = InputChannelDependency(model, dummy_input, traced_model)