code: def forward(
      self,  # type: ignore
      layer_input: torch.Tensor,
      layer_output: torch.Tensor,
      layer_index: int = None,
      total_layers: int = None,
  ) -> torch.Tensor:
      """
      Apply dropout to this layer, for this whole mini-batch.
      dropout_prob = layer_index / total_layers * undecayed_dropout_prob if layer_idx and
      total_layers is specified, else it will use the undecayed_dropout_prob directly.
  
      # Parameters
  
      layer_input `torch.FloatTensor` required
          The input tensor of this layer.
      layer_output `torch.FloatTensor` required
          The output tensor of this layer, with the same shape as the layer_input.
      layer_index `int`
          The layer index, starting from 1. This is used to calcuate the dropout prob
          together with the `total_layers` parameter.
      total_layers `int`
          The total number of layers.
  
      # Returns
  
      output : `torch.FloatTensor`
          A tensor with the same shape as `layer_input` and `layer_output`.
      """
      if layer_index is not None and total_layers is not None:
          dropout_prob = (
              1.0
              * self.undecayed_dropout_prob
              * layer_index
              / total_layers
          )
      else:
          dropout_prob = 1.0 * self.undecayed_dropout_prob
      if self.training:
          if torch.rand(1) < dropout_prob:
              return layer_input
          else:
              return layer_output + layer_input
      else:
          return (1 - dropout_prob) * layer_output + layer_input
  
imports: import torch
label_desc: Use image node_id 2 for calling the ResidualWithLayerDropout obj's underlying member method code with example usage: obj.forward(layer_input, layer_output, layer_index, total_layers) and returns: unknown, layer_input, unknown