class_docstr: New residual branches in each encoder layer.
  This alternates dilated convolutions, potentially with LSTMs and attention.
  Also before entering each residual branch, dimension is projected on a smaller subspace,
  e.g. of dim `channels // compress`.
  
  Args:
      channels (int): input/output channels for residual branch.
      compress (float, optional): amount of channel compression inside the branch. (default: 4)
      depth (int, optional): number of layers in the residual branch. Each layer has its own
          projection, and potentially LSTM and attention.(default: 2)
      init (float, optional): initial scale for LayerNorm. (default: 1e-4)
      norm_type (bool, optional): Norm type, either ``group_norm `` or ``none`` (Default: ``group_norm``)
      attn (bool, optional): use LocalAttention. (Default: ``False``)
      heads (int, optional): number of heads for the LocalAttention.  (default: 4)
      ndecay (int, optional): number of decay controls in the LocalAttention. (default: 4)
      lstm (bool, optional): use LSTM. (Default: ``False``)
      kernel_size (int, optional): kernel size for the (dilated) convolutions. (default: 3)
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docstr: DConv forward call
  
  Args:
      x (torch.Tensor): input tensor for convolution
  
  Returns:
      Tensor
          Output after being run through layers.
docstr_tok: ["DConv","forward","call","Args",":","x","(","torch.Tensor",")",":","input","tensor","for","convolution","Returns",":","Tensor","Output","after","being","run","through","layers","."]
code: def forward(self, x):
      r"""DConv forward call
  
      Args:
          x (torch.Tensor): input tensor for convolution
  
      Returns:
          Tensor
              Output after being run through layers.
      """
      for layer in self.layers:
          x = x + layer(x)
      return x
  
imports: import math
  import typing
  from typing import Any, Dict, List, Optional
  import torch
  from torch import nn
  from torch.nn import functional
label_desc: Use image node_id 2 for calling the _DConv obj's underlying member method code with example usage: obj.forward(x) and returns: x