code: def __init__(self, input_size, channels, dilation, dropout=0.1):
      super(ConvolutionLayer, self).__init__()
      self.base = nn.Sequential(
          nn.Dropout2d(dropout),
          nn.Conv2d(input_size, channels, kernel_size=1),
          nn.GELU(),
      )
  
      self.convs = nn.ModuleList(
          [
              nn.Conv2d(
                  channels,
                  channels,
                  kernel_size=3,
                  groups=channels,
                  dilation=d,
                  padding=d,
              )
              for d in dilation
          ]
      )
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
  from transformers import AutoModel
label_desc: Use image node_id 1 to create a new ConvolutionLayer object from inherited base classes: nn with example: obj = ConvolutionLayer(input_size, channels, dilation, dropout)