code: def __init__(
      self,
      self_attention,
      cross_attention,
      hidden_size,
      conv_hidden_size=None,
      dropout=0.1,
      activation="relu",
  ):
      super(TransDecoderLayer, self).__init__()
      conv_hidden_size = conv_hidden_size or 4 * hidden_size
      self.self_attention = self_attention
      self.cross_attention = cross_attention
      self.conv1 = nn.Conv1d(
          in_channels=hidden_size,
          out_channels=conv_hidden_size,
          kernel_size=1,
      )
      self.conv2 = nn.Conv1d(
          in_channels=conv_hidden_size,
          out_channels=hidden_size,
          kernel_size=1,
      )
      self.norm1 = nn.LayerNorm(hidden_size)
      self.norm2 = nn.LayerNorm(hidden_size)
      self.norm3 = nn.LayerNorm(hidden_size)
      self.dropout = nn.Dropout(dropout)
      self.activation = F.relu if activation == "relu" else F.gelu
  
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new TransDecoderLayer object from inherited base classes: nn with example: obj = TransDecoderLayer(_attention, cross_attention, hidden_size, conv_hidden_size, dropout, activation)