code: def __init__(self, cfg: D2vDecoderConfig, input_dim):
      super().__init__(cfg)
  
      def make_block(in_dim):
          block = [
              nn.Conv1d(
                  in_dim,
                  cfg.decoder_dim,
                  kernel_size=cfg.decoder_kernel,
                  padding=cfg.decoder_kernel // 2,
                  groups=cfg.decoder_groups,
              ),
              SamePad(cfg.decoder_kernel),
              TransposeLast(),
              LayerNorm(cfg.decoder_dim, elementwise_affine=False),
              TransposeLast(),
              nn.GELU(),
          ]
  
          return nn.Sequential(*block)
  
      self.blocks = nn.Sequential(
          *[
              make_block(input_dim if i == 0 else cfg.decoder_dim)
              for i in range(cfg.decoder_layers)
          ]
      )
  
      projs = []
      curr_dim = cfg.decoder_dim
      for i in range(cfg.projection_layers - 1):
          next_dim = (
              int(curr_dim * cfg.projection_ratio)
              if i == 0
              else curr_dim
          )
          projs.append(nn.Linear(curr_dim, next_dim))
          projs.append(nn.GELU())
          curr_dim = next_dim
      projs.append(nn.Linear(curr_dim, input_dim))
      if len(projs) == 1:
          self.proj = projs[0]
      else:
          self.proj = nn.Sequential(*projs)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  import numpy
  from dataclasses import dataclass
  from fairseq.modules import LayerNorm, SamePad, SamePad2d, TransposeLast
label_desc: Use image node_id 1 to create a new Decoder1d object from inherited base classes: DecoderBase with example: obj = Decoder1d(cfg, input_dim)