code: def LinearizedConv1d(
      in_channels, out_channels, kernel_size, dropout=0, **kwargs
  ):
      """Weight-normalized Conv1d layer optimized for decoding"""
      m = LinearizedConvolution(
          in_channels, out_channels, kernel_size, **kwargs
      )
      std = math.sqrt(
          (4 * (1.0 - dropout)) / (m.kernel_size[0] * in_channels)
      )
      nn.init.normal_(m.weight, mean=0, std=std)
      nn.init.constant_(m.bias, 0)
      return nn.utils.weight_norm(m, dim=2)
  
imports: import argparse
  import math
  from collections.abc import Iterable
  import torch
  import torch.nn
  from examples.speech_recognition.data.data_utils import lengths_to_encoder_padding_mask
  from fairseq import utils
  from fairseq.models import FairseqEncoder, FairseqEncoderDecoderModel, FairseqEncoderModel, FairseqIncrementalDecoder, register_model, register_model_architecture
  from fairseq.modules import LinearizedConvolution, TransformerDecoderLayer, TransformerEncoderLayer, VGGBlock
label_desc: Use image node_id 5 for calling a global function with example usage: LinearizedConv1d(in_channels, out_channels, kernel_size, dropout) and returns: nn