code: def Linear(in_features, out_features, bias=True, dropout=0):
      """Linear layer (input: N x T x C)"""
      m = nn.Linear(in_features, out_features, bias=bias)
      # m.weight.data.uniform_(-0.1, 0.1)
      # if bias:
      #     m.bias.data.uniform_(-0.1, 0.1)
      return m
  
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 4 for calling a global function with example usage: Linear(in_features, out_features, bias, dropout) and returns: m