code: def NonlinearLayer(
      in_features, out_features, bias=True, activation_fn=nn.ReLU
  ):
      """Weight-normalized non-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 nn.Sequential(m, activation_fn())
  
imports: import abc
  import math
  import torch
  import torch.nn
  import torch.nn.functional
  from fairseq import utils
  from fairseq.models import FairseqIncrementalDecoder, transformer
  from pytorch_translate import rnn_cell, utils, vocab_reduction
  from pytorch_translate.research.lexical_choice import lexical_translation
label_desc: Use image node_id 2 for calling a global function with example usage: NonlinearLayer(in_features, out_features, bias, activation_fn) and returns: nn