code: def LSTM(input_size, hidden_size, **kwargs):
      m = nn.LSTM(input_size, hidden_size, **kwargs)
      for name, param in m.named_parameters():
          if "weight" in name or "bias" in name:
              param.data.uniform_(-0.1, 0.1)
      return m
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from fairseq import options, utils
  from fairseq.models import FairseqEncoder, FairseqIncrementalDecoder, FairseqEncoderDecoderModel, register_model, register_model_architecture
label_desc: Use image node_id 2 for calling a global function with example usage: LSTM(input_size, hidden_size) and returns: m