code: def __init__(
      self,
      seq_len=seqs_length,
      batch_size=batch_size,
      feature_size=lstm_feature_size,
      hidden_size=lstm_hidden_size,
      batch_first=False,
      layer_num=1,
      bidirectional=False,
      proj_size=0,
      use_bias=True,
      rnd_weights_init=False,
  ):
      super().__init__()
  
      # Shapes
      self.shape = [seq_len, batch_size, feature_size]
      if batch_first:
          self.shape = [batch_size, seq_len, feature_size]
      layers_num = 2 * layer_num if bidirectional else layer_num
      self.h0_shape = [layers_num, batch_size, hidden_size]
      if proj_size > 0:
          self.h0_shape = [layers_num, batch_size, proj_size]
      self.c0_shape = [layers_num, batch_size, hidden_size]
      # Dummy inputs
      self.dummy_inputs = (
          torch.rand(self.shape),
          (torch.zeros(self.h0_shape), torch.zeros(self.c0_shape)),
      )
  
      if check_torch_version_for_proj_in_lstm():
          self.model = nn.LSTM(
              input_size=lstm_feature_size,
              hidden_size=lstm_hidden_size,
              num_layers=layer_num,
              bidirectional=bidirectional,
              proj_size=proj_size,
              batch_first=batch_first,
              bias=use_bias,
          )
      else:
          if proj_size > 0:
              print(
                  "WARNING: projection is not supported for torch version less than 1.8.0! ",
                  "LSTM was constructed without projection!",
              )
              # sys.exit()
          self.model = nn.LSTM(
              input_size=lstm_feature_size,
              hidden_size=lstm_hidden_size,
              num_layers=layer_num,
              bidirectional=bidirectional,
              batch_first=batch_first,
              bias=use_bias,
          )
  
      if rnd_weights_init:
          self.gen_rnd_weights()
  
imports: import torch
  import tvm
  import tvm.testing
  import onnx
  import io
  import sys
  from tvm import relay
  from tvm.contrib import graph_executor
  from torch import nn
label_desc: Use image node_id 1 to create a new LSTM_Model object from inherited base classes: RNN_Model with example: obj = LSTM_Model(seq_len, batch_size, feature_size, hidden_size, batch_first, layer_num, bidirectional, proj_size, use_bias, rnd_weights_init)