code: def test_lstm(
      self,
      input_size,
      hidden_size,
      num_layers,
      bias,
      batch_first,
      dropout,
      bidirectional,
      backend,
  ):
      model = nn.LSTM(
          input_size=input_size,
          hidden_size=hidden_size,
          num_layers=num_layers,
          bias=bias,
          batch_first=batch_first,
          dropout=dropout,
          bidirectional=bidirectional,
      )
      SEQUENCE_LENGTH = 3
      BATCH_SIZE = 2
  
      num_directions = int(bidirectional) + 1
  
      # (seq_len, batch, input_size)
      if batch_first:
          _input = torch.rand(BATCH_SIZE, SEQUENCE_LENGTH, input_size)
      else:
          _input = torch.randn(SEQUENCE_LENGTH, BATCH_SIZE, input_size)
  
      h0 = torch.randn(
          num_layers * num_directions, BATCH_SIZE, hidden_size
      )
      c0 = torch.randn(
          num_layers * num_directions, BATCH_SIZE, hidden_size
      )
  
      inputs = (_input, (h0, c0))
      expected_results = model(*inputs)
      # Need to do some output reshaping if bidirectional
      if bidirectional:
          ex_hn = self._pytorch_hidden_to_coreml(expected_results[1][0])
          ex_cn = self._pytorch_hidden_to_coreml(expected_results[1][1])
          expected_results = (expected_results[0], (ex_hn, ex_cn))
      run_compare_torch(
          inputs,
          model,
          expected_results,
          input_as_shape=False,
          backend=backend,
      )
  
imports: import sys
  from coremltools.converters.mil import testing_reqs
  from coremltools.converters.mil.testing_reqs import *
  from .testing_utils import *
label_desc: Use image node_id 2 for calling the TestLSTM obj's underlying member method code with example usage: obj.test_lstm(input_size, hidden_size, num_layers, bias, batch_first, dropout, bidirectional, backend) without return types