text: import pytest
  from torch.nn import LSTMCell as _LSTMCell
  from neuralpy.layers.recurrent import LSTMCell
  
  
  def test_RNN_should_throws_type_error():
      with pytest.raises(TypeError):
          LSTMCell()
  
  
  @pytest.mark.parametrize(
      "input_size, hidden_size, bias, name",
      [
          (False, 1, True, "test"),
          (1, 1, None, "test"),
          (1, None, False, "test"),
          (1, 2, 3, "test"),
          (1, 2, "invalid", "test"),
          (1, 2, True, ""),
      ],
  )
  def test_LSTMCell_should_throw_value_error(input_size, hidden_size, bias, name):
  
      with pytest.raises(ValueError):
          LSTMCell(input_size=input_size, hidden_size=hidden_size, bias=bias, name=name)
  
  
  # Possible values
  input_sizes = [1, 2, None]
  hidden_sizes = [2, 2]
  biases = [False, True]
  names = ["Test", None]
  
  
  @pytest.mark.parametrize(
      "input_size, hidden_size, bias, name",
      [
          (input_size, hidden_size, bias, name)
          for input_size in input_sizes
          for hidden_size in hidden_sizes
          for bias in biases
          for name in names
      ],
  )
  def test_LSTMCell_layer_get_method(input_size, hidden_size, bias, name):
  
      x = LSTMCell(input_size=input_size, hidden_size=hidden_size, bias=bias, name=name)
  
      prev_dim = (6,)
  
      if input_size is None:
          x.set_input_dim(prev_dim, "LSTMCell")
  
      details = x.get_layer()
  
      assert isinstance(details, dict) is True
  
      assert details["layer_details"] == (hidden_size,)
  
      assert details["name"] == name
  
      assert issubclass(details["layer"], _LSTMCell) is True
  
      assert isinstance(details["keyword_arguments"], dict) is True
  
      if input_size:
          assert details["keyword_arguments"]["input_size"] == input_size
      else:
          assert details["keyword_arguments"]["input_size"] == prev_dim[0]
  
      assert details["keyword_arguments"]["hidden_size"] == hidden_size
  
      assert details["keyword_arguments"]["bias"] == bias
  
  
  def test_LSTMCell_with_invalid_layer():
      with pytest.raises(ValueError):
          x = LSTMCell(hidden_size=128, input_size=None)
  
          x.set_input_dim((64,), "conv1d")
  