text: import pytest
  from torch.nn import Conv1d
  from neuralpy.layers import Conv1D
  
  
  def test_conv1d_should_throw_type_error():
      with pytest.raises(TypeError) as ex:
          x = Conv1D()
  
  
  @pytest.mark.parametrize(
      "filters, kernel_size, input_shape, stride, padding, dilation, groups, bias, name",
      [
          (0.3, 0.3, 0.36, "invalid", "invalid", "invalid", "groups", False, ""),
          (0.3, 0.3, 0.36, "invalid", "invalid", "invalid", "groups", False, ""),
          (16, 2, 0.36, "invalid", "invalid", "invalid", "groups", False, ""),
          (32, 3, 0.36, "invalid", "invalid", "invalid", "groups", False, ""),
          (32, 3, ("", 26, 26), "invalid", "invalid", "invalid", "groups", False, ""),
          (32, 3, (3, "", 26), "invalid", "invalid", "invalid", "groups", False, ""),
          (32, 3, (3, 26, ""), "invalid", "invalid", "invalid", "groups", False, ""),
          (64, 4, (3, 26, 26), "invalid", "invalid", "invalid", "groups", False, ""),
          (128, 4, (3, 26, 26), 4.6, "invalid", "invalid", "groups", False, ""),
          (256, 4, (3, 26, 26), (34,), "invalid", "invalid", "groups", False, ""),
          (256, 4, (3, 26, 26), (34,), "invalid", "invalid", "groups", False, ""),
          (256, 4, (3, 26, 26), (34,), 7.5, "invalid", "groups", False, ""),
          (256, 4, (3, 26, 26), (34,), (34,), "invalid", "groups", False, ""),
          (256, 4, (3, 26, 26), (34,), (34,), 4.7, "groups", False, ""),
          (256, 4, (3, 26, 26), (34,), (34,), (34,), "groups", False, ""),
          (256, 4, (3, 26, 26), (34,), (34,), (34,), False, False, ""),
          (256, 4, (3, 26, 26), (34,), (34,), (34,), 1, "invalid", ""),
          (256, 4, (3, 26, 26), (34,), (34,), (34,), 1, False, ""),
          (256, 4, (3, 26, 26), (34,), (34,), (34,), 1, False, 34),
      ]
  )
  def test_conv1d_should_throw_value_error(filters, kernel_size, input_shape, stride, padding, dilation, groups, bias, name):
      with pytest.raises(ValueError) as ex:
          x = Conv1D(filters=filters, kernel_size=kernel_size, input_shape=input_shape,
                     stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias, name=name)
  
  
  # Possible values
  filters = [23, 64]
  kernel_size = [4]
  input_shape = [(3, 26, 26)]
  stride = [(34,)]
  padding = [(34,)]
  dilation = [(34,)]
  groups = 1
  biases = [True, False]
  names = ["Test", None]
  
  
  # @pytest.mark.parametrize(
  #     "filters, kernel_size, input_shape, stride, padding, dilation, groups, bias, name",
  #     [(filters, kernel_size, input_shape, stride, padding, dilation, groups, bias, name)
  #      for _filters in filters
  #      for _kernel_size in kernel_size
  #      for _input_shape in input_shape
  #      for _stride in stride
  #      for _padding in padding
  #      for _dilation in dilation
  #      for _groups in groups
  #      for _bias in biases
  #      for _name in names]
  # )
  # def test_conv1d_get_layer_method(filters, kernel_size, input_shape, stride, padding, dilation, groups, bias, name):
  #     x = Conv1D(filters=filters, kernel_size=kernel_size, input_shape=input_shape,
  #                stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias, name=name)
  #     prev_dim = (3, 26, 26),
  
  #     if input_shape is None:
  #         x.get_input_dim(prev_dim, "conv1d")
  
  #     details = x.get_layer()
  
  #     assert isinstance(details, dict) == True
  
  #     assert details["layer_details"] == (n_nodes,)
  
  #     assert details["name"] == name
  
  #     assert issubclass(details["layer"], Linear) == True
  
  #     assert isinstance(details["keyword_arguments"], dict) == True
  
  #     if n_inputs:
  #         assert details["keyword_arguments"]["in_features"] == n_inputs
  #     else:
  #         assert details["keyword_arguments"]["in_features"] == prev_dim[0]
  
  #     assert details["keyword_arguments"]["out_features"] == n_nodes
  
  #     assert details["keyword_arguments"]["bias"] == bias
  