content: import numpy as np
  import torch
  import torch.nn as nn
  import torch_tensorrt
  from parameterized import parameterized
  from torch.testing._internal.common_utils import run_tests
  
  from .harness import DispatchTestCase
  
  empty_ops = [
      (
          "empty_one_dimension",
          [1],
          None,
      ),
      (
          "empty_two_dimension",
          [1, 2],
          None,
      ),
      (
          "empty_three_dimension",
          [2, 3, 4],
          None,
      ),
      (
          "empty_one_dimension_dtype",
          [1],
          torch.float32,
      ),
      (
          "empty_two_dimension_dtype",
          [2, 3],
          torch.float32,
      ),
      (
          "empty_four_dimension_dtype",
          [1, 2, 2, 1],
          torch.float32,
      ),
      (
          "empty_five_dimension_dtype",
          [1, 2, 2, 2, 1],
          torch.float32,
      ),
  ]
  
  
  class TestEmptyConverter(DispatchTestCase):
      @parameterized.expand(
          [(empty_op[0], empty_op[1], empty_op[2]) for empty_op in empty_ops]
      )
      def test_empty(self, name, shape_or_input, data_type):
          class TestModule(nn.Module):
              def forward(self, x):
                  shape_or_input[0] = x.shape[0]
                  return torch.ops.aten.empty.memory_format(
                      shape_or_input,
                      dtype=data_type,
                  )
  
          empty_model = TestModule()
  
          inputs = [torch.randint(1, 3, shape_or_input, dtype=torch.int32)]
          comparator_shape_dtype_device = (
              lambda x, y, check_dtype: x.shape == y.shape
              and (x.stride() == y.stride())
              and (x.dtype == y.dtype if check_dtype else True)
          )
          expected_ops = []
          if "dtype" in name:
              self.run_test_compare_tensor_attributes_only(
                  empty_model,
                  inputs,
                  expected_ops,
                  [(comparator_shape_dtype_device, [True])],
                  use_dynamo_tracer=True,
              )
          else:
              self.run_test_compare_tensor_attributes_only(
                  empty_model,
                  inputs,
                  expected_ops,
                  [(comparator_shape_dtype_device, [False])],
                  use_dynamo_tracer=True,
              )
  
  
  if __name__ == "__main__":
      run_tests()
  