content: import unittest
  
  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
  
  
  class TestArangeConverter(DispatchTestCase):
      @parameterized.expand(
          [
              (0, 5, 1),
              (1, 5, 2),
              (3, 5, 3),
              (5, 0, -1),
              (5, 1, -2),
              (5, 3, -3),
              (5, -2, -1),
              (-5, -2, 2),
              (-5, -3, 1),
              (-2, -5, -1),
          ]
      )
      def test_arange(self, start, end, step):
          class Arange(nn.Module):
              def forward(self, x):
                  return torch.ops.aten.arange.start_step(start, end, step)
  
          inputs = [torch.randn(1, 1)]
          self.run_test(
              Arange(),
              inputs,
              use_dynamo_tracer=True,
          )
  
      def test_arange_dynamic(self):
          class Arange(nn.Module):
              def forward(self, end_tensor):
                  return torch.ops.aten.arange.start_step(0, end_tensor, 1)
  
          pyt_input = 7
          inputs = [
              torch_tensorrt.Input(
                  min_shape=(5,),
                  opt_shape=(7,),
                  max_shape=(10,),
                  dtype=torch.int64,
                  torch_tensor=torch.tensor(pyt_input, dtype=torch.int64).cuda(),
                  is_shape_tensor=True,
              )
          ]
          self.run_test_with_dynamic_shape(
              Arange(),
              inputs,
              use_example_tensors=False,
              check_dtype=False,
              pyt_inputs=[pyt_input],
              use_dynamo_tracer=False,
          )
  
  
  if __name__ == "__main__":
      run_tests()
  