content: import torch
  import torch.nn as nn
  from parameterized import parameterized
  from torch.testing._internal.common_utils import run_tests
  from torch_tensorrt import Input
  
  from .harness import DispatchTestCase
  
  
  class TestGtConverter(DispatchTestCase):
      @parameterized.expand(
          [
              ("2d", (5, 3)),
              ("3d", (5, 3, 2)),
          ]
      )
      def test_gt_tensor(self, _, shape):
          class gt(nn.Module):
              def forward(self, lhs_val, rhs_val):
                  return torch.ops.aten.gt.Tensor(lhs_val, rhs_val)
  
          inputs = [torch.randn(shape), torch.randn(shape)]
          self.run_test(
              gt(),
              inputs,
          )
  
      @parameterized.expand(
          [
              ("2d", (5, 3), 1),
              ("3d", (5, 3, 2), 2.0),
          ]
      )
      def test_gt_tensor_scalar(self, _, shape, scalar):
          class gt(nn.Module):
              def forward(self, lhs_val):
                  return torch.ops.aten.gt.Tensor(lhs_val, torch.tensor(scalar))
  
          inputs = [torch.randn(shape)]
          self.run_test(
              gt(),
              inputs,
          )
  
      @parameterized.expand(
          [
              ("2d", (5, 3), 1),
              ("3d", (5, 3, 2), 2.0),
          ]
      )
      def test_gt_scalar(self, _, shape, scalar):
          class gt(nn.Module):
              def forward(self, lhs_val):
                  return torch.ops.aten.gt.Scalar(lhs_val, scalar)
  
          inputs = [torch.randn(shape)]
          self.run_test(
              gt(),
              inputs,
          )
  
      @parameterized.expand(
          [
              ("2d_2d", (5, 3), (5, 1)),
              ("3d_2d", (5, 3, 2), (3, 1)),
              ("4d_3d", (5, 3, 4, 1), (3, 1, 1)),
          ]
      )
      def test_gt_tensor_broadcast(self, _, lshape, rshape):
          class gt(nn.Module):
              def forward(self, lhs_val, rhs_val):
                  return torch.ops.aten.gt.Tensor(lhs_val, rhs_val)
  
          inputs = [
              torch.randint(0, 3, lshape, dtype=torch.int32),
              torch.randint(0, 3, rshape, dtype=torch.int32),
          ]
          self.run_test(
              gt(),
              inputs,
          )
  
      @parameterized.expand(
          [
              ("2d_2d", (2, 3), (4, 3), (5, 3), (2, 3), (4, 3), (5, 3)),
              ("3d_2d", (2, 2, 2), (2, 3, 2), (2, 4, 2), (2, 1), (3, 1), (4, 1)),
          ]
      )
      def test_gt_dynamic_tensor(self, *args):
          class gt(nn.Module):
              def forward(self, lhs_val, rhs_val):
                  return torch.ops.aten.gt.Tensor(lhs_val, rhs_val)
  
          input_specs = [
              Input(
                  min_shape=args[1],
                  opt_shape=args[2],
                  max_shape=args[3],
              ),
              Input(
                  min_shape=args[4],
                  opt_shape=args[5],
                  max_shape=args[6],
              ),
          ]
  
          self.run_test_with_dynamic_shape(
              gt(),
              input_specs,
          )
  
  
  if __name__ == "__main__":
      run_tests()
  