content: import torch
  import torch.nn as nn
  from torch.testing._internal.common_utils import run_tests
  from torch_tensorrt import Input
  
  from .harness import DispatchTestCase
  
  
  class TestLeakyReLUConverter(DispatchTestCase):
      def test_leaky_relu(self):
          class TestModule(nn.Module):
              def forward(self, x):
                  return torch.ops.aten.leaky_relu.default(x, 0.05)
  
          inputs = [torch.randn(1, 10)]
          self.run_test(TestModule(), inputs)
  
      def test_leaky_relu_with_dynamic_shape(self):
          class TestModule(nn.Module):
              def forward(self, x):
                  return torch.ops.aten.leaky_relu.default(x, 0.05)
  
          input_specs = [
              Input(
                  dtype=torch.float32,
                  min_shape=(1, 1, 1),
                  opt_shape=(1, 2, 3),
                  max_shape=(3, 3, 3),
              ),
          ]
          self.run_test_with_dynamic_shape(TestModule(), input_specs)
  
      def test_leaky_relu_with_dynamic_shape_four_dimensions(self):
          class TestModule(nn.Module):
              def forward(self, x):
                  return torch.ops.aten.leaky_relu.default(x, 0.05)
  
          input_specs = [
              Input(
                  dtype=torch.float32,
                  min_shape=(1, 1, 1, 5),
                  opt_shape=(1, 2, 3, 5),
                  max_shape=(3, 3, 3, 5),
              ),
          ]
  
          self.run_test_with_dynamic_shape(TestModule(), input_specs)
  
  
  if __name__ == "__main__":
      run_tests()
  