content: import torch
  import torch.nn as nn
  import torch_tensorrt.fx.tracer.acc_tracer.acc_ops as acc_ops
  from torch.testing._internal.common_utils import run_tests
  from torch_tensorrt.fx.tools.common_fx2trt import AccTestCase, InputTensorSpec
  
  
  class TestLeakyReLUConverter(AccTestCase):
      def test_leaky_relu(self):
          class TestModule(nn.Module):
              def forward(self, x):
                  return nn.functional.leaky_relu(x, negative_slope=0.05)
  
          inputs = [torch.randn(1, 10)]
          self.run_test(TestModule(), inputs, expected_ops={acc_ops.leaky_relu})
  
      def test_leaky_relu_with_dynamic_shape(self):
          class TestModule(nn.Module):
              def forward(self, x):
                  return nn.functional.leaky_relu(x)
  
          input_specs = [
              InputTensorSpec(
                  shape=(-1, -1, -1),
                  dtype=torch.float32,
                  shape_ranges=[((1, 1, 1), (1, 2, 3), (3, 3, 3))],
              ),
          ]
          self.run_test_with_dynamic_shape(
              TestModule(), input_specs, expected_ops={acc_ops.leaky_relu}
          )
  
      def test_leaky_relu_with_dynamic_shape_four_dimensions(self):
          class TestModule(nn.Module):
              def forward(self, x):
                  return nn.functional.leaky_relu(x)
  
          input_specs = [
              InputTensorSpec(
                  shape=(-1, -1, -1, -1),
                  dtype=torch.float32,
                  shape_ranges=[((1, 1, 1, 1), (1, 2, 3, 3), (3, 3, 3, 5))],
              ),
          ]
  
          self.run_test_with_dynamic_shape(
              TestModule(), input_specs, expected_ops={acc_ops.leaky_relu}
          )
  
  
  if __name__ == "__main__":
      run_tests()
  