code: def test_unary(self):
      # Testing the Unary Implementation that uses metatensors
  
      def relu_inplace(x):
          return x.relu_()
  
      def log(x):
          return torch.log(x)
  
      functions = [relu_inplace, log]
  
      input_shapes = [
          ((2, 2),),  # Simple Case
          ((0, 2),),  # Size 0 Tensor
          ((),),  # zerodim
      ]
  
      input_dtypes = [
          (float32,),  # Simple Case
          (
              int64,
          ),  # Test how some unary ops implicitly convert to float
          (complex32,),  # Show we can handle complex vals as well
      ]
  
      for fn, in_shapes, in_dtypes in product(
          functions, input_shapes, input_dtypes
      ):
          self.assert_dtype_equal(fn, in_shapes, in_dtypes)
  
imports: from itertools import product
  from typing import Tuple
  from unittest.case import expectedFailure
  import torch
  from torch import complex32, float32, float64, int32, int64
  from torch.jit._passes import _property_propagation
  from torch.testing._internal.common_methods_invocations import SampleInput, sample_inputs_adaptive_avg_pool2d, sample_inputs_conv2d
  from torch.testing._internal.common_utils import set_default_dtype, first_sample
  from torch.testing._internal.jit_utils import JitTestCase
  from torch.testing._internal.jit_metaprogramming_utils import create_traced_fn
  from torch.testing._internal.common_device_type import ops, instantiate_device_type_tests
  from torch.testing._internal.common_methods_invocations import op_db
label_desc: Use image node_id 1 for calling the TestDtypeAnalysis obj's underlying member method code with example usage: obj.test_unary() and returns: x, torch