code: def test_tensor_specs(self):
      # Test if the `input_specs` can be a tensor spec.
      class ClassWithTensorSpec:
          @property
          def input_spec1(self) -> TensorSpec:
              return TensorSpec("b, h", h=4, framework="torch")
  
          @check_input_specs(
              "input_spec1", cache=False, only_check_on_retry=False
          )
          def forward(self, input_data) -> Any:
              return input_data
  
      module = ClassWithTensorSpec()
      module.forward(torch.rand(2, 4))
      self.assertRaises(
          SpecCheckingError, lambda: module.forward(torch.rand(2, 3))
      )
  
imports: import abc
  import time
  import unittest
  from typing import Dict, Any, Type
  import numpy
  import torch
  from ray.rllib.core.models.specs.checker import SpecCheckingError
  from ray.rllib.core.models.specs.checker import convert_to_canonical_format, check_input_specs, check_output_specs
  from ray.rllib.core.models.specs.specs_base import TensorSpec, TypeSpec
  from ray.rllib.core.models.specs.specs_dict import SpecDict
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.nested_dict import NestedDict
label_desc: Use image node_id 7 for calling the TestCheckSpecs obj's underlying member method code with example usage: obj.test_tensor_specs() and returns: TensorSpec, input_data