TFE_TensorHandle* input = TFE_OpGetFlatInput(original_op, j, s);
    if (TF_GetCode(s) != TF_OK) return;
    const char* input_device = TFE_TensorHandleDeviceName(input, s);
    if (TF_GetCode(s) != TF_OK) return;
    if (dev->device_name == input_device) {
      LoggedTensor* t = reinterpret_cast<LoggedTensor*>(
          TFE_TensorHandleDevicePointer(input, s));
      if (TF_GetCode(s) != TF_OK) return;

      new Function<@Nullable String, @Nullable Character>() {
        @Override
        public @Nullable Character apply(@Nullable String input) {
          return input == null ? null : input.charAt(0);
        }
      };

  @Override
  protected Table<String, Integer, Character> create(@Nullable Object... data) {
    Table<String, Integer, String> table = HashBasedTable.create();

  absl::Status AddInput(AbstractTensorHandle* input) override {
    GraphTensor* t = dyn_cast<GraphTensor>(input);
    if (!t) {
      return tensorflow::errors::InvalidArgument(
          "Unable to cast input to GraphTensor");
    }
    TF_AddInput(op_.get(), t->output_);
    return absl::OkStatus();
  }
  absl::Status AddInputList(
      absl::Span<AbstractTensorHandle* const> inputs) override {

    }
    for test_type, expect in types.items():
        app = FastAPI()

        @app.post("/", response_model=test_type)
        def post_endpoint(input: test_type):
            return input

        res = TestClient(app).post("/", json=expect)
        assert res.status_code == 200, res.json()

                                    <label for="input" class="col-sm-3 text-sm-right col-form-label"><la:message
                                            key="labels.dict_protwords_source"/></label>
                                    <div class="col-sm-9">
                                        <la:errors property="input"/>
                                        <la:text styleId="input" property="input"

  If var_name has been set as environment variable, use the preset value, else
  ask for user input. If no input is provided, the default is used.

  Args:
    environ_cp: copy of the os.environ.
    var_name: string for name of environment variable, e.g. "TF_NEED_CUDA".
    ask_for_var: string for how to ask for user input.
    var_default: default value string.

  Returns:
    string value for var_name
  """

            "detail": {
                "errors": [
                    {
                        "type": "list_type",
                        "loc": ["body"],
                        "msg": "Input should be a valid list",
                        "input": {"numbers": [1, 2, 3]},
                    }
                ],
                "body": '{"numbers": [1, 2, 3]}',
            }
        }
    ) | IsDict(

   * (key) bits 'affects' a strictly smaller set of output bits. Funneling is bad because it can
   * result in more-than-ideal collisions for a non-uniformly distributed key space. In practice,
   * most key spaces are ANYTHING BUT uniformly distributed. A bit(i) in the input is said to
   * 'affect' a bit(j) in the output if two inputs, identical but for bit(i), will differ at output

                                    <label for="input" class="col-sm-3 text-sm-right col-form-label"><la:message
                                            key="labels.dict_stemmeroverride_source"/></label>
                                    <div class="col-sm-9">
                                        <la:errors property="input"/>
                                        <la:text styleId="input" property="input" styleClass="form-control"/>

                    "loc": ["header", "x-token"],
                    "msg": "Field required",
                    "input": None,
                },
                {
                    "type": "missing",
                    "loc": ["header", "x-key"],
                    "msg": "Field required",
                    "input": None,
                },
            ]
        }
    ) | IsDict(