"loc": ["header", "x-key"],
                    "msg": "Field required",
                    "input": None,
                },
            ]
        }
    ) | IsDict(
        # TODO: remove when deprecating Pydantic v1
        {
            "detail": [
                {
                    "loc": ["header", "x-token"],
                    "msg": "field required",

# Taken from Pydantic v1 as is
def isoformat(o: Union[datetime.date, datetime.time]) -> str:
    return o.isoformat()


# Taken from Pydantic v1 as is
# TODO: pv2 should this return strings instead?
def decimal_encoder(dec_value: Decimal) -> Union[int, float]:
    """
    Encodes a Decimal as int of there's no exponent, otherwise float

                    /*input_tensors*/ {},
                    /*expected_shape*/ make_shape({kUnknownDim, kUnknownDim}));

  TFE_DeleteOp(matmul_op);
  // TODO(bgogul): Add some death tests where status is not OK.
}

TEST_F(ShapeInferenceTest, InfersShapesFromInputTensors) {
  // Prepare some tensors for shape.
  TF_Tensor* tensor_1X6 = Int32Tensor({1, 6});

    * Tendrá el mejor rendimiento, ya que no tiene mucho código extra aparte del propio servidor.
    * No escribirías una aplicación directamente en Uvicorn. Eso significaría que tu código tendría que incluir más o menos, al menos, todo el código proporcionado por Starlette (o **FastAPI**). Y si hicieras eso, tu aplicación final tendría la misma sobrecarga que si hubieras usado un framework y minimizado el código de tu aplicación y los errores.

        IsDict(
            {
                "loc": ["query", "foo"],
                "msg": "Field required",
                "type": "missing",
                "input": None,
            }
        )
        # TODO: remove when deprecating Pydantic v1
        | IsDict(
            {
                "loc": ["query", "foo"],
                "msg": "field required",
                "type": "value_error.missing",
            }

        if (crawlingInfoPager.id != null) {
            cb.query().docMeta().setId_Equal(crawlingInfoPager.id);
        }
        // TODO Long, Integer, String supported only.
        if (StringUtil.isNotBlank(crawlingInfoPager.sessionId)) {
            cb.query().setSessionId_Match(crawlingInfoPager.sessionId);
        }

			// No rows were seen by MAX
			return FromNull(), nil
		}
		return e.aggregate.runningMax, nil

	case aggFnSum:
		// TODO: check if returning 0 when no rows were seen
		// by SUM is expected behavior.
		return e.aggregate.runningSum, nil

	default:
		// TODO:
	}

	return nil, errInvalidAggregation

    def get_model():
        return model

    client = TestClient(app)
    with client:
        response = client.get("/model")
    assert response.json() == {"dt_field": "2019-01-01T08:00:00+00:00"}


# TODO: remove when deprecating Pydantic v1
@needs_pydanticv1
def test_pydanticv1():
    class ModelWithDatetimeField(BaseModel):
        dt_field: datetime

        class Config:
            json_encoders = {

  TFE_DeleteContext(ctx);
  TF_DeleteStatus(status);

  // TODO(b/136478427): Figure out how to correctly shut the server down.
  worker_server1.release();
  worker_server2.release();
}

TEST(CAPI, DistributedFunctionNoError) {
  TestDistributedFunctionCancellation(false);
}

// TODO(b/170399182): Update test once an alternative to using the function
// optimization hook is in place.

                                        ],
                                    }
                                )
                                | IsDict(
                                    # TODO: remove when deprecating Pydantic v1
                                    {
                                        "title": "Callback Url",
                                        "maxLength": 2083,