// These XLA flags are needed to trigger XLA properly from C (more generally
    // non-Python) clients. If this API is called again with `enable` set to
    // false, it is safe to keep these flag values as is.
    tensorflow::MarkForCompilationPassFlags* flags =
        tensorflow::GetMarkForCompilationPassFlags();
    flags->tf_xla_cpu_global_jit = true;
    flags->tf_xla_min_cluster_size = 1;
  } else {

			arg1="${f:h(pager.allPageCount)}"
			arg2="${f:h(pager.allRecordCount)}" />
	</div>
	<nav aria-label="...">
	</nav>
	<div class="col-sm-10">
		<ul class="pagination pagination-sm m-0 float-right">
			<c:if test="${pager.existPrePage}">
				<li class="page-item"><la:link
						styleClass="page-link"
						href="list/${pager.currentPageNumber - 1}">
					<la:message key="labels.prev_page" />

from fastapi import FastAPI
from fastapi.testclient import TestClient
from pydantic import BaseModel

app = FastAPI()


class Product(BaseModel):
    name: str
    description: str = None  # type: ignore
    price: float


@app.get("/product")
async def create_item(product: Product):
    return product


client = TestClient(app)


def test_get_with_body():

  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
      TF_NewStatus(), TF_DeleteStatus);
  TF_Tensor* t = TFE_TensorHandleResolve(handle, status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());
  std::unique_ptr<float[]> actual_values(new float[expected_values.size()]);
  EXPECT_EQ(sizeof(float) * expected_values.size(), TF_TensorByteSize(t));

			return
		}
		x.Type = VersionType(zb0002)
	}
	{
		var zb0003 uint8
		zb0003, bts, err = msgp.ReadUint8Bytes(bts)
		if err != nil {
			err = msgp.WrapError(err, "Flags")
			return
		}
		x.Flags = xlFlags(zb0003)
	}
	o = bts
	return
}

// unmarshalV unmarshals with a specific metadata version.
func (j *xlMetaV2Version) unmarshalV(v uint8, bts []byte) (o []byte, err error) {

                                     size_t length) = 0;
  virtual absl::Status SetAttrInt(const char* attr_name, int64_t value) = 0;
  virtual absl::Status SetAttrFloat(const char* attr_name, float value) = 0;
  virtual absl::Status SetAttrBool(const char* attr_name, bool value) = 0;
  virtual absl::Status SetAttrType(const char* attr_name, DataType value) = 0;

Estos **type hints** son una nueva sintaxis, desde Python 3.6+, que permite declarar el <abbr title="por ejemplo: str, int, float, bool">tipo</abbr> de una variable.

Usando las declaraciones de tipos para tus variables, los editores y otras herramientas pueden proveerte un soporte mejor.

    @Override
    public void setUp() throws Exception {
        super.setUp();
        queryCommand = new QueryCommand() {
            @Override
            public QueryBuilder execute(QueryContext context, Query query, float boost) {
                return null;
            }

            @Override
            protected String getQueryClassName() {
                return null;
            }
        };
    }

		Int64Field         int64
		UIntField          uint
		UInt8Field         uint8
		UInt16Field        uint16
		UInt32Field        uint32
		UInt64Field        uint64
		Float32Field       float32
		Float64Field       float64
		StringField        string
		TimeField          time.Time
		TimePtrField       *time.Time
		EmbeddedStruct     `gorm:"embedded"`
		*EmbeddedPtrStruct `gorm:"embedded"`
	}

  virtual void GetNameAttrList(
      tensorflow::NameAttrList* name_and_attrs) const = 0;

  virtual bool GetInt(absl::string_view, int64_t* result) const = 0;
  virtual bool GetFloat(absl::string_view attr_name, float* result) const = 0;
  virtual bool GetBool(absl::string_view attr_name, bool* result) const = 0;
  virtual bool GetType(absl::string_view attr_name, DataType* result) const = 0;
  virtual absl::Status GetTypeList(