GetDims(theta_tensor, theta_dims.data());

  // Initialize auxilary data structures.
  vector<float> thetaPlus_data(num_elems);
  vector<float> thetaMinus_data(num_elems);
  AbstractTensorHandle* f_outputs[1];

  // Numerical Grad Check
  for (int i = 0; i < num_elems; i++) {
    // Get relative epsilon value
    float epsilon = theta_data[i] == 0 ? 1e-4 : std::abs(theta_data[i] * 1e-4);
    AbstractTensorHandlePtr two_eps;

def test_route_converters_float():
    # Test float conversion
    response = client.get("/float/25.5")
    assert response.status_code == 200, response.text
    assert response.json() == {"float": 25.5}
    assert app.url_path_for("float_convertor", param=25.5) == "/float/25.5"  # type: ignore


def test_route_converters_path():
    # Test path conversion

pkg math/big, method (*Float) Mode() RoundingMode
pkg math/big, method (*Float) Mul(*Float, *Float) *Float
pkg math/big, method (*Float) Neg(*Float) *Float
pkg math/big, method (*Float) Parse(string, int) (*Float, int, error)
pkg math/big, method (*Float) Prec() uint
pkg math/big, method (*Float) Quo(*Float, *Float) *Float
pkg math/big, method (*Float) Rat(*Rat) (*Rat, Accuracy)
pkg math/big, method (*Float) Set(*Float) *Float

    }

    public void setBoost_Equal(Float boost) {
        setBoost_Term(boost, null);
    }

    public void setBoost_Equal(Float boost, ConditionOptionCall<TermQueryBuilder> opLambda) {
        setBoost_Term(boost, opLambda);
    }

    public void setBoost_Term(Float boost) {
        setBoost_Term(boost, null);
    }

    public void setBoost_Term(Float boost, ConditionOptionCall<TermQueryBuilder> opLambda) {

            """
        ),
    ] = None,
    gt: Annotated[
        Optional[float],
        Doc(
            """
            Greater than. If set, value must be greater than this. Only applicable to
            numbers.
            """
        ),
    ] = None,
    ge: Annotated[
        Optional[float],
        Doc(
            """

from typing_extensions import Annotated

app = FastAPI()


@app.post("/")
async def get(
    x: Annotated[float, Query(allow_inf_nan=True)] = 0,
    y: Annotated[float, Query(allow_inf_nan=False)] = 0,
    z: Annotated[float, Query()] = 0,
    b: Annotated[float, Body(allow_inf_nan=False)] = 0,
) -> str:
    return "OK"


client = TestClient(app)


@pytest.mark.parametrize(

TEST_P(GradientCheckerTest, TestMatMul) {
  float A_vals[] = {1.0f, 2.0f, 3.0f, 4.0f};
  int64_t A_dims[] = {2, 2};
  AbstractTensorHandlePtr A;
  {
    AbstractTensorHandle* A_raw;
    absl::Status s = TestTensorHandleWithDims<float, TF_FLOAT>(
        ctx_.get(), A_vals, A_dims, 2, &A_raw);
    ASSERT_EQ(errors::OK, s.code()) << s.message();
    A.reset(A_raw);
  }
  float B_vals[] = {.5f, -1.0f, 1.0f, 1.0f};

TFE_TensorHandle* TestMatrixTensorHandleWithInput(TFE_Context* ctx,
                                                  float data[], int64_t dims[],
                                                  int num_dims);

// Get a Matrix TensorHandle with given float values and dimensions
TFE_TensorHandle* TestTensorHandleWithDimsFloat(TFE_Context* ctx, float data[],
                                                int64_t dims[], int num_dims);

     */

    public static int enc_floatle ( float f, byte[] dst, int di ) {
        return enc_uint32le(Float.floatToIntBits(f), dst, di);
    }


    public static int enc_floatbe ( float f, byte[] dst, int di ) {
        return enc_uint32be(Float.floatToIntBits(f), dst, di);
    }


    /*
     * Decode floating point numbers
     */

    public static float dec_floatle ( byte[] src, int si ) {

class Item(BaseModel):
    name: str
    description: Union[str, None] = None
    price: float
    tax: Union[float, None] = None
    tags: Set[str] = set()
    images: Union[List[Image], None] = None


class Offer(BaseModel):
    name: str
    description: Union[str, None] = None
    price: float
    items: List[Item]


@app.post("/offers/")
async def create_offer(offer: Offer):