final float boost, final String field, final String text) {
        context.addFieldLog(field, text);
        context.addHighlightedQuery(text);
        return buildMatchPhraseQuery(field, text).boost(boost);
    }

    protected QueryBuilder convertKeywordQuery(final FessConfig fessConfig, final QueryContext context, final TermQuery termQuery,
            final float boost, final String field, final String text) {

  }

  public void testCheckNonNegative_zeroFloat() {
    MathPreconditions.checkNonNegative("float", 0f);
  }

  public void testCheckNonNegative_maxFloat() {
    MathPreconditions.checkNonNegative("float", Float.MAX_VALUE);
  }

  public void testCheckNonNegative_minFloat() {
    MathPreconditions.checkNonNegative("float", Float.MIN_VALUE);
  }

  public void testCheckNonNegative_positiveFloat() {

def get_path_param_gt(item_id: float = Path(gt=3)):
    return item_id


@app.get("/path/param-gt0/{item_id}")
def get_path_param_gt0(item_id: float = Path(gt=0)):
    return item_id


@app.get("/path/param-ge/{item_id}")
def get_path_param_ge(item_id: float = Path(ge=3)):
    return item_id


@app.get("/path/param-lt/{item_id}")
def get_path_param_lt(item_id: float = Path(lt=3)):
    return item_id

  }

  public void testCheckNonNegative_zeroFloat() {
    MathPreconditions.checkNonNegative("float", 0f);
  }

  public void testCheckNonNegative_maxFloat() {
    MathPreconditions.checkNonNegative("float", Float.MAX_VALUE);
  }

  public void testCheckNonNegative_minFloat() {
    MathPreconditions.checkNonNegative("float", Float.MIN_VALUE);
  }

  public void testCheckNonNegative_positiveFloat() {

TEST(CAPI, TestFromProto) {
  Tensor t_cc(DT_FLOAT, TensorShape({2, 3}));
  t_cc.flat<float>().setConstant(1.0);
  tensorflow::TensorProto t_proto;
  t_cc.AsProtoField(&t_proto);

  TF_Buffer* t_buffer = TF_NewBuffer();
  TF_CHECK_OK(MessageToBuffer(t_proto, t_buffer));

  const int num_bytes = 6 * sizeof(float);
  float* values =
      reinterpret_cast<float*>(tensorflow::cpu_allocator()->AllocateRaw(

   */
  public abstract ComparisonChain compare(long left, long right);

  /**
   * Compares two {@code float} values as specified by {@link Float#compare}, <i>if</i> the result
   * of this comparison chain has not already been determined.
   */
  public abstract ComparisonChain compare(float left, float right);

  /**
   * Compares two {@code double} values as specified by {@link Double#compare}, <i>if</i> the result

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):

        return l;
    }

    /* Encode floats
     */

    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

    ASSERT_EQ(num_elem_numerical, num_elem_analytical);

    float* dnumerical = new float[num_elem_numerical]{0};
    memcpy(&dnumerical[0], TF_TensorData(numerical_tensor),
           TF_TensorByteSize(numerical_tensor));
    float* danalytical = new float[num_elem_analytical]{0};
    memcpy(&danalytical[0], TF_TensorData(analytical_tensor),
           TF_TensorByteSize(analytical_tensor));

        display: block;
        float: left;
        width: 300px;
        height: 50px;
        padding: 2px;
        font-family: Verdana, sans-serif;
        font-size: 10pt;
        color: #000000;
        text-decoration: none;
    }
    a.plain {
        display: inline;
        float: none;
        width: auto;
        height: auto;
    }
    a.sort {
        display: block;
        float: left;
        width: 100px;