</p>

<pre>
uint(iota)               // iota value of type uint
float32(2.718281828)     // 2.718281828 of type float32
complex128(1)            // 1.0 + 0.0i of type complex128
float32(0.49999999)      // 0.5 of type float32
float64(-1e-1000)        // 0.0 of type float64
string('x')              // "x" of type string
string(0x266c)           // "♬" of type string

  std::vector<float> result_values;
  result_values.reserve(output_size);

  for (int i = 0; i < output_size; ++i) {
    // Dot product with Kahan/Neumaier summation to minimize numeric errors.
    float sum = has_bias ? *bias_values_it : 0.0f;
    float compensation = 0.0f;
    for (int j = 0; j < input_size; ++j) {
      const float addend = input_values_it[j] * weights_row_it[j];
      const float new_sum = sum + addend;

                 element_type)) {
    std::vector<float> mins = {static_cast<float>(qtype.getMin())};
    std::vector<float> maxs = {static_cast<float>(qtype.getMax())};
    q_params = tflite::CreateQuantizationParameters(
        builder_, builder_.CreateVector<float>(mins),
        builder_.CreateVector<float>(maxs));
  }
  return tflite::CreateTensor(

// CHECK-LABEL: dot_general_upstream_srq_wrong_contracting
// CHECK: stablehlo.dot_general
// CHECK-NOT: tfl.batch_matmul

// -----

// Tests static range quantized dot_general with float operands

// CHECK-LABEL: dot_general_upstream_srq_float_operands
func.func @dot_general_upstream_srq_float_operands(%arg0: tensor<1x2x3x4xf32>, %arg1: tensor<1x2x4x5xf32>) -> tensor<1x2x3x5xf32> {

	return j
}

type PrecisionItem struct {
	Name    string  `json:"name,omitempty"`
	Int32   int32   `json:"int32,omitempty"`
	Int64   int64   `json:"int64,omitempty"`
	Float32 float32 `json:"float32,omitempty"`
	Float64 float64 `json:"float64,omitempty"`
}

var (
	precisionItem             PrecisionItem
	precisionItemStructSchema = PatchMetaFromStruct{T: GetTagStructTypeOrDie(precisionItem)}
)

REGISTER_OP("UnaryTest")
    .Input("a: float")
    .Output("o: float")
    .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
      ::tensorflow::shape_inference::ShapeHandle o;
      TF_RETURN_IF_ERROR(c->Merge(c->UnknownShape(), c->input(0), &o));
      c->set_output(0, o);
      return absl::OkStatus();
    });
REGISTER_OP("BinaryTest")
    .Input("a: float")
    .Input("b: float")

(Trunc64to32 (Const64  [c])) => (Const32  [int32(c)])
(Cvt64Fto32F (Const64F [c])) => (Const32F [float32(c)])
(Cvt32Fto64F (Const32F [c])) => (Const64F [float64(c)])
(Cvt32to32F  (Const32  [c])) => (Const32F [float32(c)])
(Cvt32to64F  (Const32  [c])) => (Const64F [float64(c)])
(Cvt64to32F  (Const64  [c])) => (Const32F [float32(c)])
(Cvt64to64F  (Const64  [c])) => (Const64F [float64(c)])
(Cvt32Fto32  (Const32F [c])) => (Const32  [int32(c)])

		return nil, nil
	}
	return int64(n.Byte), nil
}

// NullFloat64 represents a float64 that may be null.
// NullFloat64 implements the [Scanner] interface so
// it can be used as a scan destination, similar to [NullString].
type NullFloat64 struct {
	Float64 float64
	Valid   bool // Valid is true if Float64 is not NULL
}

// Scan implements the [Scanner] interface.

l-stretch{-ms-flex-item-align:stretch!important;align-self:stretch!important}}.float-left{float:left!important}.float-right{float:right!important}.float-none{float:none!important}@media (min-width:576px){.float-sm-left{float:left!important}.float-sm-right{float:right!important}.float-sm-none{float:none!important}}@media (min-width:768px){.float-md-left{float:left!important}.float-md-right{float:right!important}.float-md-none{float:none!important}}@media (min-width:992px){.float-lg-left{float:lef...

}

// Converts an Attribute with a single value of float or integral type to an
// Attribute holding a single value of float type. If attr has no elements, the
// result is 0.0f.
TypedAttr ConvertSingleElementAttrToFloatAttr(Attribute attr) {
  const auto dense_fp_attr = mlir::dyn_cast_or_null<DenseFPElementsAttr>(attr);
  if (dense_fp_attr) {
    // Already float => return
    return dense_fp_attr;
  }