}

// CanFloat reports whether [Value.Float] can be used without panicking.
func (v Value) CanFloat() bool {
	switch v.kind() {
	case Float32, Float64:
		return true
	default:
		return false
	}
}

// Float returns v's underlying value, as a float64.
// It panics if v's Kind is not [Float32] or [Float64]
func (v Value) Float() float64 {
	k := v.kind()
	switch k {
	case Float32:

		if globalExpiryState != nil {
			expPendingTasks.Value = float64(globalExpiryState.PendingTasks())
			expMissedTasks.Value = float64(globalExpiryState.stats.MissedTasks())
			expMissedFreeVersions.Value = float64(globalExpiryState.stats.MissedFreeVersTasks())
			expMissedTierJournalTasks.Value = float64(globalExpiryState.stats.MissedTierJournalTasks())
			expNumWorkers.Value = float64(globalExpiryState.stats.NumWorkers())
		}

				"unsortedInts": []interface{}{int64(2), int64(1)},
				"emptyInts":    []interface{}{},

				"doubles":         []interface{}{float64(1), float64(2), float64(2), float64(3)},
				"unsortedDoubles": []interface{}{float64(2), float64(1)},
				"emptyDoubles":    []interface{}{},

				"intBackedDoubles":          []interface{}{int64(1), int64(2), int64(2), int64(3)},

	// Fun with floating point.
	{math.NaN(), math.NaN(), false},
	{&[1]float64{math.NaN()}, &[1]float64{math.NaN()}, false},
	{&[1]float64{math.NaN()}, self{}, true},
	{[]float64{math.NaN()}, []float64{math.NaN()}, false},
	{[]float64{math.NaN()}, self{}, true},
	{map[float64]float64{math.NaN(): 1}, map[float64]float64{1: 2}, false},
	{map[float64]float64{math.NaN(): 1}, self{}, true},

	// Nil vs empty: not the same.

        bias = array_ops.constant(
            np.random.uniform(size=[shapes[1][-1]]), dtype=dtypes.float32
        )
    model = MatmulModel(bias)
    x = array_ops.constant(
        np.random.uniform(size=x_shape), dtype=dtypes.float32
    )
    y = array_ops.constant(
        np.random.uniform(size=y_shape), dtype=dtypes.float32
    )
    if use_kernel:
      model.matmul = model.matmul_with_kernel

    return op.emitOpError("requires min to be a 1d float tensor");

  auto max = GetRankedTensorTypeForOperand(op.getMax());
  if (max && !IsOfRankedFloatTensorType(max, 1))
    return op.emitOpError("requires max to be a 1d float tensor");

  Value inputs = op.getInputs();
  if (!HasRankAtLeast(inputs, 1))
    return op.emitError("requires inputs to be at least 1d float tensor");

  int64_t num_bits = op.getNumBits();

  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> {

(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)])