message StaticRangePtq {
  // Operand index -> QuantizedType mapping. Operands that are not specified
  // here will be quantized with best effort.
  map<int32, QuantizedType> input_quantized_types = 1;
}

message WeightOnlyPtq {
  // Operand index -> QuantizedType mapping. Operands that are not specified
  // here will be quantized with best effort.
  map<int32, QuantizedType> input_quantized_types = 1;
}

        op->getLoc(), *output_type, op.getInput());
    rewriter.replaceOpWithNewOp<mhlo::BitcastConvertOp>(
        op,
        output_type->clone(
            mlir::dyn_cast<quant::QuantizedType>(output_type->getElementType())
                .getStorageType()),
        result);

    return success();
  }
};

// UniformDequantizeOp takes TF quantized types as input which would have been

  // Matches all convolution quantizable unit family.
  spec.mutable_matcher()->mutable_function_name()->set_regex(
      "composite_conv.*");

  // Enable per-channel quantization for convolution weights.
  QuantizedType conv_weight_quantized_type{};

  // Assumes NHWC format, specifying the channel dimension (3) as the
  // quantized axis.
  conv_weight_quantized_type.mutable_dimension_specs()->set_dimension(3);

  ArrayRef<double> multiplier_array(multiplier_values.data(),
                                    multiplier_values.size());

  // Multiply the quantization parameters by the multiplier.
  QuantizedType new_qtype;
  auto element_type = mlir::cast<TensorType>(q_op.getType()).getElementType();
  if (auto uniform_type = llvm::dyn_cast<UniformQuantizedType>(element_type)) {
    if (multiplier_attr.isSplat()) {

    "].cast<IntegerAttr>().getInt())">;

// Use the tensor type information from $0 and convert min $1, max $2 and
// numBits $3 and narrowRange $4 to a QuantizedType.
def ConvertToQuantTypeFromAttrs : NativeCodeCall<
    "quant::GetQuantizedTypeAttr($_builder, $0.getType(), $1, $2, -1, $3, $4, /*is_signed=*/false)">;

// Converts an integer attribute $0 to 32-bit with builder.

    And<[
      SubstLeaves<"$_self", "getElementTypeOrSelf($_op.getOperand(" # j # "))",
        quant_QuantizedType.predicate>,
      CPred<"quant::QuantizedType::castToStorageType("
                "getElementTypeOrSelf($_op.getResult(" # i # "))) == "
            "quant::QuantizedType::castToStorageType("
                "getElementTypeOrSelf($_op.getOperand(" # j # ")))">]>]>]>;

def TFL_SameFirstOperandAndFirstResultElementType :

    TF_ASSIGN_OR_RETURN(value,
                        tfl::ConvertIntBuffer(shaped_type, buffer, truncate));
    TF_ASSIGN_OR_RETURN(
        mlir::quant::QuantizedType type,
        tfl::GetQuantizedType(tensor, builder, /*is_constant=*/true,
                              /*storage_type=*/value.getElementType()));
    shaped_type = shaped_type.clone(type);

        `saved_model.load` and `saved_model.main_op`, which will be replaced by
        `saved_model.main_op` in V2.
    *   Deprecate tf.QUANTIZED_DTYPES. The official new symbol is
        tf.dtypes.QUANTIZED_DTYPES.
    *   Update sklearn imports for deprecated packages.
    *   Deprecate `Variable.count_up_to` and `tf.count_up_to` in favor of
        `Dataset.range`.

0-6.  The min_range and max_range values are therefore 0.0 and 6.0.
Dequantize on quint8 will take each value, cast to float, and multiply
by 6 / 255.
Note that if quantizedtype is qint8, the operation will additionally add
each value by 128 prior to casting.

If the mode is 'MIN_FIRST', then this approach is used:

```c++
num_discrete_values = 1 << (# of bits in T)