def QuantizeByQuantizedType : NativeCodeCall<"quant::Quantize($0, $1.getValue())">;
def F32ElementsAttr : ElementsAttrBase<
  CPred<"$_self.cast<ElementsAttr>().getShapedType().getElementType().isF32()">, "float constant tensor">;

// Squash tfl.dequantize and tfl.quantize pairs.
// TODO(fengliuai): Compare the scale of input and output. This can also be
// squashed to a requantize op if the scales are different.

    if (!output_type || !output_type.getElementType().isF32()) return failure();

    auto input_type = mlir::dyn_cast<ShapedType>(input_dequant.getType());
    // TODO(renjieliu): support UniformQuantizedPerAxisType.
    auto q_type = mlir::dyn_cast_or_null<quant::UniformQuantizedType>(
        input_type.getElementType());
    if (!q_type) return failure();

  output_shape[3] = composite_result_shape[1];

  auto input_type = mlir::cast<ShapedType>(old_op->getOperand(0).getType());

  return RankedTensorType::get(output_shape, input_type.getElementType());
}
}  // namespace odml

  // doesn't require quantization.
  auto tensor_type = mlir::dyn_cast<TensorType>(value.getType());
  if (!tensor_type) {
    // There are none type values.
    return;
  }
  if (!tensor_type.getElementType().isF32()) return;

  // If the result is consumed by a quantize op, it has been quantized.
  if (value.hasOneUse() &&
      llvm::isa<TFL::QuantizeOp>(*value.getUsers().begin()))
    return;

    if (!splat_elements_attr) {
      return;
    }
    if (splat_elements_attr.getNumElements() == 1) {
      return;
    }
    auto element_type = splat_elements_attr.getType().getElementType();
    if (mlir::isa<ComplexType>(element_type) ||
        mlir::isa<quant::QuantizedType>(element_type)) {
      return;
    }
    op_builder->setInsertionPoint(const_op);

    SmallVector<int64_t, 4> new_shape(permutation.size());
    for (size_t i = 0; i < permutation.size(); ++i)
      new_shape[i] = shape[permutation[i]];

    return RankedTensorType::get(new_shape, ranked_type.getElementType());
  }

  return type;
}

bool AreCancellablePermutations(DenseIntElementsAttr perm0,
                                DenseIntElementsAttr perm1) {

}

ExpressedToQuantizedConverter ExpressedToQuantizedConverter::forInputType(
    Type input_type) {
  if (isa<TensorType, VectorType>(input_type)) {
    Type element_type = cast<ShapedType>(input_type).getElementType();
    if (!isQuantizablePrimitiveType(element_type))
      return ExpressedToQuantizedConverter{input_type, nullptr};
    return ExpressedToQuantizedConverter{input_type, element_type};
  }

// Returns true if it is a shaped type of f32 elements.
inline bool IsF32ShapedType(Type t) {
  if (auto shaped_type = mlir::dyn_cast_or_null<ShapedType>(t)) {
    return shaped_type.getElementType().isF32();
  }
  return false;
}

// Return true when the given element_type is QI8.
inline bool IsQI8Type(Type t) {
  auto quantized_type = quant::QuantizedType::getQuantizedElementType(t);

  auto original = mlir::dyn_cast<ShapedType>(original_ty);

  // Both types must be shaped types.
  if (!original || !updated) return false;

  // Element types must match.
  if (original.getElementType() != updated.getElementType()) return false;

  // If the updated type doesn't have a rank, then it can't be a more refined
  // type.
  if (!updated.hasRank()) return false;

import java.util.Collection;

public interface CollectionPropertyInternal<T, C extends Collection<T>> extends PropertyInternal<C>, HasMultipleValues<T>, CollectionProviderInternal<T, C> {
    @Override
    Class<T> getElementType();

    /**
     * Adds an element to the property value.
     *
     * <p>
     * When invoked on a property with no value, this method first sets the value