Permute<int64_t>(result_type.getShape(), kNchwToNhwcPermutation);

    const TensorType new_result_type =
        result_type.cloneWith(new_result_shape, result_type.getElementType());

    // Create a new `stablehlo.reduce_window` with all relevant attributes
    // permutated to match the new operand & result type.
    auto new_reduce_window_op =

                         QuantizationUnits& quantizable_ops) const {
    // Non-float tensors do not need quantization.
    auto type = mlir::dyn_cast<ShapedType>(op.getType());
    if (!type || !type.getElementType().isF32()) return false;

    Value value = op.getResult();

    // Check whether dynamic range quantization can be applied.
    for (auto& use : value.getUses()) {
      Operation* user = use.getOwner();

                }
            });
        }
    }

    protected void maybeAddDependentType(Set<String> types, Type type) {
        while (type.getSort() == Type.ARRAY) {
            type = type.getElementType();
        }
        if (type.getSort() != Type.OBJECT) {
            return;
        }
        String name = type.getClassName();
        if (typeFilter.test(name)) {

    if (cur_rank != 4 || cur_shape[2] == 1) return failure();
    TensorType new_shape = RankedTensorType::get(
        {cur_shape[0], cur_shape[1], 1, cur_shape[2] * cur_shape[3]},
        attr.getElementType());

    // Inserts a reshape op.
    auto shape_spec_type =
        RankedTensorType::get({cur_rank}, rewriter.getIntegerType(64));
    auto new_shape_const_attr =

                   permuted_shape.begin() + input_rank - 2);
    return rewriter.create<TFL::TransposeOp>(
        op->getLoc(),
        RankedTensorType::get(permuted_shape, input_type.getElementType()),
        input, permutation_tensor_op.getResult());
  };

  if (!can_convert_to_fully_connected(op)) {
    return false;
  }

  Value input_lhs = get_real_input_value(op.getX());

        if (auto attr = inst.getAttrOfType<mlir::ElementsAttr>(attr_id)) {
          mlir::Attribute rand_val;
          mlir::Type element_type = attr.getShapedType().getElementType();
          if (mlir::isa<mlir::IntegerType>(element_type)) {
            rand_val = mlir::IntegerAttr::get(element_type, std::rand());
          } else if (element_type.isF16() || element_type.isF32() ||

      elemType.isInteger(64))
    return true;

  // Complex<F<32>> is allowed.
  if (mlir::isa<ComplexType>(elemType) &&
      mlir::cast<ComplexType>(elemType).getElementType().isF32())
    return true;

  // QUINT8 and UI8 are allowed.
  if (mlir::isa<TF::Quint8Type>(elemType) ||
      (elemType.isInteger(8) && mlir::cast<IntegerType>(elemType).isUnsigned()))
    return true;

  for (int64_t i = 0; i < permutation_array.size(); ++i) {
    transposed_shape[i] = input_type.getDimSize(permutation_array[i]);
  }
  auto transposed_type =
      RankedTensorType::get(transposed_shape, input_type.getElementType());
  return transposed_type;
}

// Returns shape of a ranked tensor.
// Precondition: output_val's is ranked tensor.
// Returns a truncated shape when `truncate` is set to true.

  Type attribute_type = builder.getI64Type();
  return builder.create<TF::StridedSliceOp>(
      loc,
      RankedTensorType::get(
          {}, mlir::cast<ShapedType>(shape_value.getType()).getElementType()),
      /*input=*/shape_value,
      /*begin=*/Create1DConstValue<int32_t>(builder, loc, {dim}),
      /*end=*/Create1DConstValue<int32_t>(builder, loc, {dim + 1}),

        }
        return element;
    }

    Element<T> cachingElement(CollectionProviderInternal<T, ? extends Iterable<T>> provider) {
        final Element<T> element = new Element<>(provider.getElementType(), new ElementsFromCollectionProvider<>(provider), this::doAddRealized);
        if (provider instanceof ChangingValue) {