llvm::SmallVector<Attribute, 2> shape_attrs;
  llvm::SmallVector<Attribute, 2> type_attrs;
  for (Type type : dataset_types) {
    shape_attrs.push_back(
        TF::ShapeAttr::get(builder.getContext(), mlir::cast<ShapedType>(type)));
    type_attrs.push_back(TypeAttr::get(getElementTypeOrSelf(type)));
  }

  auto anonymous_iterator = builder.create<AnonymousIteratorV3Op>(

          }
          attribute = TypeAttr::get(type);
        }
        Value attr_cst;
        // Wrap these special attributes as a special TFR constant, so the SSA
        // value has a valid type to be used as TFR function argument. These
        // attributes are not expected to be manipulated by the lowering passes.
        if (mlir::isa<TypeAttr>(attribute) || mlir::isa<ArrayAttr>(attribute) ||

#include "tensorflow/core/framework/tensor_shape.pb.h"
#include "tensorflow/core/platform/status.h"
#include "tsl/platform/statusor.h"

namespace mlir {
namespace TFL {

absl::StatusOr<TypedAttr> CreateTypedAttr(ShapedType shaped_type, int value) {
  Type element_type = shaped_type.getElementType();
  if (element_type.isF16()) {
    auto floatType = mlir::FloatType::getF16(element_type.getContext());

}

/// Returns whether the given `a` and `b` have broadcast-compatible
/// types.
bool IsBroadcastableElementsAttrs(mlir::TypedAttr a, mlir::TypedAttr b);
// Returns true if every dimension of the attribute is 1 except the last one.
bool IsDimensionsDegenerateExceptLastOne(mlir::TypedAttr val);
// Returns true if every element is 1 except the last one.
bool IsDimensionsDegenerateExceptLastOne(ArrayRef<int64_t> elements_shape);

  return !std::any_of(elements.begin(), elements.end(), [](Attribute e) {
    return mlir::cast<IntegerAttr>(e).getValue() != 1;
  });
}

bool IsBroadcastableElementsAttrs(mlir::TypedAttr a, mlir::TypedAttr b) {
  // This would return false if we had unranked tensors (where they should
  // probably be considered as broadcastable), but given we are working with
  // attributes here that shouldn't be an issue,

      /*asymmetric_quantize_inputs=*/mlir::BoolAttr(),
      /*input_to_input_intermediate=*/mlir::TypeAttr(),
      /*input_to_forget_intermediate=*/mlir::TypeAttr(),
      /*input_to_cell_intermediate=*/mlir::TypeAttr(),
      /*input_to_output_intermediate=*/mlir::TypeAttr(),
      /*effective_hidden_scale_intermediate=*/mlir::TypeAttr());

  // Cast the static shaped lstm result to FuncOp's signature -

  return srcScastOp.getArg();
}

/// The quantization specification should match the expressed type.
static bool isValidQuantizationSpec(Attribute quantSpec, Type expressed) {
  if (auto typeAttr = mlir::dyn_cast<TypeAttr>(quantSpec)) {
    Type spec = typeAttr.getValue();
    if (mlir::isa<TensorType, VectorType>(spec)) return false;

    // The spec should be either a quantized type which is compatible to the

  return srcScastOp.getArg();
}

/// The quantization specification should match the expressed type.
static bool isValidQuantizationSpec(Attribute quantSpec, Type expressed) {
  if (auto typeAttr = mlir::dyn_cast<TypeAttr>(quantSpec)) {
    Type spec = typeAttr.getValue();
    if (mlir::isa<TensorType, VectorType>(spec)) return false;

    // The spec should be either a quantized type which is compatible to the

    case AttrValue::kB:
      return builder->getBoolAttr(value.b());
    case AttrValue::kType: {
      mlir::Type type;
      TF_RETURN_IF_ERROR(ConvertDataType(value.type(), *builder, &type));
      return mlir::TypeAttr::get(type);
    }
    case AttrValue::kShape:
      return ConvertTensorShapeProto(value.shape(), builder->getContext());
    case AttrValue::kTensor:
      return ConvertTensorProto(value.tensor(), builder);

// before the op being constant folded. Since the constant
// folding logic will use a "arith.constant" op to replace the
// "tf.FakeQuantWithMinMaxVarsOp", the "quant.qcast" op is used to preserve
// the quantization parameters as a TypeAttr and "quant.dcast" op used to
// convert the output type to the next op. Here are the transformations:
//
// input   min cst       max cst              input
//  \       |             |                     |