for (const OpDef::ArgDef& arg_def : op_def.input_arg()) {
    ArgSpec arg = ArgSpec::CreateInput(arg_def, input_args_.size());
    input_args_.push_back(arg);
    if (!arg_def.type_attr().empty()) {
      inferred_attrs.insert(arg_def.type_attr());
      if (!arg_def.number_attr().empty()) {
        inferred_attrs.insert(arg_def.number_attr());
      }
    } else if (!arg_def.type_list_attr().empty()) {

  return ArgType(arg_def, kOutput);
}

ArgType::ArgType(const OpDef::ArgDef& arg_def, Kind kind)
    : kind_(kind), data_type_(arg_def.type()) {
  if (!arg_def.type_attr().empty()) {
    type_attr_name_ = arg_def.type_attr();
  }
  if (!arg_def.type_list_attr().empty()) {
    type_attr_name_ = arg_def.type_list_attr();
  }

  is_list_ =

    for (auto type_and_name :
         llvm::zip(intermediate_types, kIntermediateNames)) {
      mlir::TypeAttr type_attr =
          mlir::TypeAttr::get(std::get<0>(type_and_name));
      auto named_attr =
          builder.getNamedAttr(std::get<1>(type_and_name), type_attr);
      op_state.addAttribute(named_attr.getName(), named_attr.getValue());
    }
  }
  return absl::OkStatus();
}

          }
          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());

  Status status;
  NodeDef def;
  def.set_op("Bitcast");
  def.set_device(DEVICE_CPU);

  AttrValue typeAttr;
  SetAttrValue(input_tensor->dtype(), &typeAttr);

  AttrValue outTypeAttr;
  SetAttrValue(out_type, &outTypeAttr);

  (*def.mutable_attr())["T"] = typeAttr;
  (*def.mutable_attr())["type"] = outTypeAttr;

  def.add_input(

}

/// 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,

  if (!rop) return failure();

  llvm::SmallVector<Type, 4> input_specs, out_specs;
  for (auto spec : rop.getInputSpecs()) {
    input_specs.push_back(spec.cast<TypeAttr>().getValue());
  }
  for (auto spec : rop.getOutputSpecs()) {
    out_specs.push_back(spec.cast<TypeAttr>().getValue());
  }

  auto in_spec = input_specs[0].dyn_cast<UniformQuantizedType>();
  // TODO(fengliuai): handles the PerAxis QuantizedType.

      /*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 -