"'");
      TypeAttr type_attr = mlir::dyn_cast<TypeAttr>(attr);
      if (!type_attr)
        return InvalidArgument("Attribute '", output_arg.type_attr(),
                               "' required for output '", output_arg.name(),
                               "' isn't a type attribute");
      state_->types.push_back(UnrankedTensorType::get(type_attr.getValue()));
    } else if (!output_arg.type_list_attr().empty()) {

  DataType dtype;
  TF_RETURN_IF_ERROR(ConvertToDataType(arg_type.getElementType(), &dtype));
  AttrValue type_attr;
  type_attr.set_type(dtype);
  (*node_def->mutable_attr())["T"] = type_attr;

  AttrValue index_attr;
  index_attr.set_i(index);
  (*node_def->mutable_attr())["index"] = index_attr;

  if (auto device_attr =

  DataType dtype;
  TF_RETURN_IF_ERROR(ConvertToDataType(arg_type.getElementType(), &dtype));
  AttrValue type_attr;
  type_attr.set_type(dtype);
  (*node_def->mutable_attr())["T"] = type_attr;

  AttrValue index_attr;
  index_attr.set_i(index);
  (*node_def->mutable_attr())["index"] = index_attr;

  if (auto device_attr =

            std::string type = "DT_UINT8";

            if (arg.type() != DT_INVALID) {
              type = DataType_Name(arg.type());
            } else if (!arg.type_attr().empty()) {
              OpDef_AttrDef attr =
                  *FindAttr(arg.type_attr(), op_info.graph_op_def);
              if (attr.has_default_value() &&
                  attr.default_value().value_case() == AttrValue::kType) {

  }
  return string(name);
}

void InferArgAttributes(const OpDef::ArgDef& arg,
                        std::unordered_map<string, string>* inferred_attrs) {
  if (!arg.type_attr().empty()) {
    gtl::InsertIfNotPresent(inferred_attrs, arg.type_attr(), arg.name());
  } else if (!arg.type_list_attr().empty()) {
    gtl::InsertIfNotPresent(inferred_attrs, arg.type_list_attr(), arg.name());
  }
  if (!arg.number_attr().empty()) {

    if (matchPattern(cst_tensor_op.getArg(), m_Constant(&array))) {
      llvm::DenseSet<Type> all_types;
      for (auto it : array) {
        TypedAttr typed_attr = it.dyn_cast<TypedAttr>();
        if (!typed_attr) return failure();
        all_types.insert(typed_attr.getType());
      }
      if (all_types.size() != 1) return failure();
      ShapedType new_out_type = RankedTensorType::get(

  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) ||

      /*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 -

        input_specs.push_back(original_input_specs[i]);
      } else if (requantize.pos == RequantizeState::ON_OUTPUT) {
        input_specs.push_back(TypeAttr::get(requantize.params));
      } else {
        input_specs.push_back(TypeAttr::get(state.params));
      }
    }
    op->setAttr("input_specs", ArrayAttr::get(context, input_specs));

    llvm::SmallVector<Attribute, 4> output_specs;