auto complex_value = std::complex<float>(static_cast<float>(value), 0.0f);
      content.assign(reinterpret_cast<const char*>(&complex_value),
                     sizeof(complex_value));
      repr.set_tensor_content(content);
      std::string mangled = tensorflow::mangling_util::MangleTensor(repr);

      return mlir::TF::TensorProtoAttr::get(shaped_type, mangled);
    } else {

      ret.add_string_val(str.str, str.len);
    }
    return ret;
  }
  std::string content;
  content.assign(reinterpret_cast<const char*>(buffer.data()), buffer.size());
  ret.set_tensor_content(content);
  return ret;
}

}  // namespace TFL

using mlir::RankedTensorType;
using mlir::ShapedType;
using mlir::Type;
using tensorflow::errors::InvalidArgument;

static TensorProto ConvertToProto(const Tensor& input_tensor,
                                  bool use_tensor_content = true) {
  TensorProto tensor_proto;
  // Using tensor content (mostly*) reduces serialization overhead during RPC
  // calls, but is less human reader friendly. People reading protobufs are less