ResultOf(IsSplat, IsFalse())));
}

TEST(ConvertTypeToTensorSpecProtoTest, UnrankedTensorType) {
  mlir::MLIRContext context;
  mlir::Builder b(&context);

  auto output_proto = ConvertTypeToTensorSpecProto(
      mlir::UnrankedTensorType::get(b.getF32Type()));
  TF_ASSERT_OK(output_proto.status());
  EXPECT_EQ(output_proto->dtype(), DT_FLOAT);
  EXPECT_TRUE(output_proto->shape().unknown_rank());
}

mlir::TF::ShapeAttr ConvertTypeToTensorShapeAttr(const mlir::Type& type);

// Converts an MLIR shaped type to a Tensorflow tensor spec proto.
absl::StatusOr<TensorSpecProto> ConvertTypeToTensorSpecProto(
    const mlir::Type& type);

// Converts a TensorFlow shape attribute to an MLIR shape attribute.
absl::StatusOr<mlir::Attribute> ConvertTensorShapeProto(

  // Empty TensorShape indicates a scalar.
  return mlir::TF::ShapeAttr::get(type.getContext(), ArrayRef<int64_t>());
}

absl::StatusOr<TensorSpecProto> ConvertTypeToTensorSpecProto(
    const mlir::Type& type) {
  DataType dtype;
  TF_RETURN_IF_ERROR(ConvertToDataType(type, &dtype));
  TensorSpecProto tensor_spec;
  tensor_spec.set_dtype(dtype);