shape.add_dim()->set_size(dim);
  }
  mlir::MLIRContext context;
  mlir::Builder b(&context);
  auto status_or = ConvertToMlirTensorType(shape, dtype, &b);
  std::string buf;
  llvm::raw_string_ostream os(buf);
  status_or.value().print(os);
  return os.str();
}

TEST(MlirConvertType, ConvertToMlirTensorType) {
  // Simple case of static shapes.
  EXPECT_EQ("tensor<4x8x16xi32>",

                          llvm::SmallVectorImpl<int64_t>* shape);

// Given a tensor shape and dtype, get the corresponding MLIR tensor type.
absl::StatusOr<mlir::Type> ConvertToMlirTensorType(
    const TensorShapeProto& shape, DataType dtype, mlir::Builder* builder);

}  // namespace tensorflow

    }
    shape->push_back(d.size() == kTFDynamicSize ? ShapedType::kDynamic
                                                : d.size());
  }
  return absl::OkStatus();
}

absl::StatusOr<mlir::Type> ConvertToMlirTensorType(
    const TensorShapeProto& shape, DataType dtype, mlir::Builder* builder) {
  mlir::Type element_type;
  TF_RETURN_IF_ERROR(ConvertDataType(dtype, *builder, &element_type));
  if (shape.unknown_rank()) {

    if (output_shapes && !output_shapes->list().shape().empty()) {
      const auto& output_shape = output_shapes->list().shape(idx);
      const auto& element_type = element_types->list().type(idx);
      return ConvertToMlirTensorType(output_shape, element_type, &builder);
    }
  }

  auto type_from_array_attr = [&node, &idx, &builder](
                                  absl::string_view output_shape_attr,