const char* plugin_resource_name, void* plugin_resource,
    void (*delete_func)(void*), TF_Status* status);

// If the ResourceMgr provided by the `ctx` has a resource
// `plugin_resource_name`, returns it in `*result_plugin_resource`. Otherwise,
// invokes create_func to create the resource. `delete_func` is needed for
// ResourceMgr to clean up the resource. `status` will be set. If `status` is

            static_cast<TF_Code>(tensorflow::error::PERMISSION_DENIED));
  EXPECT_EQ(TF_UNAUTHENTICATED,
            static_cast<TF_Code>(tensorflow::error::UNAUTHENTICATED));
  EXPECT_EQ(TF_RESOURCE_EXHAUSTED,
            static_cast<TF_Code>(tensorflow::error::RESOURCE_EXHAUSTED));
  EXPECT_EQ(TF_FAILED_PRECONDITION,
            static_cast<TF_Code>(tensorflow::error::FAILED_PRECONDITION));

      container_name, plugin_resource_name, &tf_plugin_resource,
      [plugin_resource_name, create_func, create_func_args,
       delete_func](tensorflow::PluginResource** new_resource) {
        void* opaque_plugin_resource = create_func(create_func_args);
        *new_resource = new tensorflow::PluginResource(
            opaque_plugin_resource, plugin_resource_name, delete_func);

    *dst = tensorflow::Tensor(tensorflow::DT_RESOURCE, dst->shape());
    if (!dst->scalar<tensorflow::ResourceHandle>()().ParseFromString(
            string(static_cast<const char*>(tensor_interface->Data()),
                   tensor_interface->ByteSize()))) {
      return InvalidArgument(
          "Malformed TF_RESOURCE tensor: unable to parse resource handle");
    }
    return absl::OkStatus();
  }
  return absl::OkStatus();
}

                     TF_ShapeAndTypeList* input_tensors_as_shapes,
                     TF_ShapeAndTypeList** input_resource_shapes_and_types,
                     TF_ShapeAndTypeList** output_shapes,
                     TF_ShapeAndTypeList*** output_resource_shapes_and_types,
                     TF_Status* status) {
  using tensorflow::NodeDef;
  using tensorflow::OpRegistrationData;

        "//tensorflow/compiler/jit:variable_info",
        "//tensorflow/compiler/jit:variable_info_util",
        "//tensorflow/core:framework",
        "//tensorflow/core/common_runtime/next_pluggable_device:plugin_resource",
        "//tensorflow/core/platform:refcount",
        "//tensorflow/core/platform:status",
        "//tensorflow/core/tfrt/common:pjrt_util",
        "@com_google_absl//absl/status",

        "//tensorflow/core:test_main",
        "//tensorflow/core/kernels:array",
        "//tensorflow/core/kernels:control_flow_ops",
        "//tensorflow/core/kernels:math",
        "//tensorflow/core/platform:resource_loader",
    ],
)

tf_cc_test(
    name = "c_api_experimental_test",
    size = "medium",
    srcs = ["c_api_experimental_test.cc"],
    data = [
        "testdata/tf_record",

  CHECK(tensorflow::protobuf::TextFormat::ParseFromString(
      "    signature {"
      "      name: 'AddVariablesFunction'"
      "      input_arg {"
      "        name: 'var'"
      "        type: DT_RESOURCE"
      "      }"
      "      output_arg {"
      "        name: 'sum'"
      "        type: DT_FLOAT"
      "      }"
      "    }"
      "    node_def {"
      "      name: 'read0'"

        `tf.lookup.StaticHashTable.__init__` to create the table in anonymous
        mode. In this mode, the table resource can only be accessed via resource
        handles (not resource names) and will be deleted automatically when all
        resource handles pointing to it are gone.
*   `tf.data`:
    *   Introduce the `tf.data.experimental.at` API which provides random access

        // the tape, so no gradient needs to be computed from it unless this
        // target is also a source.
        auto source_tensor = sources_that_are_targets.find(id);
        if (source_tensor != sources_that_are_targets.end()) {
          Gradient* ones_like = nullptr;
          TF_RETURN_IF_ERROR(
              vspace.BuildOnesLike(source_tensor->second, &ones_like));
          (*result)[id].push_back(ones_like);
        }
      }