/// won't be registered anymore is if this function gets called by core
  /// TensorFlow and the `TF_Filesystem*` object is destroyed. However, due to
  /// registration being done in a static instance of `Env`, the destructor of
  /// `FileSystem` is never called (see
  /// https://github.com/tensorflow/tensorflow/issues/27535). In turn, this
  /// function will never be called. There are plans to refactor registration
  /// and fix this.

}

// Registers filesystem at `index`, if plugin is providing valid information.
//
// Extracted to a separate function so that pointers inside `info` are freed
// by the caller regardless of whether validation/registration failed or not.
//
// Must be called only with `index` a valid index in `info->ops`.
static Status ValidateAndRegisterFilesystems(
    const TF_FilesystemPluginInfo* info, int index) {

  // Consumes the internal state of the tape (so cannot be called more than
  // once unless the tape is persistent) and produces the gradient of the target
  // tensors with respect to the source tensors. The output gradients are used
  // if not empty and not null. The result is populated with one tensor per
  // target element.
  Status ComputeGradient(

// 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
// not OK, `*result_plugin_resource` will be set as nullptr.
//
// Caller does not take ownership of the `plugin_resource`.
TF_CAPI_EXPORT extern void TF_LookupOrCreatePluginResource(
    TF_OpKernelContext* ctx, const char* container_name,

    if (op_) {
      return errors::FailedPrecondition("Reset called on already built op.");
    }
    if (raw_device_name) {
      device_name_ = raw_device_name;
    }
    op_type_ = op;
    return absl::OkStatus();
  }
  Status SetOpName(const char* const op_name) override {
    if (op_) {
      return errors::FailedPrecondition(
          "SetOpName called on already built op.");
    }
    if (op_type_.empty()) {

      ImmediateExecutionTensorHandle* handle,
      const tensorflow::string& target_device_name,
      ImmediateExecutionTensorHandle** result) override {
    TF_Status status;
    handle->Ref();
    TFE_TensorHandle* result_handle = device_.copy_tensor_from_device(
        context_, tensorflow::wrap(handle), target_device_name.c_str(), &status,
        info_);
    handle->Unref();

  ///    placed in `out`.
  /// 4) `TF_OK` otherwise (i.e. the read succeeded, and at least one byte was
  /// placed
  ///    in `buffer`).
  ///
  /// Caller is responsible for allocating memory for `buffer`.
  /// `buffer` will be left unchanged in case of errors.
  int64_t Read(const std::string& filename, size_t offset, size_t n,
               char* buffer, TF_Status* status);

*   <IF RELEASE CONTAINS MULTIPLE FEATURES FROM SAME AREA, GROUP THEM TOGETHER>

*   Add `is_cpu_target_available`, which indicates whether or not TensorFlow was
    built with support for a given CPU target. This can be useful for skipping
    target-specific tests if a target is not supported.

*   `tf.data`
    * Support `data.experimental.distribued_save`. `distribued_save` uses
      tf.data service

              bool expect_failure = false) {
    DefineT(num_opers, opers, ToOutput(inputs), ToOutput(outputs), output_names,
            expect_failure);
  }

  // Caller must delete[] the returned value
  static const char** ToArray(const std::vector<string>& strs) {
    const char** ptr = nullptr;
    if (!strs.empty()) {
      ptr = new const char*[strs.size()];

    "@sobol_data//:__subpackages__",
    "@stablehlo//:__subpackages__",
    "@tf_runtime//:__subpackages__",
    "@bazel_tools//:__subpackages__",
    "@boringssl//:__subpackages__",
    "@com_github_cares_cares//:__subpackages__",
    "@com_github_googlecloudplatform_tensorflow_gcp_tools//:__subpackages__",
    "@com_github_grpc_grpc//:__subpackages__",
    "@com_google_absl//:__subpackages__",