if (!status->status.ok()) {
    return nullptr;
  }
  return tensor;
}

// Deleter for DLManagedTensor
void DLManagedTensorDeleter(DLManagedTensor* arg) {
  TfDlManagedTensorCtx* owner =
      static_cast<TfDlManagedTensorCtx*>(arg->manager_ctx);
  owner->reference.Unref();
  delete owner;
}

// Converts TF_DATAType to DLPack data type.

  if (op_id == -1) {
    // Do not delete watched tensors.
    return;
  }
  tensor_tape_.erase(tensor_op_it);
  auto op_it = op_tape_.find(op_id);
  CHECK(op_it != op_tape_.end());
  for (const auto& output : op_it->second.output_tensor_info) {
    if (tensor_usage_.find(output.GetID()) != tensor_usage_.end()) {
      // Found a usage for an output, so cannot delete the op.
      return;
    }
  }

      _ll.load_pluggable_device_library(_plugin_dir)

if _os.getenv("TF_PLUGGABLE_DEVICE_LIBRARY_PATH", ""):
  _ll.load_pluggable_device_library(
      _os.getenv("TF_PLUGGABLE_DEVICE_LIBRARY_PATH")
  )

# Delete modules that should be hidden from dir().
# Don't fail if these modules are not available.
# For e.g. this file will be originally placed under tensorflow/_api/v1 which

// GraphOperation wraps and populates a TF_OperationDescription.
class GraphOperation : public TracingOperation {
 public:
  explicit GraphOperation(TF_Graph* g) : TracingOperation(kGraph), g_(g) {}
  void Release() override { delete this; }
  Status Reset(const char* op, const char* raw_device_name) override {
    if (op_) {
      return errors::FailedPrecondition("Reset called on already built op.");
    }
    if (raw_device_name) {

namespace tensorflow {
namespace parallel_device {
namespace {

class OpDeleter {
 public:
  void operator()(TFE_Op* to_delete) const { TFE_DeleteOp(to_delete); }
};

using OpPtr = std::unique_ptr<TFE_Op, OpDeleter>;

class StatusDeleter {
 public:
  void operator()(TF_Status* to_delete) const { TF_DeleteStatus(to_delete); }
};

using StatusPtr = std::unique_ptr<TF_Status, StatusDeleter>;

class ExecutorDeleter {
 public:

      op_name, tape_tensors, input_ids, input_dtypes,
      [gradient_function]() -> GradientFunction* { return gradient_function; },
      [](GradientFunction* ptr) {
        if (ptr) {
          delete ptr;
        }
      });
}
bool Tape::ShouldRecord(
    absl::Span<const AbstractTensorHandle* const> tensors) const {
  std::vector<int64_t> tensor_ids(tensors.size());

    TFE_ContextOptions* options, TFE_ContextDevicePlacementPolicy policy) {
  options->device_placement_policy = policy;
}

void TFE_DeleteContextOptions(TFE_ContextOptions* options) { delete options; }

TFE_Context* TFE_NewContext(const TFE_ContextOptions* opts, TF_Status* status) {
  if (opts->use_tfrt) {
    status->status = tensorflow::errors::Unimplemented("TFRT is not supported");
    return nullptr;

# contents. About arguments to gsutil:
# "gsutil -m rsync" runs in parallel.
# "gsutil rsync -r" is recursive and makes directories work.
# "gsutil rsync -d" is "sync and delete files from destination if not present in source"

DOWNLOADS="$(mktemp -d)"
mkdir -p "$DOWNLOADS"
gsutil -m rsync -r "$TFCI_ARTIFACT_STAGING_GCS_URI" "$DOWNLOADS"
ls "$DOWNLOADS"

    *   Add a compiler flag to enable building a TFLite library that applies
        `XNNPACK` delegate automatically when the model has a `fp32` operation.
*   GPU
    *   Allow GPU acceleration starting with internal graph nodes
    *   Experimental support for quantized models with the Android GPU delegate
    *   Add GPU delegate whitelist.
    *   Rename GPU whitelist -> compatibility (list).

}

void TF_DeleteFunction(TF_Function* func) {
  if (func == nullptr) {
    return;
  }

  func->record->Unref();
  func->record = nullptr;
  delete func;