mutex_lock session_lock(session->mu);
    const Graph& graph = session->graph->graph;

    const string& mutation_warning = session->graph->sessions[session];
    if (!mutation_warning.empty()) {
      // TODO(b/74949947): turn this back into an error status
      LOG(WARNING) << mutation_warning;
      session->graph->sessions[session].clear();
    }

    const auto num_nodes = graph.num_node_ids();

      negligent acts) or agreed to in writing, shall any Contributor be
      liable to You for damages, including any direct, indirect, special,
      incidental, or consequential damages of any character arising as a
      result of this License or out of the use or inability to use the
      Work (including but not limited to damages for loss of goodwill,
      work stoppage, computer failure or malfunction, or any and all

///
/// To ensure ABI and API compatibility, we have out-of-bounds data that is used
/// by both core TensorFlow and the plugin at load time. We don't include this
/// data in the structures here to prevent cases when padding/packing enabled by
/// different compiler options breaks compatibility. For more details about how
/// this is used, please consult next sections. Here we just wrap these tables

    if (tensorflow::CustomDeviceTensorHandle::classof(unwrapped_handle)) {
      // One of the inputs we're trying to pack is on a custom device. We'll let
      // the first custom device we see handle all of the packing.
      auto* custom_device_handle =
          tensorflow::down_cast<tensorflow::CustomDeviceTensorHandle*>(
              unwrapped_handle);
      tensorflow::ImmediateExecutionTensorHandle* result;

      attr_name, static_cast<const char*>(value), length);
  if (!s.ok()) {
    LOG(WARNING) << "Unable to set attribute: " << attr_name;
  }
}

void TFE_OpSetAttrInt(TFE_Op* op, const char* attr_name, int64_t value) {
  auto s = tensorflow::unwrap(op)->SetAttrInt(attr_name, value);
  if (!s.ok()) {
    LOG(WARNING) << "Unable to set attribute: " << attr_name;
  }
}

  // threadpool, so that we avoid the possibility of running the runner_ in the
  // threadpool of GPU event mgr, as that can trigger more callbacks to be
  // scheduled on that same threadpool, causing a deadlock in cases where the
  // caller of event_mgr->ThenExecute() blocks on the completion of the callback
  // (as in the case of ConstOp kernel creation on GPU, which involves copying a
  // CPU tensor to GPU).

// TF_Version returns a string describing version information of the
// TensorFlow library. TensorFlow uses semantic versioning.
TF_CAPI_EXPORT extern const char* TF_Version(void);

// Parsing a serialized TensorProto into a TF_Tensor.
TF_CAPI_EXPORT extern void TF_TensorFromProto(const TF_Buffer* from,
                                              TF_Tensor* to, TF_Status* status);

  // TODO(allenl): We should remove "TPU" from these op names at the very least,
  // or consider other ways of packing/unpacking parallel tensors.
  if (operation_name == std::string("TPUReplicatedInput")) {
    // Special-cased operation for packing per-device tensors into one parallel
    // tensor.
    if (inputs.size() != parallel_device.num_underlying_devices()) {
      std::string message(absl::StrCat(

  if (pr_match_groups.length != 2) {
    console.log(`PR Number not found in ${context.payload.head_commit.message}`);
    throw "Error extracting PR Number from commit message";
  }
  const pr_number = parseInt(pr_match_groups[1]);
  const owner = context.payload.repository.owner.name;
  const repo = context.payload.repository.name;
  console.log(`Original PR: ${pr_number} and Rollback Commit: ${rollback_commit}`);

  if (tmp_dir == "")
    return nullptr;
  else
    return &tmp_dir;
}

namespace tensorflow {
namespace {

// TODO(vnvo2409): Refactor `gcs_filesystem_test` to remove unnecessary tests
// after porting all tests from
// `//tensorflow/core/platform/cloud:gcs_file_system_test`.
class GCSFilesystemTest : public ::testing::Test {
 public:
  void SetUp() override {
    root_dir_ = io::JoinPath(
        *GetTmpDir(),