virtual void ClearCachesAndThreadExecutors() = 0;

  // Initialize the step resource container for a training step. This is used
  // in current TF runtime. For tfrt, it is used by fallback op handler.
  virtual void StartStep() = 0;
  // Destroy the step resource container for a training step.
  virtual void EndStep() = 0;

  // Return the Eager Executor for current thread. Please note that Eager

        `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

        "//tensorflow/core/kernels:dataset_ops",
        "//tensorflow/core/platform:logging",
        "//tensorflow/core/platform:path",
        "//tensorflow/core/platform:stacktrace_handler",
        "//tensorflow/core/platform:statusor",
        "//tensorflow/core/platform:stringpiece",
        "//tensorflow/core/platform:types",
        "//tensorflow/core/profiler/internal:print_model_analysis",

  std::vector<TensorHandlePtr> vector_handles;
  vector_handles.reserve(2);
  vector_handles.push_back(std::move(two_vector));
  vector_handles.push_back(std::move(three_vector));
  std::unique_ptr<ParallelTensor> unknown_length_vector =
      ParallelTensor::FromTensorHandles(
          parallel_device, std::move(vector_handles), status.get());

        "notap",
    ],
    deps = [
        ":gcs_filesystem_impl",
        "//tensorflow/c:tf_status_helper",
        "//tensorflow/core/platform:path",
        "//tensorflow/core/platform:stacktrace_handler",
        "//tensorflow/core/platform:test",
        "@com_google_absl//absl/strings",
    ],
)

cc_library(
    name = "expiring_lru_cache",
    hdrs = ["expiring_lru_cache.h"],
    deps = [

w/core/tfrt/mlrt/kernel,tensorflow/core/tfrt/mlrt/bytecode,tensorflow/core/tfrt/mlrt/interpreter,tensorflow/compiler/mlir/tfrt/translate/mlrt,tensorflow/compiler/mlir/tfrt/translate/mlrt/testdata,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils,tensorflow/core/tfrt/utils/debug,tensorflow/c...

                      ", dtype=", DataType_Name(DataType()),
                      ", type=", FullType().DebugString(), ")");
}

Status AbstractTensorHandle::TensorHandleStatus() const {
  // Tensor handles in current runtime don't carry error info and this method
  // should always return OK status.
  return absl::OkStatus();
}

  if (TF_GetCode(status) != TF_OK) return;

  TFE_TensorHandle* result_handles[num_replicas];
  int num_retvals = num_replicas;
  TFE_Execute(op.get(), result_handles, &num_retvals, status);
  if (TF_GetCode(status) != TF_OK) return;
  for (int i = 0; i < num_replicas; ++i) {
    (*components)[i].reset(result_handles[i]);
  }
}

TensorHandlePtr Multiply(TFE_Context* context, TFE_TensorHandle* first,

  // Pack 3 variable handles into one TFE_TensorHandle.
  // When remote is false, function device is placed on task0. Handle types are
  // REMOTE, REMOTE, LOCAL on task0. When remote is true, function device is
  // placed on task1, Handle types are LOCAL, REMOTE, LOCAL on task1.
  int num_replicas = 3;
  std::vector<TFE_TensorHandle*> handles = {h0, h1, h2};
  TFE_TensorHandle* packed_handle =

fi

# Generate an overview page describing the build
if [[ "$TFCI_INDEX_HTML_ENABLE" == 1 ]]; then
  ./ci/official/utilities/generate_index_html.sh "$TFCI_OUTPUT_DIR/index.html"
fi

# Single handler for all cleanup actions, triggered on an EXIT trap.
# TODO(angerson) Making this use different scripts may be overkill.
cleanup() {
  if [[ "$TFCI_DOCKER_ENABLE" == 1 ]]; then
    ./ci/official/utilities/cleanup_docker.sh