return errors::FailedPrecondition(
          "SetOpName called on already built op.");
    }
    if (op_type_.empty()) {
      return errors::FailedPrecondition(
          "GraphOperation::Reset must be called before calling SetOpName.");
    }
    // TODO(b/145674566): We use Graph::NewName to get a unique name here but
    // this may not be consistent with python's naming policy.
    mutex_lock l(g_->mu);

if not hasattr(_current_module, "__path__"):
  __path__ = [_tf_api_dir]
elif _tf_api_dir not in __path__:
  __path__.append(_tf_api_dir)

# Hook external TensorFlow modules.
# Import compat before trying to import summary from tensorboard, so that
# reexport_tf_summary can get compat from sys.modules. Only needed if using
# lazy loading.
_current_module.compat.v2  # pylint: disable=pointless-statement

    echo "# Size of $TF_WHEEL is $WHEEL_MEGABYTES / $LARGEST_OK_SIZE megabytes." >&3
    test "$WHEEL_MEGABYTES" -le "$LARGEST_OK_SIZE"
}

# Note: this runs before the tests further down the file, so TF is installed in
# the venv and the venv is active when those tests run. The venv gets cleaned
# up in teardown_file() above.
@test "Wheel is installable" {
    python3 -m venv /tf/venv

// directly, instead of requiring us to serialize to a GraphDef and
// call Session::Extend().
bool ExtendSessionGraphHelper(TF_Session* session, TF_Status* status) {
  if (session->graph != nullptr) {
    // Take the graph lock before the session lock to avoid deadlock. This is
    // safe since session->graph does not change.
    session->graph->mu.lock();
    mutex_lock session_lock(session->mu);
    const Graph& graph = session->graph->graph;

}

// The URI schemes that need to be tested are provided by the user via flags
// (or, if none is supplied, all existing schemes are used). As a scheme can
// become available after a shared object with a filesystem implementation is
// loaded, we can only check for availability after all arguments have been
// parsed.
//

                                                TF_Status* status);

// Infer OpInfo::TensorProperties for graph nodes inputs/outputs.
//
// Typical use case, is to infer tensor properties from a graph, before doing
// optimization pass. Nodes modified during optimization pass have to be
// invalidated, to prevent further incorrect optimizations based on wrong shape
// and data type properties.

/// Except where noted otherwise, all pointer arguments are owned by core
/// TensorFlow and are guaranteed to not be `nullptr`.
///
/// All path-like arguments are null terminated `char*` strings. Plugins can
/// assume that before any function using path arguments is invoked, the path is
/// made canonical by calling the function provided by `translate_name` or a
/// default implementation of that (supplied by core TensorFlow).
///

               char* buffer, TF_Status* status);

  // Validate the given file signature with the existing file signature in the
  // cache. Returns true if the signature doesn't change or the file doesn't
  // exist before. If the signature changes, update the existing signature with
  // the new one and remove the file from cache.
  bool ValidateAndUpdateFileSignature(const std::string& filename,

    TFE_TensorDebugInfo* debug_info, int dim_index);

// Description of the TensorFlow op to execute.
//
// Assumes that the provided 'ctx' outlives the returned TFE_Op, i.e.,
// TFE_DeleteOp() is called before TFE_DeleteContext().
//
// Very similar to TF_OperationDescription with some differences:
// (1) TF_Output or TFE_TensorHandle* as arguments to TF_AddInput,
//     TF_AddInputList

    bool is_async, bool enable_streaming_enqueue, int in_flight_nodes_limit);

// Deletes the eager Executor without waiting for enqueued nodes. Please call
// TFE_ExecutorWaitForAllPendingNodes before calling this API if you want to
// make sure all nodes are finished.
TF_CAPI_EXPORT extern void TFE_DeleteExecutor(TFE_Executor*);

// Returns true if the executor is in async mode.