TF_Graph* const graph = s->graph;
  if (graph != nullptr) {
    graph->mu.lock();
    graph->sessions.erase(s);
    const bool del = graph->delete_requested && graph->sessions.empty();
    graph->mu.unlock();
    if (del) delete graph;
  }
  delete s->session;
  delete s;
}

void TF_SessionRun(TF_Session* session, const TF_Buffer* run_options,

  tsl::Set_TF_Status_from_Status(status, result->counter->GetStatus());
  if (!result->counter->GetStatus().ok()) {
    delete result;
    return nullptr;
  }
  return result;
}

void TFE_MonitoringDeleteCounter0(TFE_MonitoringCounter0* counter) {
  delete counter;
}

TFE_MonitoringCounterCell* TFE_MonitoringGetCellCounter0(
    TFE_MonitoringCounter0* counter) {

// opaque pointer, since TensorFlow cannot parse it). `delete_func` is needed
// for ResourceMgr to clean up the resource. `status` will be set.
TF_CAPI_EXPORT extern void TF_CreatePluginResource(
    TF_OpKernelContext* ctx, const char* container_name,
    const char* plugin_resource_name, void* plugin_resource,
    void (*delete_func)(void*), TF_Status* status);

fi

# Repair wheels with auditwheel and delete the old one.
if [[ "$TFCI_WHL_AUDIT_ENABLE" == "1" ]]; then
  python3 -m auditwheel repair --plat "$TFCI_WHL_AUDIT_PLAT" --wheel-dir . *.whl
  # if the wheel is already named correctly, auditwheel won't rename it. so we
  # list all .whl files by their modification time (ls -t) and delete anything
  # other than the most recently-modified one (the new one).

    for (int j = 0; j < num_elem_numerical; j++) {
      ASSERT_NEAR(dnumerical[j], danalytical[j], abs_error);
    }
    TF_DeleteTensor(analytical_tensor);
    TF_DeleteTensor(numerical_tensor);
    delete[] danalytical;
    delete[] dnumerical;
    outputs[i]->Unref();
  }
}

void CheckTensorValue(AbstractTensorHandle* t, absl::Span<const float> manuals,
                      absl::Span<const int64_t> dims, double abs_error) {

}

void TF_DeleteShapeAndTypeList(TF_ShapeAndTypeList* shape_list) {
  if (shape_list == nullptr) return;
  for (size_t i = 0; i < shape_list->num_items; ++i) {
    delete[] shape_list->items[i].dims;
  }
  delete[] shape_list->items;
  delete shape_list;
}

void TF_DeleteShapeAndTypeListArray(TF_ShapeAndTypeList** shape_list_array,
                                    int num_items) {

      tf_posix_filesystem::NewReadOnlyMemoryRegionFromFile;
  ops->filesystem_ops->create_dir = tf_posix_filesystem::CreateDir;
  ops->filesystem_ops->delete_file = tf_posix_filesystem::DeleteFile;
  ops->filesystem_ops->delete_dir = tf_posix_filesystem::DeleteDir;
  ops->filesystem_ops->rename_file = tf_posix_filesystem::RenameFile;
  ops->filesystem_ops->copy_file = tf_posix_filesystem::CopyFile;

  void (*execute)(const TFE_Op* op, int* num_outputs,
                  TFE_TensorHandle** outputs, TF_Status* s, void* device_info);

  // Method to delete a device.
  void (*delete_device)(void* device_info);

  // Implements TFE_CreatePackedTensorHandle when one of `handles` is on this
  // custom device.
  //
  // Many devices will want to simply return an "unimplemented" status

    importlib.import_module("tf_keras.src.optimizers")
  else:
    importlib.import_module("keras.src.optimizers")
except (ImportError, AttributeError):
  pass

del importlib

# 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

void TF_DeleteAbstractTensor(TF_AbstractTensor* t) { unwrap(t)->Unref(); }

TF_OutputList* TF_NewOutputList() { return wrap(new OutputList); }
void TF_DeleteOutputList(TF_OutputList* o) { delete unwrap(o); }
void TF_OutputListSetNumOutputs(TF_OutputList* o, int num_outputs,
                                TF_Status* s) {
  unwrap(o)->expected_num_outputs = num_outputs;
  unwrap(o)->outputs.clear();