because the tensors are loaded asynchronously.
  }];

  let arguments = (ins
    Arg<TF_StrTensor, [{Must have a single element.  The prefix of a V2 checkpoint.}]>:$prefix,
    Arg<TF_StrTensor, [{shape {N}.  The names of the tensors to be restored. This is tensor name in the model.}]>:$tensor_names,
    Arg<TF_StrTensor, [{shape {N}.  The slice specs of the tensors to be restored.

		po.addchild(i2, i1, true)
	}

	return true
}

// Checkpoint saves the current state of the DAG so that it's possible
// to later undo this state.
// Complexity is O(1).
func (po *poset) Checkpoint() {
	po.undo = append(po.undo, posetUndo{typ: undoCheckpoint})
}

// Undo restores the state of the poset to the previous checkpoint.
// Complexity depends on the type of operations that were performed

}

// CheckpointContainer tries to checkpoint a container. The parameters are used to
// look up the specified container. If the container specified by the given parameters
// cannot be found an error is returned. If the container is found the container
// engine will be asked to checkpoint the given container into the kubelet's default
// checkpoint directory.
func (kl *Kubelet) CheckpointContainer(

1.  Weight Transformation

    Weight Transformation is similar to Input Transform. Weight transform is
    needed to apply space to depth optimization for a model that needs to load a
    pre-train checkpoint. This transform can be done on the host or TPU device
    based on the cost. As the size of the kernel is relatively small, this won't
    add additional cost to TPU device time. Below is the logic to transform the

}

void TF_MakeInternalErrorStatus(TF_Status* status, const char* errMsg) {
  status->status = tensorflow::errors::Internal(errMsg);
}

struct TF_CheckpointReader : public tensorflow::checkpoint::CheckpointReader {
  using tensorflow::checkpoint::CheckpointReader::CheckpointReader;
  std::vector<std::string> variable_list;
};

TF_CheckpointReader* TF_NewCheckpointReader(const char* filename,

    saver_def: `saver_pb2.SaverDef` to create a `saver.Saver` from. The created
      saver will be used to save and load variables. This may be `None` if no
      variables exist in the graph.
    checkpoint_dir: Path to checkpoint file where variable values are saved.
    function_aliases: Function name -> function alias mapping.
    asset_file_defs: `AssetFileDef`s that associates the asset files and the

                                        /*container=*/"", shared_name);

    // Assign the ConstOp to each VarHandleOp. These will be used to save the
    // variable values to the checkpoint.
    auto const_op_copy =
        builder.create<TF::ConstOp>(const_op.getLoc(), const_op.getValue());

    builder.create<TF::AssignVariableOp>(const_op.getLoc(),

type SERVICE_STATUS struct {
	ServiceType             uint32
	CurrentState            uint32
	ControlsAccepted        uint32
	Win32ExitCode           uint32
	ServiceSpecificExitCode uint32
	CheckPoint              uint32
	WaitHint                uint32
}

const (
	SERVICE_RUNNING      = 4
	SERVICE_QUERY_STATUS = 4
)

your code interacts with the filesystem, network, etc. and uses checkpointed
variables as part of those interactions (ex: using a string variable to build a
filesystem path), a maliciously created checkpoint might be able to change the
targets of those operations, which could result in arbitrary
read/write/executions.

### Running a TensorFlow server

	28: "virtual Timer Expired",
	29: "profiling Timer Expired",
	30: "cpu Limit Exceeded",
	31: "file Size Limit Exceeded",
	32: "no runnable lwp",
	33: "inter-lwp signal",
	34: "checkpoint Freeze",
	35: "checkpoint Thaw",
	36: "thread Cancellation",
	37: "resource Lost",
	38: "resource Control Exceeded",
	39: "reserved for JVM 1",
	40: "reserved for JVM 2",