code: def to(self, device, non_blocking=False, copy=False):
      return BatchTuple(
          batch=self.batch.to(
              device=device,
              non_blocking=non_blocking,
              copy=copy,
          ),
          metadata=self.metadata,
      )
  
imports: import numpy
  from typing import NamedTuple, Mapping, Dict
  from enum import Enum
  from dataclasses import dataclass
  import torch
  import torch.utils.data.dataloader
  from jiant.utils.python.datastructures import ExtendedDataClassMixin, combine_dicts
label_desc: Use image node_id 1 for calling the BatchTuple obj's underlying member method code with example usage: obj.to(device, non_blocking, copy) and returns: BatchTuple