code: def is_batch_empty(
      batch: Union[torch.Tensor, Iterable[torch.Tensor]]
  ):
      if type(batch) is torch.Tensor:
          return batch.numel() == 0
      else:
          return batch[0].numel() == 0
  
imports: import io
  from typing import Callable, Dict, Iterable, List, Union
  import numpy
  import torch
  import torch.nn
  from opacus.grad_sample import wrap_model
  from opacus.utils.module_utils import trainable_parameters
  from opacus.utils.packed_sequences import compute_seq_lengths
  from torch.nn.utils.rnn import PackedSequence, pad_packed_sequence
label_desc: Use image node_id 2 for calling a global function with example usage: is_batch_empty(batch) and returns: unknown, unknown