code: def collate(
      batch: List[torch.Tensor],
      *,
      collate_fn: Optional[_collate_fn_t],
      sample_empty_shapes: Sequence[Tuple],
      dtypes: Sequence[Union[torch.dtype, Type]],
  ):
      """
      Wraps `collate_fn` to handle empty batches.
  
      Default `collate_fn` implementations typically can't handle batches of length zero.
      Since this is a possible case for poisson sampling, we need to wrap the collate
      method, producing tensors with the correct shape and size (albeit the batch
      dimension being zero-size)
  
      Args:
          batch: List of tensort to be passed to collate_fn implementation
          collate_fn: Collame method to be wrapped
          sample_empty_shapes: Sample tensors with the expected shape
          dtypes: Expected dtypes
  
      Returns:
          Batch tensor(s)
      """
  
      if len(batch) > 0:
          return collate_fn(batch)
      else:
          return [
              torch.zeros(shape, dtype=dtype)
              for shape, dtype in zip(sample_empty_shapes, dtypes)
          ]
  
file_path: opacus/opacus/data_loader.py
imports: import logging
  from functools import partial
  from typing import Any, List, Optional, Sequence, Tuple, Type, Union
  import torch
  from opacus.utils.uniform_sampler import DistributedUniformWithReplacementSampler, UniformWithReplacementSampler
  from torch.utils.data import BatchSampler, DataLoader, Dataset, IterableDataset, Sampler
  from torch.utils.data._utils.collate import default_collate
  from torch.utils.data.dataloader import _collate_fn_t
label_desc: Use image node_id 1 for calling a global function with example usage: collate(batch) and returns: collate_fn, unknown