class_docstr: A `torch.utils.data.BatchSampler` that skips the first `n` batches of another `torch.utils.data.BatchSampler`.
class_docstr_tok: ["A","`","torch.utils.data.BatchSampler","`","that","skips","the","first","`","n","`","batches","of","another","`","torch.utils.data.BatchSampler","`","."]
code: def __iter__(self):
      for index, samples in enumerate(self.batch_sampler):
          if index >= self.skip_batches:
              yield samples
  
imports: import math
  from contextlib import suppress
  from typing import Callable, List, Optional, Union
  import torch
  from torch.utils.data import BatchSampler, DataLoader, IterableDataset, RandomSampler
  from .logging import get_logger
  from .state import AcceleratorState, DistributedType, GradientState, is_tpu_available
  from .utils import RNGType, broadcast, broadcast_object_list, concatenate, find_batch_size, get_data_structure, initialize_tensors, is_torch_version, send_to_device, slice_tensors, synchronize_rng_states
label_desc: Use image node_id 2 for calling the SkipBatchSampler obj's underlying member method code with example usage: obj.__iter__() without return types