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 __init__(self, batch_sampler, skip_batches=0):
      self.batch_sampler = batch_sampler
      self.skip_batches = skip_batches
  
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 1 to create a new SkipBatchSampler object from inherited base classes: BatchSampler with example: obj = SkipBatchSampler(batch_sampler, skip_batches)