class_docstr: Subclass of a PyTorch `DataLoader` that will skip the first batches.
  
  Args:
      dataset (`torch.utils.data.dataset.Dataset`):
          The dataset to use to build this datalaoder.
      skip_batches (`int`, *optional*, defaults to 0):
          The number of batches to skip at the beginning.
      kwargs:
          All other keyword arguments to pass to the regular `DataLoader` initialization.
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code: def __init__(self, dataset, skip_batches=0, **kwargs):
      super().__init__(dataset, **kwargs)
      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 SkipDataLoader object from inherited base classes: DataLoader with example: obj = SkipDataLoader(dataset, skip_batches)