code: def __init__(self, loader: "Loader"):
      super().__init__(loader)
  
      if self.distributed:
          self.sampler = DistributedSampler(
              self.indices,
              shuffle=False,
              seed=self.seed,
              drop_last=False,
          )
  
file_path: ffcv/ffcv/traversal_order/sequential.py
imports: from typing import Sequence, TYPE_CHECKING
  import numpy
  from torch.utils.data import DistributedSampler
  from .base import TraversalOrder
label_desc: Use image node_id 1 to create a new Sequential object from inherited base classes: TraversalOrder with example: obj = Sequential(loader)