class_docstr: Classy Dataset which produces random synthetic video clips.
  
  Useful for testing since the dataset is much faster to initialize and fetch samples
  from, compared to real world datasets.
  
  Note: Unlike :class:`SyntheticImageDataset`, this dataset generates targets
      randomly, independent of the video clips.
class_docstr_tok: ["Classy","Dataset","which","produces","random","synthetic","video","clips",".","Useful","for","testing","since","the","dataset","is","much","faster","to","initialize","and","fetch","samples","from",",","compared","to","real","world","datasets",".","Note",":","Unlike",":","class",":","`","SyntheticImageDataset","`",",","this","dataset","generates","targets","randomly",",","independent","of","the","video","clips","."]
code: def _get_sampler(self, epoch):
      world_size = get_world_size()
      rank = get_rank()
      sampler = DistributedSampler(
          self, num_replicas=world_size, rank=rank, shuffle=self.shuffle
      )
      sampler.set_epoch(epoch)
      return sampler
  
file_path: ClassyVision/classy_vision/dataset/classy_synthetic_video.py
imports: from typing import Any, Callable, Dict
  from classy_vision.generic.distributed_util import get_rank, get_world_size
  from torch.utils.data.distributed import DistributedSampler
  from .None import register_dataset
  from .classy_video_dataset import ClassyVideoDataset
  from .core import RandomVideoDataset
  from .transforms.util_video import build_video_field_transform_default
label_desc: Use image node_id 4 for calling the SyntheticVideoDataset obj's underlying member method code with example usage: obj._get_sampler(epoch) and returns: sampler