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.
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docstr: The constructor of SyntheticVideoDataset.
  
  Args:
      num_classes: Number of classes in the generated targets.
      split: Split of dataset to use
      batchsize_per_replica: batch size per model replica
      shuffle: Whether we should shuffle between epochs
      transform: Transform to be applied to each sample
      num_samples: Number of samples to return
      frames_per_clip: Number of frames in a video clip
      video_width: Width of the video clip
      video_height: Height of the video clip
      audio_samples: Audio sample rate
      clips_per_video: Number of clips per video
docstr_tok: ["The","constructor","of","SyntheticVideoDataset",".","Args",":","num_classes",":","Number","of","classes","in","the","generated","targets",".","split",":","Split","of","dataset","to","use","batchsize_per_replica",":","batch","size","per","model","replica","shuffle",":","Whether","we","should","shuffle","between","epochs","transform",":","Transform","to","be","applied","to","each","sample","num_samples",":","Number","of","samples","to","return","frames_per_clip",":","Number","of","frames","in","a","video","clip","video_width",":","Width","of","the","video","clip","video_height",":","Height","of","the","video","clip","audio_samples",":","Audio","sample","rate","clips_per_video",":","Number","of","clips","per","video"]
code: def __init__(
      self,
      num_classes: int,
      split: str,
      batchsize_per_replica: int,
      shuffle: bool,
      transform: Callable,
      num_samples: int,
      frames_per_clip: int,
      video_width: int,
      video_height: int,
      audio_samples: int,
      clips_per_video: int,
  ):
      """The constructor of SyntheticVideoDataset.
  
      Args:
          num_classes: Number of classes in the generated targets.
          split: Split of dataset to use
          batchsize_per_replica: batch size per model replica
          shuffle: Whether we should shuffle between epochs
          transform: Transform to be applied to each sample
          num_samples: Number of samples to return
          frames_per_clip: Number of frames in a video clip
          video_width: Width of the video clip
          video_height: Height of the video clip
          audio_samples: Audio sample rate
          clips_per_video: Number of clips per video
      """
      dataset = RandomVideoDataset(
          num_classes,
          split,
          num_samples,
          frames_per_clip,
          video_width,
          video_height,
          audio_samples,
          clips_per_video,
      )
      super().__init__(
          dataset,
          split,
          batchsize_per_replica,
          shuffle,
          transform,
          num_samples,
          clips_per_video,
      )
  
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 1 to create a new SyntheticVideoDataset object from inherited base classes: ClassyVideoDataset with example: obj = SyntheticVideoDataset(num_classes, split, batchsize_per_replica, shuffle, transform, num_samples, frames_per_clip, video_width, video_height, audio_samples, clips_per_video)