class_docstr: This implements RandAugment for video. Assume the input video tensor with shape
  (T, C, H, W).
  
  RandAugment: Practical automated data augmentation with a reduced search space
  (https://arxiv.org/abs/1909.13719)
docstr: This implements RandAugment for video.
  
  Args:
      magnitude (int): Magnitude used for transform function.
      num_layers (int): How many transform functions to apply for each
          augmentation.
      prob (float): The probablity of applying each transform function.
      transform_hparas (Optional[Dict[Any]]): Transform hyper parameters.
          Needs to have key fill. By default, it uses transform_default_hparas.
      sampling_type (str): Sampling method for magnitude of transform. It should
          be either gaussian or uniform.
      sampling_hparas (Optional[Dict[Any]]): Hyper parameters for sampling. If
          gaussian sampling is used, it needs to have key sampling_std. By
          default, it uses SAMPLING_RANDAUG_DEFAULT_HPARAS.
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code: def __init__(
      self,
      magnitude: int = 9,
      num_layers: int = 2,
      prob: float = 0.5,
      transform_hparas: Optional[Dict[str, Any]] = None,
      sampling_type: str = "gaussian",
      sampling_hparas: Optional[Dict[str, Any]] = None,
  ) -> None:
      """
      This implements RandAugment for video.
  
      Args:
          magnitude (int): Magnitude used for transform function.
          num_layers (int): How many transform functions to apply for each
              augmentation.
          prob (float): The probablity of applying each transform function.
          transform_hparas (Optional[Dict[Any]]): Transform hyper parameters.
              Needs to have key fill. By default, it uses transform_default_hparas.
          sampling_type (str): Sampling method for magnitude of transform. It should
              be either gaussian or uniform.
          sampling_hparas (Optional[Dict[Any]]): Hyper parameters for sampling. If
              gaussian sampling is used, it needs to have key sampling_std. By
              default, it uses SAMPLING_RANDAUG_DEFAULT_HPARAS.
      """
      assert sampling_type in ["gaussian", "uniform"]
      sampling_hparas = (
          sampling_hparas or SAMPLING_RANDAUG_DEFAULT_HPARAS
      )
      if sampling_type == "gaussian":
          assert "sampling_std" in sampling_hparas
  
      randaug_fn = [
          AugmentTransform(
              transform_name,
              magnitude,
              prob=prob,
              transform_max_paras=_TRANSFORM_RANDAUG_MAX_PARAMS,
              transform_hparas=transform_hparas,
              sampling_type=sampling_type,
              sampling_hparas=sampling_hparas,
          )
          for transform_name in list(
              _TRANSFORM_RANDAUG_MAX_PARAMS.keys()
          )
      ]
      self.randaug_fn = OpSampler(randaug_fn, num_sample_op=num_layers)
  
imports: from typing import Any, Dict, Optional
  import torch
  from pytorchvideo.transforms.augmentations import AugmentTransform
  from pytorchvideo.transforms.transforms import OpSampler
label_desc: Use image node_id 1 to create a new RandAugment object  with example: obj = RandAugment(magnitude, num_layers, prob, transform_hparas, sampling_type, sampling_hparas)