class_docstr: Apply a random transformation to the gamma of a tensor image.
  
  This implementation aligns PIL. Hence, the output is close to TorchVision.
  
  .. image:: _static/img/RandomGamma.png
  
  Args:
      p: probability of applying the transformation.
      gamma: the gamma factor to apply.
      gain: the gain factor to apply.
      same_on_batch: apply the same transformation across the batch.
      keepdim: whether to keep the output shape the same as input (True) or broadcast it
               to the batch form (False).
  Shape:
      - Input: :math:`(C, H, W)` or :math:`(B, C, H, W)`, Optional: :math:`(B, 3, 3)`
      - Output: :math:`(B, C, H, W)`
  
  .. note::
      This function internally uses :func:`kornia.enhance.adjust_gamma`
  
  Examples:
      >>> rng = torch.manual_seed(0)
      >>> inputs = torch.rand(1, 3, 3, 3)
      >>> aug = RandomGamma((0.5,2.),(1.5,1.5),p=1.)
      >>> aug(inputs)
      tensor([[[[1.0000, 1.0000, 0.3912],
                [0.4883, 0.7801, 1.0000],
                [1.0000, 1.0000, 0.9702]],
      <BLANKLINE>
               [[1.0000, 0.8368, 0.9048],
                [0.1824, 0.5597, 0.7609],
                [1.0000, 1.0000, 1.0000]],
      <BLANKLINE>
               [[0.5452, 0.7441, 1.0000],
                [1.0000, 0.8990, 1.0000],
                [0.9267, 1.0000, 1.0000]]]])
  
  To apply the exact augmenation again, you may take the advantage of the previous parameter state:
      >>> input = torch.rand(1, 3, 32, 32)
      >>> aug = RandomGamma((0.8,1.2), p=1.)
      >>> (aug(input) == aug(input, params=aug._params)).all()
      tensor(True)
code: def apply_transform(
      self,
      input: Tensor,
      params: Dict[str, Tensor],
      flags: Dict[str, Any],
      transform: Optional[Tensor] = None,
  ) -> Tensor:
      gamma_factor = params["gamma_factor"].to(input)
      gain_factor = params["gain_factor"].to(input)
      return adjust_gamma(input, gamma_factor, gain_factor)
  
file_path: kornia/kornia/augmentation/_2d/intensity/gamma.py
imports: from typing import Any, Dict, Optional, Tuple
  from kornia.augmentation import random_generator
  from kornia.augmentation._2d.intensity.base import IntensityAugmentationBase2D
  from kornia.core import Tensor
  from kornia.enhance.adjust import adjust_gamma
label_desc: Use image node_id 2 for calling the RandomGamma obj's underlying member method code with example usage: obj.apply_transform(input, params, flags, transform) and returns: adjust_gamma