class_docstr: Apply random transformation to Grayscale according to a probability p value.
  
  .. image:: _static/img/RandomGrayscale.png
  
  Args:
      rgb_weights: Weights that will be applied on each channel (RGB).
          The sum of the weights should add up to one.
      p: probability of the image to be transformed to grayscale.
      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.color.rgb_to_grayscale`.
  
  Examples:
      >>> rng = torch.manual_seed(0)
      >>> inputs = torch.randn((1, 3, 3, 3))
      >>> aug = RandomGrayscale(p=1.0)
      >>> aug(inputs)
      tensor([[[[-1.1344, -0.1330,  0.1517],
                [-0.0791,  0.6711, -0.1413],
                [-0.1717, -0.9023,  0.0819]],
      <BLANKLINE>
               [[-1.1344, -0.1330,  0.1517],
                [-0.0791,  0.6711, -0.1413],
                [-0.1717, -0.9023,  0.0819]],
      <BLANKLINE>
               [[-1.1344, -0.1330,  0.1517],
                [-0.0791,  0.6711, -0.1413],
                [-0.1717, -0.9023,  0.0819]]]])
  
  To apply the exact augmenation again, you may take the advantage of the previous parameter state:
      >>> input = torch.randn(1, 3, 32, 32)
      >>> aug = RandomGrayscale(p=1.0)
      >>> (aug(input) == aug(input, params=aug._params)).all()
      tensor(True)
code: def __init__(
      self,
      rgb_weights: Optional[Tensor] = None,
      same_on_batch: bool = False,
      p: float = 0.1,
      keepdim: bool = False,
  ) -> None:
      super().__init__(
          p=p, same_on_batch=same_on_batch, keepdim=keepdim
      )
      self.rgb_weights = rgb_weights
  
file_path: kornia/kornia/augmentation/_2d/intensity/grayscale.py
imports: from typing import Any, Dict, Optional
  import torch
  from torch import Tensor
  from kornia.augmentation._2d.intensity.base import IntensityAugmentationBase2D
  from kornia.color import rgb_to_grayscale
label_desc: Use image node_id 1 to create a new RandomGrayscale object from inherited base classes: IntensityAugmentationBase2D with example: obj = RandomGrayscale(rgb_weights, same_on_batch, p, keepdim)