class_docstr: Generate random probabilities for a batch of inputs.
  
  Args:
      p: probability to generate an 1-d binary mask. Default value is 0.5.
  
  Returns:
      A dict of parameters to be passed for transformation.
          - probs (Tensor): element-wise probabilities with a shape of (B,).
  
  Note:
      The generated random numbers are not reproducible across different devices and dtypes. By default,
      the parameters will be generated on CPU in float32. This can be changed by calling
      ``self.set_rng_device_and_dtype(device="cuda", dtype=torch.float64)``.
class_docstr_tok: ["Generate","random","probabilities","for","a","batch","of","inputs",".","Args",":","p",":","probability","to","generate","an","1-d","binary","mask",".","Default","value","is","0.5",".","Returns",":","A","dict","of","parameters","to","be","passed","for","transformation",".","-","probs","(","Tensor",")",":","element-wise","probabilities","with","a","shape","of","(","B",",",")",".","Note",":","The","generated","random","numbers","are","not","reproducible","across","different","devices","and","dtypes",".","By","default",",","the","parameters","will","be","generated","on","CPU","in","float32",".","This","can","be","changed","by","calling","``","self.set_rng_device_and_dtype","(","device=","''","cuda","''",",","dtype=torch.float64",")","``","."]
code: def __repr__(self) -> str:
      repr = f"p={self.p}"
      return repr
  
file_path: kornia/kornia/augmentation/random_generator/_2d/probability.py
imports: from typing import Dict, Tuple
  import torch
  from torch.distributions import Bernoulli
  from kornia.augmentation.random_generator.base import RandomGeneratorBase
  from kornia.augmentation.utils import _adapted_sampling, _common_param_check
  from kornia.core import Tensor, tensor
label_desc: Use image node_id 2 for calling the ProbabilityGenerator obj's underlying member method code with example usage: obj.__repr__() and returns: repr