class_docstr: Base class for generating random augmentation parameters.
class_docstr_tok: ["Base","class","for","generating","random","augmentation","parameters","."]
docstr: Change the random generation device and dtype.
  
  Note:
      The generated random numbers are not reproducible across different devices and dtypes.
docstr_tok: ["Change","the","random","generation","device","and","dtype",".","Note",":","The","generated","random","numbers","are","not","reproducible","across","different","devices","and","dtypes","."]
code: def set_rng_device_and_dtype(
      self,
      device: torch.device = torch.device("cpu"),
      dtype: torch.dtype = torch.float32,
  ) -> None:
      """Change the random generation device and dtype.
  
      Note:
          The generated random numbers are not reproducible across different devices and dtypes.
      """
      if self.device != device or self.dtype != dtype:
          self.make_samplers(device, dtype)
          self.device = device
          self.dtype = dtype
  
imports: from typing import Any, Callable, Dict, Optional, Tuple, Type, TypeVar
  import torch
  from torch.distributions import Distribution, Uniform
  from kornia.augmentation.utils.helpers import MultiprocessWrapper
  from kornia.core import Device, Module, Tensor
label_desc: Use image node_id 3 for calling the RandomGeneratorBase obj's underlying member method code with example usage: obj.set_rng_device_and_dtype(device, dtype) without return types