class_docstr: Base class for generating random augmentation parameters.
class_docstr_tok: ["Base","class","for","generating","random","augmentation","parameters","."]
code: def forward(
      self, batch_shape: Tuple[int, ...], same_on_batch: bool = False
  ) -> Dict[str, Tensor]:
      raise NotImplementedError
  
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 6 for calling the RandomGeneratorBase obj's underlying member method code with example usage: obj.forward(batch_shape, same_on_batch) without return types