class_docstr: Wraps a distribution with a value mapper function.
  
  This is used to restrict the output values of a given distribution by a value mapper function.
  The value mapper function can be functions like sigmoid, tanh, etc.
  
  Args:
      dist: the target distribution.
      map_fn: the callable function to adjust the output from distributions.
  
  Example:
      >>> from torch.distributions import Normal
      >>> import torch.nn as nn
      >>> # without mapper
      >>> dist = DistributionWithMapper(Normal(0., 1.,), map_fn=None)
      >>> _ = torch.manual_seed(0)
      >>> dist.rsample((8,))
      tensor([ 1.5410, -0.2934, -2.1788,  0.5684, -1.0845, -1.3986,  0.4033,  0.8380])
      >>> # with sigmoid mapper
      >>> dist = DistributionWithMapper(Normal(0., 1.,), map_fn=nn.Sigmoid())
      >>> _ = torch.manual_seed(0)
      >>> dist.rsample((8,))
      tensor([0.8236, 0.4272, 0.1017, 0.6384, 0.2527, 0.1980, 0.5995, 0.6980])
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code: def __getattr__(self, attr: str) -> Any:
      try:
          return getattr(self, attr)
      except AttributeError:
          return getattr(self.dist, attr)
  
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 5 for calling the DistributionWithMapper obj's underlying member method code with example usage: obj.__getattr__(attr) and returns: getattr, getattr