class_docstr: Distribution is the abstract base class for probability distributions.
docstr: Generates a sample_shape shaped reparameterized sample or sample_shape
  shaped batch of reparameterized samples if the distribution parameters
  are batched.
docstr_tok: ["Generates","a","sample_shape","shaped","reparameterized","sample","or","sample_shape","shaped","batch","of","reparameterized","samples","if","the","distribution","parameters","are","batched","."]
code: def rsample(
      self, sample_shape: torch.Size = torch.Size()
  ) -> torch.Tensor:
      """
      Generates a sample_shape shaped reparameterized sample or sample_shape
      shaped batch of reparameterized samples if the distribution parameters
      are batched.
      """
      raise NotImplementedError
  
imports: import warnings
  from typing import Any, Dict, Optional, Tuple
  import torch
  from torch.distributions import constraints
  from torch.distributions.utils import lazy_property
  from torch.types import _size
label_desc: Use image node_id 13 for calling the Distribution obj's underlying member method code with example usage: obj.rsample(sample_shape) without return types