code: def _sample(self, sample_fn, sample_shape):
      interstitial_dims = tuple(self._interstitial_sizes.keys())
      interstitial_dims = tuple(
          i - self.event_dim for i in interstitial_dims
      )
      interstitial_sizes = tuple(self._interstitial_sizes.values())
      expanded_sizes = tuple(self._expanded_sizes.values())
      batch_shape = expanded_sizes + interstitial_sizes
      samples = sample_fn(sample_shape + batch_shape)
      interstitial_idx = len(sample_shape) + len(expanded_sizes)
      interstitial_sample_dims = tuple(
          range(
              interstitial_idx,
              interstitial_idx + len(interstitial_sizes),
          )
      )
      for dim1, dim2 in zip(
          interstitial_dims, interstitial_sample_dims
      ):
          samples = samples.transpose(dim1, dim2)
      return samples.reshape(
          sample_shape + self.batch_shape + self.event_shape
      )
  
imports: import warnings
  from collections import OrderedDict
  from typing import Callable
  import torch
  from torch.distributions.kl import kl_divergence, register_kl
  import pyro.distributions.torch
  from .None import constraints
  from .distribution import Distribution
  from .score_parts import ScoreParts
  from .util import broadcast_shape, scale_and_mask
label_desc: Use image node_id 7 for calling the ExpandedDistribution obj's underlying member method code with example usage: obj._sample(sample_fn, sample_shape) and returns: samples