class_docstr: EXPERIMENTAL Given a prefix of t-many observations, condition a (t+f)-long
  distribution on the observations, converting it to an f-long distribution.
  
  :param dict data: A dict mapping site name to tensors of observations.
class_docstr_tok: ["EXPERIMENTAL","Given","a","prefix","of","t-many","observations",",","condition","a","(","t+f",")","-long","distribution","on","the","observations",",","converting","it","to","an","f-long","distribution",".",":","param","dict","data",":","A","dict","mapping","site","name","to","tensors","of","observations","."]
code: def _pyro_sample(self, msg):
      if msg["name"] not in self.data:
          return
  
      assert msg["value"] is None
      data = self.data[msg["name"]]
      msg["fn"] = prefix_condition(msg["fn"], data)
  
imports: import numbers
  from functools import singledispatch
  import torch
  from torch.distributions import transform_to, transforms
  import pyro.distributions
  from pyro.infer.reparam import DiscreteCosineReparam, HaarReparam
  from pyro.poutine.messenger import Messenger
  from pyro.poutine.util import site_is_subsample
  from pyro.primitives import get_param_store
label_desc: Use image node_id 2 for calling the PrefixConditionMessenger obj's underlying member method code with example usage: obj._pyro_sample(msg) without return types