code: def prefix_condition(d, data):
      """
      EXPERIMENTAL Given a distribution ``d`` of shape ``batch_shape + (t+f, d)``
      and data ``x`` of shape ``batch_shape + (t, d)``, compute a conditional
      distribution of shape ``batch_shape + (f, d)``. Typically ``t`` is the
      number of training time steps, ``f`` is the number of forecast time steps,
      and ``d`` is the data dimension.
  
      :param d: a distribution with ``len(d.shape()) >= 2``
      :type d: ~pyro.distributions.Distribution
      :param data: data of dimension at least 2.
      :type data: ~torch.Tensor
      """
      try:
          return d.prefix_condition(data)
      except AttributeError as e:
          raise NotImplementedError(
              "prefix_condition() does not suport {}".format(type(d))
          ) from e
  
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 3 for calling a global function with example usage: prefix_condition(d, data) and returns: d