class_docstr: Degenerate discrete distribution (a single point).
  
  Discrete distribution that assigns probability one to the single element in
  its support. Delta distribution parameterized by a random choice should not
  be used with MCMC based inference, as doing so produces incorrect results.
  
  :param torch.Tensor v: The single support element.
  :param torch.Tensor log_density: An optional density for this Delta. This
      is useful to keep the class of :class:`Delta` distributions closed
      under differentiable transformation.
  :param int event_dim: Optional event dimension, defaults to zero.
code: def log_prob(self, x):
      v = self.v.expand(self.shape())
      log_prob = (x == v).type(x.dtype).log()
      log_prob = sum_rightmost(log_prob, self.event_dim)
      return log_prob + self.log_density
  
file_path: pyro/pyro/distributions/delta.py
imports: import numbers
  import torch
  from pyro.distributions.torch_distribution import TorchDistribution
  from pyro.distributions.util import sum_rightmost
  from .None import constraints
label_desc: Use image node_id 5 for calling the Delta obj's underlying member method code with example usage: obj.log_prob(x) and returns: unknown