code: def indiv_filter(a0, a, e, x):
      alph = torch.zeros((x.shape[0], a0.shape[0]))
      for j in range(a0.shape[0]):
          vec = a0[j]
          if torch.sum(x[0, :]) > 0.5:
              vec = vec * torch.dot(x[0, :], e[j, :])
          alph[0, j] = vec
      alph[0, :] = alph[0, :] / torch.sum(alph[0, :])
      for t in range(1, x.shape[0]):
          for j in range(a0.shape[0]):
              vec = torch.sum(alph[t - 1, :] * a[:, j])
              if torch.sum(x[t, :]) > 0.5:
                  vec = vec * torch.dot(x[t, :], e[j, :])
              alph[t, j] = vec
          alph[t, :] = alph[t, :] / torch.sum(alph[t, :])
      return torch.log(alph)
  
file_path: pyro/tests/contrib/mue/test_missingdatahmm.py
imports: import pytest
  import torch
  from pyro.contrib.mue.missingdatahmm import MissingDataDiscreteHMM
  from pyro.distributions import Categorical, DiscreteHMM
label_desc: Use image node_id 5 for calling a global function with example usage: indiv_filter(a0, a, e, x) and returns: torch