parameters: (self, shard_id, shard)
docstring: Probability per example in a class.
  
      Updates a matrix with dimension num_examples X num_classes.
  
      Args:
        shard_id: id of the current shard.
        shard: current data shard, 1 X num_examples X dimensions.
docstring_summary: Probability per example in a class.
docstring_tokens: Probability per example in a class .
function: def _define_log_prob_operation(self, shard_id, shard):
      """Probability per example in a class.
  
      Updates a matrix with dimension num_examples X num_classes.
  
      Args:
        shard_id: id of the current shard.
        shard: current data shard, 1 X num_examples X dimensions.
      """
      # TODO(xavigonzalvo): Use the pdf defined in
      # third_party/tensorflow/contrib/distributions/python/ops/gaussian.py
      if self._covariance_type == FULL_COVARIANCE:
        self._define_full_covariance_probs(shard_id, shard)
      elif self._covariance_type == DIAG_COVARIANCE:
        self._define_diag_covariance_probs(shard_id, shard)
      self._probs[shard_id] += math_ops.log(self._alpha)
function_tokens: def _define_log_prob_operation ( self , shard_id , shard ) : # TODO(xavigonzalvo): Use the pdf defined in # third_party/tensorflow/contrib/distributions/python/ops/gaussian.py if self . _covariance_type == FULL_COVARIANCE : self . _define_full_covariance_probs ( shard_id , shard ) elif self . _covariance_type == DIAG_COVARIANCE : self . _define_diag_covariance_probs ( shard_id , shard ) self . _probs [ shard_id ] += math_ops . log ( self . _alpha )