parameters: (self, times)
return_statement: return array_ops.concat(values=member_observation_models, axis=2)
docstring: Computes a combined observation model based on member models.
  
      The effect is that predicted observations from each model are summed.
  
      Args:
        times: A [batch dimension] int32 Tensor with times for each part of the
            batch, on which member observation models can depend.
      Returns:
        A [batch dimension x num features x combined state dimension] Tensor with
        the combined observation model.
docstring_summary: Computes a combined observation model based on member models.
docstring_tokens: Computes a combined observation model based on member models .
function: def get_broadcasted_observation_model(self, times):
      """Computes a combined observation model based on member models.
  
      The effect is that predicted observations from each model are summed.
  
      Args:
        times: A [batch dimension] int32 Tensor with times for each part of the
            batch, on which member observation models can depend.
      Returns:
        A [batch dimension x num features x combined state dimension] Tensor with
        the combined observation model.
      """
      member_observation_models = [
          ops.convert_to_tensor(
              member.get_broadcasted_observation_model(times), dtype=self.dtype)
          for member in self._ensemble_members
      ]
      return array_ops.concat(values=member_observation_models, axis=2)
function_tokens: def get_broadcasted_observation_model ( self , times ) : member_observation_models = [ ops . convert_to_tensor ( member . get_broadcasted_observation_model ( times ) , dtype = self . dtype ) for member in self . _ensemble_members ] return array_ops . concat ( values = member_observation_models , axis = 2 )