code: def _produce_predict_output(self, x: Tuple) -> torch.Tensor:
      if self.likelihood:
          output = self(x)
          if self.predict_likelihood_parameters:
              return self.likelihood.predict_likelihood_parameters(
                  output
              )
          else:
              return self.likelihood.sample(output)
      else:
          return self(x).squeeze(dim=-1)
  
imports: from abc import ABC, abstractmethod
  from functools import wraps
  from typing import Any, Dict, Optional, Sequence, Tuple, Union
  import pytorch_lightning
  import torch
  import torch.nn
  import torchmetrics
  from joblib import Parallel, delayed
  from darts.logging import get_logger, raise_if, raise_log
  from darts.models.components.layer_norm_variants import RINorm
  from darts.timeseries import TimeSeries
  from darts.utils.likelihood_models import Likelihood
  from darts.utils.timeseries_generation import _build_forecast_series
  from darts.utils.torch import MonteCarloDropout
label_desc: Use image node_id 17 for calling the PLForecastingModule obj's underlying member method code with example usage: obj._produce_predict_output(x) and returns: self, self, self