docstr: to be set from TorchForecastingModel before calling trainer.predict() and reset at self.on_predict_end()
docstr_tok: ["to","be","set","from","TorchForecastingModel","before","calling","trainer.predict","(",")","and","reset","at","self.on_predict_end","(",")"]
code: def set_predict_parameters(
      self,
      n: int,
      num_samples: int,
      roll_size: int,
      batch_size: int,
      n_jobs: int,
      predict_likelihood_parameters: bool,
  ) -> None:
      """to be set from TorchForecastingModel before calling trainer.predict() and reset at self.on_predict_end()"""
      self.pred_n = n
      self.pred_num_samples = num_samples
      self.pred_roll_size = roll_size
      self.pred_batch_size = batch_size
      self.pred_n_jobs = n_jobs
      self.predict_likelihood_parameters = predict_likelihood_parameters
  
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 7 for calling the PLForecastingModule obj's underlying member method code with example usage: obj.set_predict_parameters(n, num_samples, roll_size, batch_size, n_jobs, predict_likelihood_parameters) without return types