class_docstr: This class contains all possible arguments that can be passed to a Lightwood predictor at inference time.
  On each predict call, all arguments included in a parameter dictionary will update the respective fields
  in the `PredictionArguments` instance that the predictor will have.
  
  :param predict_proba: triggers (where supported) predictions in raw probability output form. I.e. for classifiers,
  instead of returning only the predicted class, the output additionally includes the assigned probability for
  each class.   
  :param all_mixers: forces an ensemble to return predictions emitted by all its internal mixers.
  :param mixer_weights: a list with coefficients that are normalized into 0-1 bounded scores to mix the output of all mixers available to a compatible ensemble (e.g. [0.5, 0.5] for an ensemble with two mixers would yield the mean prediction). Can be used with WeightedMeanEnsemble, StackedEnsemble or TsStackedEnsemble.
  :param fixed_confidence: Used in the ICP analyzer module, specifies an `alpha` fixed confidence so that predictions, in average, are correct `alpha` percent of the time. For unsupervised anomaly detection, this also translates into the expected error rate. Bounded between 0.01 and 0.99 (respectively implies wider and tighter bounds, all other parameters being equal).
  :param anomaly_cooldown: Sets the minimum amount of timesteps between consecutive firings of the the anomaly detector.
  :param simple_ts_bounds: in forecasting contexts, enabling this parameter disables the usual conformal-based bounds (with Bonferroni correction) and resorts to a simpler way of scaling bounds through the horizon based on the uncertainty estimation for the first value in the forecast (see helpers.ts.add_tn_num_conf_bounds for the implementation).
  :param anomaly_cooldown: Sets the minimum amount of timesteps between consecutive firings of the the anomaly         detector.
  :param time_format: For time series predictors. If set to `infer`, predicted `order_by` timestamps will be formatted back to the original dataset's `order_by` format. Any other string value will be used as a formatting string, unless empty (''), which disables the feature (this is the default behavior).
  :param force_ts_infer: For time series predictors. If set to `true`, an additional row will be produced per each group in the input DF, corresponding to an out-of-sample forecast w.r.t. to the input timestamps.
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docstr: Creates a python dictionary from the ``PredictionArguments`` object
  
  :returns: A python dictionary
docstr_tok: ["Creates","a","python","dictionary","from","the","``","PredictionArguments","``","object",":","returns",":","A","python","dictionary"]
code: def to_dict(self, encode_json=False) -> Dict[str, Json]:
      """
      Creates a python dictionary from the ``PredictionArguments`` object
  
      :returns: A python dictionary
      """
      return _asdict(self, encode_json=encode_json)
  
imports: from typing import Dict, List, Optional, Union
  import sys
  from type_infer.dtype import dtype
  from dataclasses import dataclass
  from lightwood.helpers.log import log
  from dataclasses_json import dataclass_json
  from dataclasses_json.core import _asdict, Json
  import json
label_desc: Use image node_id 2 for calling the PredictionArguments obj's underlying member method code with example usage: obj.to_dict(encode_json) and returns: _asdict