class_docstr: This ensemble determines a weight vector to return a weighted mean of the underlying mixers.
  
  More specifically, each model is evaluated on the validation dataset and assigned an accuracy score (as per the fixed accuracy function at the JsonAI level).
  
  Afterwards, all the scores are softmaxed to obtain the final weights.
  
  Note: this ensemble only supports regression tasks.
code: def accuracies_to_weights(x: np.array) -> np.array:
      # Converts accuracies to weights using the softmax function.
      e_x = np.exp(x - np.max(x))
      return e_x / e_x.sum()
  
file_path: lightwood/lightwood/ensemble/weighted_mean_ensemble.py
imports: from typing import List, Optional
  import numpy
  import pandas
  from lightwood.helpers.log import log
  from type_infer.helpers import is_nan_numeric
  from lightwood.mixer.base import BaseMixer
  from lightwood.ensemble.base import BaseEnsemble
  from lightwood.api.types import PredictionArguments
  from lightwood.data.encoded_ds import EncodedDs
  from mindsdb_evaluator import evaluate_accuracies
  from type_infer.dtype import dtype
label_desc: Use image node_id 3 for calling the WeightedMeanEnsemble obj's underlying member method code with example usage: obj.accuracies_to_weights(x) and returns: unknown