class_docstr: Simple encoder that assigns a unique integer to every observed label.
  
  Allocates an `unknown` label by default to index 0.
  
  Labels must be exact matches between inference and training (e.g. no .lower() on strings is performed here).
class_docstr_tok: ["Simple","encoder","that","assigns","a","unique","integer","to","every","observed","label",".","Allocates","an","`","unknown","`","label","by","default","to","index","0",".","Labels","must","be","exact","matches","between","inference","and","training","(","e.g",".","no",".lower","(",")","on","strings","is","performed","here",")","."]
docstr: :param normalize: can be used to temporarily return unnormalized values
docstr_tok: [":","param","normalize",":","can","be","used","to","temporarily","return","unnormalized","values"]
code: def decode(
      self, encoded_values: torch.Tensor, normalize=True
  ) -> List[object]:
      """
      :param normalize: can be used to temporarily return unnormalized values
      """
      if normalize and self.normalize:
          encoded_values *= self.n_labels
      values = (
          encoded_values.long().squeeze().tolist()
      )  # long() as inv_label_map expects an int key
      values = [
          self.inv_label_map.get(v, _UNCOMMON_WORD) for v in values
      ]
      return values
  
file_path: lightwood/lightwood/encoder/categorical/simple_label.py
imports: from typing import List, Union
  from collections import defaultdict
  import pandas
  import numpy
  import torch
  from lightwood.encoder.base import BaseEncoder
  from lightwood.helpers.constants import _UNCOMMON_WORD
label_desc: Use image node_id 4 for calling the SimpleLabelEncoder obj's underlying member method code with example usage: obj.decode(encoded_values, normalize) and returns: values