class_docstr: Toy example: we want to make predictions on inputs of the form {"inputs": ["hello world", "foo", "bar"]}
class_docstr_tok: ["Toy","example",":","we","want","to","make","predictions","on","inputs","of","the","form","{","``","inputs","''",":","[","``","hello","world","''",",","``","foo","''",",","``","bar","''","]","}"]
code: def decode(self, output: torch.tensor) -> List[Dict[str, Any]]:
      probabilities = torch.nn.functional.softmax(output, dim=1)
      probability_values, indices = probabilities.max(dim=1)
  
      return [
          {
              "class": self.vectorizer.data_vocab.lookup_index(
                  index=int(res[1])
              ),
              "probability": float(res[0]),
          }
          for res in zip(probability_values, indices)
      ]
  
imports: import logging
  from typing import Dict, List, Any
  import numpy
  import pandas
  import torch
  from transfer_nlp.common.tokenizers import CustomTokenizer
  from transfer_nlp.embeddings.embeddings import Embedding
  from transfer_nlp.loaders.loaders import DatasetSplits, DataFrameDataset
  from transfer_nlp.loaders.vectorizers import Vectorizer
  from transfer_nlp.loaders.vocabulary import CBOWVocabulary
  from transfer_nlp.plugins.config import register_plugin
  from transfer_nlp.plugins.predictors import PredictorABC
label_desc: Use image node_id 4 for calling the CBOWPredictor obj's underlying member method code with example usage: obj.decode(output) and returns: unknown