class_docstr: A modified wrapper for a public python BPE tokenizer. The modifications allow encoding directly into the formats
  required for finetune. Particularly with respect to formatting with multiple inputs.
class_docstr_tok: ["A","modified","wrapper","for","a","public","python","BPE","tokenizer",".","The","modifications","allow","encoding","directly","into","the","formats","required","for","finetune",".","Particularly","with","respect","to","formatting","with","multiple","inputs","."]
docstr: Convert a batch of ids [batch_size, id] into text(ish).
docstr_tok: ["Convert","a","batch","of","ids","[","batch_size",",","id","]","into","text","(","ish",")","."]
code: def decode(self, ids):
      """
      Convert a batch of ids [batch_size, id] into text(ish).
      """
  
      raise NotImplementedError(
          "The inverse of the BERT encoder is not implemented because we have not implemented generation with BERT"
      )
  
imports: import os
  import numpy
  import finetune
  from finetune.encoding.input_encoder import EncodedOutput, BaseEncoder
  from finetune.base_models.bert.tokenizer import FullTokenizer
label_desc: Use image node_id 5 for calling the BERTEncoder obj's underlying member method code with example usage: obj.decode(ids) without return types