class_docstr: Convert encodings stored in the pickle file to encoding and mask.
  Transform the pad and resize the embedding to match the generator config.
code: def __call__(self, data):
      out_dict = dict()
      for token_length, encoding_key, out_key in zip(
          self.encoding_lengths, self.encoding_keys, self.out_keys
      ):
          embed, mask = self._pad_and_resize(
              data[encoding_key]["encodings"], token_length
          )
          out_dict[f"{out_key}_embeddings"] = embed
          out_dict[f"{out_key}_mask"] = mask
      return out_dict
  
imports: from typing import List, Optional
  import torch
  from nemo.utils import logging
label_desc: Use image node_id 3 for calling the PickleTransform obj's underlying member method code with example usage: obj.__call__(data) and returns: out_dict