code: def pad(
      self, minibatch: List[Tuple[List[int], List[float], List[int]]]
  ) -> Tuple[List[List[int]], List[List[float]], List[int]]:
      # Pad a minibatch of dictionary features to be
      # batch_size * max_number_of_words * max_number_of_features
      # unpack the minibatch
      feats, weights, lengths = [], [], []
      for fs, ws, ls in minibatch:
          feats.append(fs)
          weights.append(ws)
          lengths.append(ls)
  
      lengths_flattened = [l for l_list in lengths for l in l_list]
      seq_lens = [len(l_list) for l_list in lengths]
      max_ex_len = self.pad_length(max(seq_lens))
      max_feat_len = max(lengths_flattened)
      all_lengths, all_feats, all_weights = [], [], []
      for i, seq_len in enumerate(seq_lens):
          ex_feats, ex_weights, ex_lengths = [], [], []
          feats_lengths, feats_vals, feats_weights = (
              lengths[i],
              feats[i],
              weights[i],
          )
          max_feat_len_example = max(feats_lengths)
          r_offset = 0
          for _ in feats_lengths:
              # The dict feats obtained from the featurizer will have necessary
              # padding at the utterance level. Therefore we move the offset by
              # max feature length in the example.
              ex_feats.extend(
                  feats_vals[r_offset : r_offset + max_feat_len_example]
              )
              ex_feats.extend(
                  [self.pad_token]
                  * (max_feat_len - max_feat_len_example)
              )
              ex_weights.extend(
                  feats_weights[
                      r_offset : r_offset + max_feat_len_example
                  ]
              )
              ex_weights.extend(
                  [0.0] * (max_feat_len - max_feat_len_example)
              )
              r_offset += max_feat_len_example
          ex_lengths.extend(feats_lengths)
          # Pad examples
          ex_padding = (max_ex_len - seq_len) * max_feat_len
          if self.pad_first:
              # left padding
              ex_feats = [self.pad_token] * ex_padding + ex_feats
              ex_weights = [0.0] * ex_padding + ex_weights
              ex_lengths = [1] * (max_ex_len - seq_len) + ex_lengths
          else:
              # right padding
              ex_feats.extend([self.pad_token] * ex_padding)
              ex_weights.extend([0.0] * ex_padding)
              ex_lengths.extend([1] * (max_ex_len - seq_len))
          all_feats.append(ex_feats)
          all_weights.append(ex_weights)
          all_lengths.append(ex_lengths)
      return all_feats, all_weights, all_lengths
  
file_path: pytext/pytext/fields/dict_field.py
imports: from collections import Counter
  from typing import List, Tuple
  import torch
  from pytext.common.constants import VocabMeta
  from pytext.legacy import data, vocab
  from pytext.utils.data import no_tokenize
  from .field import VocabUsingField
label_desc: Use image node_id 3 for calling the DictFeatureField obj's underlying member method code with example usage: obj.pad(minibatch) and returns: all_feats,  all_weights,  all_lengths