code: def collater(self, samples):
      if len(samples) == 0:
          return {}
  
      features = [s["features"] for s in samples]
      sizes = [len(s) for s in features]
  
      target_size = max(sizes)
  
      collated_features = features[0].new_zeros(
          len(features), target_size, features[0].size(-1)
      )
      padding_mask = torch.BoolTensor(
          collated_features.shape[:-1]
      ).fill_(False)
      for i, (f, size) in enumerate(zip(features, sizes)):
          collated_features[i, :size] = f
          padding_mask[i, size:] = True
  
      res = {
          "id": torch.LongTensor([s["id"] for s in samples]),
          "net_input": {
              "features": collated_features,
              "padding_mask": padding_mask,
          },
      }
  
      if len(self.labels) > 0:
          target = data_utils.collate_tokens(
              [s["target"] for s in samples],
              pad_idx=self.label_dict.pad(),
              left_pad=False,
          )
          res["target"] = target
  
      if self.aux_tgt:
          idxs = torch.nn.utils.rnn.pad_sequence(
              [s["aux_target"] for s in samples],
              batch_first=True,
              padding_value=-1,
          )
          res["net_input"]["aux_target"] = idxs
  
      return res
  
file_path: fairseq/examples/wav2vec/unsupervised/data/extracted_features_dataset.py
imports: import logging
  import os
  import contextlib
  import numpy
  import torch
  from fairseq.data import FairseqDataset, data_utils
label_desc: Use image node_id 4 for calling the ExtractedFeaturesDataset obj's underlying member method code with example usage: obj.collater(samples) and returns: res, dict