code: def collater(self, samples):
      # Filter out None samples
      samples = [s for s in samples if s is not None]
      # Check if samples is empty after filtering
      if not samples:
          return {}
      collated_dict = {}
      keys = samples[
          0
      ].keys()  # Use the keys of the first sample as a reference
      for k in keys:
          values = [sample[k] for sample in samples]
          # If the value type for the key is torch.Tensor, stack them else return list
          collated_dict[k] = (
              torch.stack(values, dim=0)
              if isinstance(values[0], torch.Tensor)
              else values
          )
      return collated_dict
  
file_path: lavis/lavis/datasets/datasets/base_dataset.py
imports: import json
  from typing import Iterable
  import pandas
  import torch
  from torch.utils.data import Dataset, ConcatDataset
  from torch.utils.data.dataloader import default_collate
label_desc: Use image node_id 3 for calling the BaseDataset obj's underlying member method code with example usage: obj.collater(samples) and returns: collated_dict, dict