code: def __call__(self, batch: List[Any]) -> Any:
      elem = batch[0]
      if isinstance(elem, BaseData):
          return Batch.from_data_list(
              batch,
              follow_batch=self.follow_batch,
              exclude_keys=self.exclude_keys,
          )
      elif isinstance(elem, torch.Tensor):
          return default_collate(batch)
      elif isinstance(elem, TensorFrame):
          return torch_frame.cat(batch, dim=0)
      elif isinstance(elem, float):
          return torch.tensor(batch, dtype=torch.float)
      elif isinstance(elem, int):
          return torch.tensor(batch)
      elif isinstance(elem, str):
          return batch
      elif isinstance(elem, Mapping):
          return {
              key: self([data[key] for data in batch]) for key in elem
          }
      elif isinstance(elem, tuple) and hasattr(elem, "_fields"):
          return type(elem)(*(self(s) for s in zip(*batch)))
      elif isinstance(elem, Sequence) and not isinstance(elem, str):
          return [self(s) for s in zip(*batch)]
  
      raise TypeError(f"DataLoader found invalid type: '{type(elem)}'")
  
file_path: pytorch_geometric/torch_geometric/loader/dataloader.py
imports: from collections.abc import Mapping
  from typing import Any, List, Optional, Sequence, Union
  import torch.utils.data
  from torch.utils.data.dataloader import default_collate
  from torch_geometric.data import Batch, Dataset
  from torch_geometric.data.data import BaseData
  from torch_geometric.data.datapipes import DatasetAdapter
  from torch_geometric.typing import TensorFrame, torch_frame
label_desc: Use image node_id 2 for calling the Collater obj's underlying member method code with example usage: obj.__call__(batch) and returns: Batch, default_collate, torch_frame, torch, torch, batch, unknown, type, unknown