code: def batch_states(
      states: Sequence[Any],
      device: torch.device,
      phi: Callable[[Any], Any],
  ) -> Any:
      """The default method for making batch of observations.
  
      Args:
          states (list): list of observations from an environment.
          device (module): CPU or GPU the data should be placed on
          phi (callable): Feature extractor applied to observations
  
      Return:
          the object which will be given as input to the model.
      """
      features = [phi(s) for s in states]
      # return concat_examples(features, device=device)
      collated_features = default_collate(features)
      if isinstance(features[0], tuple):
          collated_features = tuple(collated_features)
      return _to_recursive(collated_features, device)
  
imports: from typing import Any, Callable, Sequence
  import torch
  from torch.utils.data._utils.collate import default_collate
label_desc: Use image node_id 2 for calling a global function with example usage: batch_states(states, device, phi) and returns: _to_recursive