code: def classify(self, segments) -> pd.DataFrame:
      import torch
  
      with torch.no_grad():
          preds = self.model(segments).softmax(1)
      label_indices = preds.argmax(axis=1)
      actions = self.labels[label_indices]
      # TODO ACTION: In the current pipeline, actions will always get batches on
      # length 1, so this case would never be invoked.
      if np.isscalar(actions) == 1:
          outcome = pd.DataFrame({"labels": np.array([actions])})
      return outcome
  
file_path: evadb/evadb/functions/mvit_action_recognition.py
imports: import numpy
  import pandas
  from evadb.functions.abstract.pytorch_abstract_function import PytorchAbstractClassifierFunction
  from evadb.utils.generic_utils import try_to_import_torch, try_to_import_torchvision
label_desc: Use image node_id 6 for calling the MVITActionRecognition obj's underlying member method code with example usage: obj.classify(segments) and returns: outcome