code: def mock_video_data_frame():
      """Creates a temporary mock encoded video dataset with 4 videos labeled from 0 to 4.
  
      Returns a labeled video file which points to this mock encoded video dataset, the ordered label and videos tuples
      and the video duration in seconds.
  
      """
      num_frames = 10
      fps = 5
      with temp_encoded_video(num_frames=num_frames, fps=fps) as (
          video_file_name_1,
          data_1,
      ), temp_encoded_video(num_frames=num_frames, fps=fps) as (
          video_file_name_2,
          data_2,
      ):
          data_frame = DataFrame.from_dict(
              {
                  "file": [
                      video_file_name_1,
                      video_file_name_2,
                      video_file_name_1,
                      video_file_name_2,
                  ],
                  "target": ["cat", "dog", "cat", "dog"],
              }
          )
  
          video_duration = num_frames / fps
          yield data_frame, video_duration
  
imports: import contextlib
  import os
  import tempfile
  from pathlib import Path
  from typing import Any
  import flash
  import pytest
  import torch
  from flash.core.data.io.input import DataKeys
  from flash.core.utilities.imports import _FIFTYONE_AVAILABLE, _TOPIC_VIDEO_AVAILABLE
  from flash.video import VideoClassificationData, VideoClassifier
  from pandas import DataFrame
  from torch import Tensor
  from torch.utils.data import SequentialSampler
  from tests.helpers.task_tester import TaskTester
  from tests.video.classification.test_data import create_dummy_video_frames, temp_encoded_tensors
label_desc: Use image node_id 2 for calling a global function with example usage: mock_video_data_frame() without return types