code: def test_video_classifier_finetune_from_csv(tmpdir):
      with mock_video_csv_file(tmpdir) as (mock_csv, total_duration):
          half_duration = total_duration / 2 - 1e-9
  
          datamodule = VideoClassificationData.from_csv(
              "file",
              "target",
              train_file=mock_csv,
              clip_sampler="uniform",
              clip_duration=half_duration,
              video_sampler=SequentialSampler,
              decode_audio=False,
              batch_size=1,
          )
  
          for sample in datamodule.train_dataset.data:
              expected_t_shape = 5
              assert sample["video"].shape[1] == expected_t_shape
  
          model = VideoClassifier(
              num_classes=datamodule.num_classes,
              pretrained=False,
              backbone="slow_r50",
          )
          trainer = flash.Trainer(
              default_root_dir=tmpdir,
              fast_dev_run=True,
              gpus=torch.cuda.device_count(),
          )
          trainer.finetune(model, datamodule=datamodule)
  
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 10 for calling a global function with example usage: test_video_classifier_finetune_from_csv(tmpdir) without return types