code: def test_video_classifier_predict_from_tensors(tmpdir):
      mock_tensors = temp_encoded_tensors(num_frames=5)
      datamodule = VideoClassificationData.from_tensors(
          train_data=[mock_tensors, mock_tensors],
          train_targets=["Patient", "Doctor"],
          predict_data=[mock_tensors, mock_tensors],
          video_sampler=SequentialSampler,
          batch_size=1,
      )
  
      for sample in datamodule.train_dataset.data:
          expected_t_shape = 5
          assert sample["video"].shape[1] == expected_t_shape
  
      assert (
          len(datamodule.labels) == 2
      ), f"Expected number of labels to be 2 but found {len(datamodule.labels)}"
  
      model = VideoClassifier(
          num_classes=datamodule.num_classes,
          pretrained=False,
          backbone="slow_r50",
          labels=datamodule.labels,
      )
      trainer = flash.Trainer(
          default_root_dir=tmpdir,
          fast_dev_run=True,
          gpus=torch.cuda.device_count(),
      )
      trainer.finetune(model, datamodule=datamodule)
      predictions = trainer.predict(
          model, datamodule=datamodule, output="labels"
      )
  
      assert predictions is not None
      assert predictions[0][0] in datamodule.labels
  
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 9 for calling a global function with example usage: test_video_classifier_predict_from_tensors(tmpdir) without return types