code: def test_predict_tensor():
      img = torch.rand(1, 3, 64, 64)
      model = SemanticSegmentation(2, backbone="mobilenetv3_large_100")
      datamodule = SemanticSegmentationData.from_tensors(
          predict_data=img, batch_size=1
      )
      trainer = Trainer()
      out = trainer.predict(
          model, datamodule=datamodule, output="labels"
      )
      assert isinstance(out[0][0], list)
      assert len(out[0][0]) == 64
      assert len(out[0][0][0]) == 64
  
imports: from typing import Any
  from unittest.mock import patch
  import numpy
  import pytest
  import torch
  from flash import Trainer
  from flash.core.data.io.input import DataKeys
  from flash.core.utilities.imports import _SEGMENTATION_MODELS_AVAILABLE, _TOPIC_IMAGE_AVAILABLE, _TOPIC_SERVE_AVAILABLE
  from flash.image import SemanticSegmentation
  from flash.image.segmentation.data import SemanticSegmentationData
  from torch import Tensor
  from tests.helpers.task_tester import TaskTester
label_desc: Use image node_id 4 for calling a global function with example usage: test_predict_tensor() without return types