code: def test_summarization_from_video(benchmark, setup_pytorch_tests):
      asr_udf = "SpeechRecognizer"
      create_udf = (
          f"CREATE UDF {asr_udf} TYPE HuggingFace "
          "TASK 'automatic-speech-recognition' MODEL 'openai/whisper-base';"
      )
      execute_query_fetch_all(setup_pytorch_tests, create_udf)
  
      summary_udf = "Summarizer"
      create_udf = (
          f"CREATE UDF {summary_udf} TYPE HuggingFace "
          "TASK 'summarization' MODEL 'philschmid/bart-large-cnn-samsum' MIN_LENGTH 10 MAX_LENGTH 100;"
      )
      execute_query_fetch_all(setup_pytorch_tests, create_udf)
  
      # TODO: use with SAMPLE AUDIORATE 16000
      select_query = (
          f"SELECT {summary_udf}({asr_udf}(audio)) FROM VIDEOS;"
      )
      output = benchmark(
          execute_query_fetch_all, setup_pytorch_tests, select_query
      )
  
      # verify that output has one row and one column only
      assert output.frames.shape == (1, 1)
      # verify that summary is as expected
      assert (
          output.frames.iloc[0][0]
          == "Jalen Hurts has scored his second rushing touchdown of the game."
      )
  
      drop_udf_query = f"DROP UDF {asr_udf};"
      execute_query_fetch_all(setup_pytorch_tests, drop_udf_query)
      drop_udf_query = f"DROP UDF {summary_udf};"
      execute_query_fetch_all(setup_pytorch_tests, drop_udf_query)
  
file_path: evadb/test/benchmark_tests/test_benchmark_pytorch.py
imports: import os
  from test.util import create_large_scale_image_dataset
  import pytest
  from evadb.server.command_handler import execute_query_fetch_all
label_desc: Use image node_id 6 for calling a global function with example usage: test_summarization_from_video(benchmark, setup_pytorch_tests) without return types