code: def main(args):
      ### Dataset Loading
      train_df = load_dataset(
          "wietsedv/stsbenchmark", split="train"
      ).to_pandas()
      val_df = load_dataset(
          "wietsedv/stsbenchmark", split="validation"
      ).to_pandas()
      test_df = load_dataset(
          "wietsedv/stsbenchmark", split="test"
      ).to_pandas()
  
      start = time.time()
      predictor = MultiModalPredictor(
          pipeline="feature_extraction",
          hyperparameters={
              "model.hf_text.checkpoint_name": args.checkpoint_name,
          },
      )
      ag_load = time.time()
      evaluate(predictor, test_df)
      ag_eval = time.time()
  
      ort_sess = ort.InferenceSession(
          args.onnx_path, providers=["CUDAExecutionProvider"]
      )
      onnx_load = time.time()
      evaluate(predictor, test_df, ort_sess)
      onnx_eval = time.time()
  
      print("Autogluon load time: %.4f" % (ag_load - start))
      print("Autogluon eval time: %.4f" % (ag_eval - ag_load))
      print("ONNX load time: %.4f" % (onnx_load - ag_eval))
      print("ONNX eval time: %.4f" % (onnx_eval - onnx_load))
  
      exit()
      # TODO: support fit after predict for two tower models:
      predictor.fit(
          train_df,
          val_df,
          hyperparameters={
              "optimization.max_epochs": 1,
          },
      )
      evaluate(predictor, test_df)
  
file_path: autogluon/examples/automm/pipeline/feature_extraction_example.py
imports: import argparse
  from autogluon.multimodal import MultiModalPredictor
  from datasets import load_dataset
  import time
  from sklearn.metrics.pairwise import paired_cosine_distances
  from scipy.stats import pearsonr, spearmanr
  import numpy
  import onnx
  import onnxruntime
  import torch
  from torch import tensor
label_desc: Use image node_id 2 for calling a global function with example usage: main(args) without return types