code: def main(args):
      tasks = {
          "cola": ["CoLA.tsv", "glue/cola/test.tsv"],
          "sst": ["SST-2.tsv", "glue/sst/test.tsv"],
          "mrpc": ["MRPC.tsv", "glue/mrpc/test.tsv"],
          "sts": ["STS-B.tsv", "glue/sts/test.tsv"],
          "qqp": ["QQP.tsv", "glue/qqp/test.tsv"],
          "mnli_m": ["MNLI-m.tsv", "glue/mnli/test_matched.tsv"],
          "mnli_mm": ["MNLI-mm.tsv", "glue/mnli/test_mismatched.tsv"],
          "qnli": ["QNLI.tsv", "glue/qnli/test.tsv"],
          "rte": ["RTE.tsv", "glue/rte/test.tsv"],
          "wnli": ["WNLI.tsv", "glue/wnli/test.tsv"],
          "ax": ["AX.tsv", "glue/rte_diagnostic/diagnostic.tsv"],
      }
  
      os.makedirs(args.save_dir, exist_ok=True)
  
      for task, (save_name, test_file_path) in tasks.items():
          if task == "ax":
              # For AX, we need to load the mnli-m checkpoint and run inference
              test_df = pd.read_csv(test_file_path, sep="\t", header=0)
              test_index = test_df["index"]
              predictor = MultiModalPredictor.load(
                  f"{args.prefix}_mnli_m"
              )
              label_column = predictor.label
              predictions = predictor.predict(test_df)
          else:
              test_index = get_test_index(test_file_path)
              prediction_df = pd.read_csv(
                  f"{args.prefix}_{task}/test_prediction.csv",
                  index_col=0,
              )
              label_column = prediction_df.columns[0]
              predictions = prediction_df[label_column]
          if task == "sts":
              predictions = np.clip(predictions, 0, 5)
          with open(os.path.join(args.save_dir, save_name), "w") as of:
              of.write("index\t{}\n".format(label_column))
              for i in range(len(predictions)):
                  of.write(
                      "{}\t{}\n".format(test_index[i], predictions[i])
                  )
  
file_path: autogluon/examples/automm/text_prediction/generate_submission.py
imports: import pandas
  from autogluon.multimodal import MultiModalPredictor
  import argparse
  import os
  import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: main(args) without return types