code: def compute_metrics(eval_pred):
      predictions, labels = eval_pred
      predictions = np.argmax(predictions, axis=1)
      return metric.compute(predictions=predictions, references=labels)
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/examples/research_projects/codeparrot/examples/train_complexity_predictor.py
imports: import argparse
  from copy import deepcopy
  import numpy
  from datasets import ClassLabel, DatasetDict, load_dataset
  from evaluate import load
  from transformers import AutoModelForSequenceClassification, AutoTokenizer, DataCollatorWithPadding, Trainer, TrainerCallback, TrainingArguments, set_seed
label_desc: Use image node_id 2 for calling a global function with example usage: compute_metrics(eval_pred) and returns: metric