code: def test_model(model, X_test, y_test):
      model.eval()
      with torch.no_grad():
          predict_out = model(X_test)
          _, predict_y = torch.max(predict_out, 1)
  
          print(
              "\nprediction accuracy",
              float(accuracy_score(y_test.cpu(), predict_y.cpu())),
          )
          return infer_signature(X_test.numpy(), predict_out.numpy())
  
file_path: mlflow/examples/pytorch/torchscript/IrisClassification/iris_classification.py
imports: import argparse
  import torch
  import torch.nn.functional
  from sklearn.datasets import load_iris
  from sklearn.metrics import accuracy_score
  from sklearn.model_selection import train_test_split
  from torch import nn
  import mlflow.pytorch
  from mlflow.models import infer_signature
label_desc: Use image node_id 3 for calling a global function with example usage: test_model(model, X_test, y_test) and returns: infer_signature