code: def test_multiple_inputs_model(self):
      sc = init_nncontext()
      rdd = sc.parallelize(range(100))
  
      from pyspark.sql import SparkSession
  
      spark = SparkSession(sc)
      df = rdd.map(
          lambda x: (
              [float(x)] * 25,
              [float(x)] * 25,
              [int(np.random.randint(0, 2, size=()))],
          )
      ).toDF(["f1", "f2", "label"])
  
      estimator = get_estimator(
          workers_per_node=2, model_fn=lambda config: MultiInputNet()
      )
      estimator.fit(
          df,
          batch_size=4,
          epochs=2,
          feature_cols=["f1", "f2"],
          label_cols=["label"],
      )
      estimator.evaluate(
          df,
          batch_size=4,
          feature_cols=["f1", "f2"],
          label_cols=["label"],
      )
      result = estimator.predict(
          df, batch_size=4, feature_cols=["f1", "f2"]
      )
      result.collect()
  
imports: import os
  from unittest import TestCase
  import numpy
  import pytest
  import torch
  import torch.nn
  from zoo.orca import OrcaContext
  from zoo.orca.data.pandas import read_csv
  from zoo.orca.learn.metrics import Accuracy
  from zoo import init_nncontext
  from zoo.orca.learn.pytorch import Estimator
  from zoo.orca.data import SparkXShards
  from zoo.orca.data.image.utils import chunks
label_desc: Use image node_id 9 for calling the TestPyTorchEstimator obj's underlying member method code with example usage: obj.test_multiple_inputs_model() without return types