code: def test_bigdl_pytorch_estimator_dataframe_predict(self):
      def loss_func(input, target):
          return nn.CrossEntropyLoss().forward(
              input, target.flatten().long()
          )
  
      class IdentityNet(nn.Module):
          def __init__(self):
              super().__init__()
              # need this line to avoid optimizer raise empty variable list
              self.fc1 = nn.Linear(5, 5)
  
          def forward(self, input_):
              return input_
  
      model = IdentityNet()
      rdd = self.sc.range(0, 100)
      df = rdd.map(
          lambda x: (
              [float(x)] * 5,
              [int(np.random.randint(0, 2, size=()))],
          )
      ).toDF(["feature", "label"])
  
      with tempfile.TemporaryDirectory() as temp_dir_name:
          estimator = Estimator.from_torch(
              model=model,
              loss=loss_func,
              optimizer=SGD(learningrate_schedule=Default()),
              model_dir=temp_dir_name,
              backend="bigdl",
          )
          result = estimator.predict(df, feature_cols=["feature"])
          expr = "sum(cast(feature <> to_array(prediction) as int)) as error"
          assert result.selectExpr(expr).first()["error"] == 0
  
imports: from unittest import TestCase
  import os
  import pytest
  import torch.nn
  import torch.nn.functional
  import numpy
  from pyspark.sql import SparkSession
  from pyspark.sql.types import ArrayType, DoubleType
  from pyspark.sql import SparkSession
  from bigdl.orca import init_orca_context, stop_orca_context
  from bigdl.orca.data.pandas import read_csv
  from bigdl.orca.data import SparkXShards
  from bigdl.orca.learn.pytorch import Estimator
  from bigdl.orca.learn.metrics import Accuracy
  from bigdl.orca.learn.trigger import EveryEpoch
  from bigdl.orca.learn.optimizers import SGD
  from bigdl.orca.learn.optimizers.schedule import Default
  from bigdl.orca import OrcaContext
  import tempfile
label_desc: Use image node_id 3 for calling the TestEstimatorForDataFrame obj's underlying member method code with example usage: obj.test_bigdl_pytorch_estimator_dataframe_predict() and returns: nn, input_