code: def test_bigdl_pytorch_estimator_pandas_dataframe(self):
      class SimpleModel(nn.Module):
          def __init__(self):
              super(SimpleModel, self).__init__()
              self.fc = nn.Linear(1, 10)
  
          def forward(self, x):
              x = torch.unsqueeze(x, dim=1)
              x = self.fc(x)
              return F.log_softmax(x, dim=1)
  
      def loss_func(input, target):
          return nn.CrossEntropyLoss().forward(
              input, target.flatten().long()
          )
  
      model = SimpleModel()
  
      OrcaContext.pandas_read_backend = "pandas"
      file_path = os.path.join(
          resource_path, "orca/learn/simple_feature_label.csv"
      )
      data_shard = read_csv(file_path)
  
      with tempfile.TemporaryDirectory() as temp_dir_name:
          estimator = Estimator.from_torch(
              model=model,
              loss=loss_func,
              metrics=[Accuracy()],
              optimizer=SGD(learningrate_schedule=Default()),
              model_dir=temp_dir_name,
              backend="bigdl",
          )
          estimator.fit(
              data=data_shard,
              epochs=1,
              batch_size=4,
              feature_cols=["feature"],
              label_cols=["label"],
              validation_data=data_shard,
              checkpoint_trigger=EveryEpoch(),
          )
          estimator.evaluate(
              data_shard,
              batch_size=4,
              feature_cols=["feature"],
              label_cols=["label"],
          )
          est2 = Estimator.from_torch(
              model=model,
              loss=loss_func,
              metrics=[Accuracy()],
              optimizer=None,
              backend="bigdl",
          )
          est2.load_orca_checkpoint(temp_dir_name)
          est2.predict(
              data_shard, batch_size=4, feature_cols=["feature"]
          )
  
imports: from unittest import TestCase
  import os
  import pytest
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  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 4 for calling the TestEstimatorForSpark obj's underlying member method code with example usage: obj.test_bigdl_pytorch_estimator_pandas_dataframe() and returns: nn, F