class_docstr: PyTorch neural network models for classification/regression with tabular data.
class_docstr_tok: ["PyTorch","neural","network","models","for","classification\/regression","with","tabular","data","."]
docstr: Process train or test DataFrame into a form fit for neural network models.
  Args:
      df (pd.DataFrame): Data to be processed (X)
      labels (pd.Series): labels to be processed (y)
  Returns:
      Dataset object
docstr_tok: ["Process","train","or","test","DataFrame","into","a","form","fit","for","neural","network","models",".","Args",":","df","(","pd.DataFrame",")",":","Data","to","be","processed","(","X",")","labels","(","pd.Series",")",":","labels","to","be","processed","(","y",")","Returns",":","Dataset","object"]
code: def _process_test_data(self, df, labels=None):
      """Process train or test DataFrame into a form fit for neural network models.
      Args:
          df (pd.DataFrame): Data to be processed (X)
          labels (pd.Series): labels to be processed (y)
      Returns:
          Dataset object
      """
      from .tabular_torch_dataset import TabularTorchDataset
  
      # sklearn processing n_quantiles warning
      warnings.filterwarnings("ignore", module="sklearn.preprocessing")
      if labels is not None and len(labels) != len(df):
          raise ValueError(
              "Number of examples in Dataframe does not match number of labels"
          )
      if (
          self.processor is None
          or self._types_of_features is None
          or self.feature_arraycol_map is None
          or self.feature_type_map is None
      ):
          raise ValueError(
              "Need to process training data before test data"
          )
      if self.features_to_drop:
          drop_cols = [
              col for col in df.columns if col in self.features_to_drop
          ]
          if drop_cols:
              df = df.drop(columns=drop_cols)
  
      # self.feature_arraycol_map, self.feature_type_map have been previously set while processing training data.
      df = self.processor.transform(df)
      return TabularTorchDataset(
          df,
          self.feature_arraycol_map,
          self.feature_type_map,
          self.problem_type,
          labels,
      )
  
imports: import json
  import logging
  import os
  import random
  import time
  import warnings
  from typing import Dict, Union
  import numpy
  import pandas
  from autogluon.common.features.types import R_BOOL, R_CATEGORY, R_FLOAT, R_INT, S_TEXT_AS_CATEGORY, S_TEXT_NGRAM
  from autogluon.common.utils.pandas_utils import get_approximate_df_mem_usage
  from autogluon.common.utils.resource_utils import ResourceManager
  from autogluon.common.utils.try_import import try_import_torch
  from autogluon.core.constants import BINARY, MULTICLASS, QUANTILE, REGRESSION, SOFTCLASS
  from autogluon.core.hpo.constants import RAY_BACKEND
  from autogluon.core.models.abstract.abstract_nn_model import AbstractNeuralNetworkModel
  from autogluon.core.utils.exceptions import TimeLimitExceeded
  from ..compilers.native import TabularNeuralNetTorchNativeCompiler
  from ..compilers.onnx import TabularNeuralNetTorchOnnxCompiler
  from ..hyperparameters.parameters import get_default_param
  from ..hyperparameters.searchspaces import get_default_searchspace
  from ..utils.data_preprocessor import create_preprocessor, get_feature_arraycol_map, get_feature_type_map
  from ..utils.nn_architecture_utils import infer_y_range
label_desc: Use image node_id 16 for calling the TabularNeuralNetTorchModel obj's underlying member method code with example usage: obj._process_test_data(df, labels) and returns: TabularTorchDataset