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","."]
code: def _predict_tabular_data(self, new_data, process=True):
      from .tabular_torch_dataset import TabularTorchDataset
  
      if process:
          new_data = self._process_test_data(new_data)
      if not isinstance(new_data, TabularTorchDataset):
          raise ValueError(
              "new_data must of of type TabularTorchDataset if process=False"
          )
      val_dataloader = new_data.build_loader(
          self.max_batch_size,
          self.num_dataloading_workers,
          is_test=True,
      )
      preds_dataset = []
      for data_batch in val_dataloader:
          preds_batch = self.model.predict(data_batch)
          preds_dataset.append(preds_batch)
      preds_dataset = np.concatenate(preds_dataset, 0)
      return preds_dataset
  
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 14 for calling the TabularNeuralNetTorchModel obj's underlying member method code with example usage: obj._predict_tabular_data(new_data, process) and returns: preds_dataset