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: Take the compiler to perform actual compilation.
  
  This overrides the _compile() in AbstractModel, since we won't
  overwrite self.model in the compilation process.
  Instead, self.processor would be converted from sklearn ColumnTransformer
  to TabularNeuralNetTorchOnnxTransformer.
docstr_tok: ["Take","the","compiler","to","perform","actual","compilation",".","This","overrides","the","_compile","(",")","in","AbstractModel",",","since","we","won't","overwrite","self.model","in","the","compilation","process",".","Instead",",","self.processor","would","be","converted","from","sklearn","ColumnTransformer","to","TabularNeuralNetTorchOnnxTransformer","."]
code: def _compile(self, **kwargs):
      """
      Take the compiler to perform actual compilation.
  
      This overrides the _compile() in AbstractModel, since we won't
      overwrite self.model in the compilation process.
      Instead, self.processor would be converted from sklearn ColumnTransformer
      to TabularNeuralNetTorchOnnxTransformer.
      """
      from sklearn.compose._column_transformer import ColumnTransformer
  
      input_types = kwargs.get(
          "input_types",
          self._get_input_types(batch_size=self.max_batch_size),
      )
      assert isinstance(self.processor, ColumnTransformer), (
          f"unexpected processor type {type(self.processor)}, "
          "expecting processor type to be sklearn.compose._column_transformer.ColumnTransformer"
      )
      self.processor = self._compiler.compile(
          model=(self.processor, self.model),
          path=self.path,
          input_types=input_types,
      )
  
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 33 for calling the TabularNeuralNetTorchModel obj's underlying member method code with example usage: obj._compile() without return types