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: Compile the trained model for faster inference.
  
  This completely 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 its alternative counterpart.
docstr_tok: ["Compile","the","trained","model","for","faster","inference",".","This","completely","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","its","alternative","counterpart","."]
code: def compile(self, compiler_configs=None):
      """
      Compile the trained model for faster inference.
  
      This completely 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 its alternative counterpart.
      """
      assert (
          self.is_fit()
      ), "The model must be fit before calling the compile method."
      if compiler_configs is None:
          compiler_configs = {}
      # Take self.max_batch_size as default batch size, instead of None in AbstractModel
      batch_size = compiler_configs.get(
          "batch_size", self.max_batch_size
      )
      compiler_configs.update(batch_size=batch_size)
      super().compile(compiler_configs)
  
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 32 for calling the TabularNeuralNetTorchModel obj's underlying member method code with example usage: obj.compile(compiler_configs) without return types