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 _prepare_params(params):
      params = params.copy()
  
      processor_param_keys = {
          "proc.embed_min_categories",
          "proc.impute_strategy",
          "proc.max_category_levels",
          "proc.skew_threshold",
          "use_ngram_features",
      }
      processor_kwargs = {
          k: v for k, v in params.items() if k in processor_param_keys
      }
      for key in processor_param_keys:
          params.pop(key, None)
  
      optimizer_param_keys = {
          "optimizer",
          "learning_rate",
          "weight_decay",
      }
      optimizer_kwargs = {
          k: v for k, v in params.items() if k in optimizer_param_keys
      }
      for key in optimizer_param_keys:
          params.pop(key, None)
  
      fit_param_keys = {"num_epochs", "epochs_wo_improve"}
      fit_kwargs = {
          k: v for k, v in params.items() if k in fit_param_keys
      }
      for key in fit_param_keys:
          params.pop(key, None)
  
      loss_param_keys = {"loss_function", "gamma"}
      loss_kwargs = {
          k: v for k, v in params.items() if k in loss_param_keys
      }
      for key in loss_param_keys:
          params.pop(key, None)
  
      return (
          processor_kwargs,
          optimizer_kwargs,
          fit_kwargs,
          loss_kwargs,
          params,
      )
  
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 9 for calling the TabularNeuralNetTorchModel obj's underlying member method code with example usage: obj._prepare_params(params) and returns: processor_kwargs,  optimizer_kwargs,  fit_kwargs,  loss_kwargs,  params