code: def tabular_classifier(
      name,
      train_data,
      multilabel=None,
      metrics=None,
      hidden_layers=[1000, 500],
      hidden_dropouts=[0.0, 0.5],
      bn=False,
      verbose=1,
  ):
      """
      ```
      Build and return a classification model for tabular data
  
      Args:
          name (string): currently accepts 'mlp' for multilayer perceptron
          train_data (TabularDataset): TabularDataset instance returned from one of the tabular_from_* functions
          multilabel (bool):  If True, multilabel model will be returned.
                              If false, binary/multiclass model will be returned.
                              If None, multilabel will be inferred from data.
          metrics(list): List of metrics to use.  If None: 'accuracy' is used for binar/multiclass,
                         'binary_accuracy' is used for multilabel classification, and 'mae' is used for regressio
          hidden_layers(list): number of units in each hidden layer of NN
          hidden_dropouts(list): Dropout values after each hidden layer of NN
          bn(bool): If True, BatchNormalization will be used before each fully-connected layer in NN
          verbose (boolean): verbosity of output
      Return:
          model (Model): A Keras Model instance
      ```
      """
  
      return _tabular_model(
          name,
          train_data,
          multilabel=multilabel,
          metrics=metrics,
          hidden_layers=hidden_layers,
          hidden_dropouts=hidden_dropouts,
          bn=bn,
          verbose=verbose,
          is_regression=False,
      )
  
file_path: ktrain/ktrain/tabular/models.py
imports: from ..None import utils
  from ..imports import *
  from ..models import bn_drop_lin
label_desc: Use image node_id 4 for calling a global function with example usage: tabular_classifier(name, train_data, multilabel, metrics, hidden_layers, hidden_dropouts, bn, verbose) and returns: _tabular_model