code: def tabular_regression_model(
      name,
      train_data,
      metrics=["mae"],
      hidden_layers=[1000, 500],
      hidden_dropouts=[0.0, 0.5],
      bn=False,
      verbose=1,
  ):
      """
      ```
      Build and return a regression 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
          metrics(list): list of metrics to use
          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 before used 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=None,
          metrics=metrics,
          hidden_layers=hidden_layers,
          hidden_dropouts=hidden_dropouts,
          bn=bn,
          verbose=verbose,
          is_regression=True,
      )
  
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 5 for calling a global function with example usage: tabular_regression_model(name, train_data, metrics, hidden_layers, hidden_dropouts, bn, verbose) and returns: _tabular_model