code: def image_regression_model(
      name,
      train_data,
      val_data=None,
      freeze_layers=None,
      metrics=["mae"],
      optimizer_name=U.DEFAULT_OPT,
      pt_fc=[],
      pt_ps=[],
      verbose=1,
  ):
      """
      ```
      Returns a pre-defined/pre-trained model ready to be trained/fine-tuned
      for multi-class classification. By default, all layers are
      trainable/unfrozen.
  
  
      Args:
          name (string): one of model shown on ktrain.vision.print_image_regression_models
          train_data (image.Iterator): train data. Note: Will be manipulated here!
          val_data (image.Iterator): validation data.  Note: Will be manipulated here!
          freeze_layers (int):  number of beginning layers to make untrainable
                              If None, then all layers except new Dense layers
                              will be frozen/untrainable.
          metrics (list):  metrics to use
          optimizer_name(str): name of Keras optimizer (e.g., 'adam', 'sgd')
          multilabel(bool):  If True, model will be build to support
                             multilabel classificaiton (labels are not mutually exclusive).
                             If False, binary/multiclassification model will be returned.
                             If None, multilabel status will be inferred from data.
          pt_fc (list of ints): number of hidden units in extra Dense layers
                                  before final Dense layer of pretrained model.
                                  Only takes effect if name in PRETRAINED_MODELS
          pt_ps (list of floats): dropout probabilities to use before
                                  each extra Dense layer in pretrained model.
                                  Only takes effect if name in PRETRAINED_MODELS
          verbose (int):         verbosity
      Return:
          model(Model):  the compiled model ready to be fine-tuned/trained
  
      ```
      """
  
      return image_model(
          name,
          train_data,
          val_data=val_data,
          freeze_layers=freeze_layers,
          metrics=metrics,
          optimizer_name=optimizer_name,
          multilabel=False,
          pt_fc=pt_fc,
          pt_ps=pt_ps,
          verbose=verbose,
      )
  
file_path: ktrain/ktrain/vision/models.py
imports: from ..None import utils
  from ..imports import *
  from .wrn import create_wide_residual_network
label_desc: Use image node_id 5 for calling a global function with example usage: image_regression_model(name, train_data, val_data, freeze_layers, metrics, optimizer_name, pt_fc, pt_ps, verbose) and returns: image_model