code: def pretrained_datagen(data, name):
      if not data or not U.is_iter(data):
          return
      idg = data.image_data_generator
      if name == PRETRAINED_RESNET50:
          idg.preprocessing_function = (
              keras.applications.resnet50.preprocess_input
          )
          idg.ktrain_preproc = "resnet50"
          idg.rescale = None
          idg.featurewise_center = False
          idg.samplewise_center = False
          idg.featurewise_std_normalization = False
          idg.samplewise_std_normalization = False
          idg.zca_whitening = False
      elif name == PRETRAINED_MOBILENET:
          idg.preprocessing_function = (
              keras.applications.mobilenet.preprocess_input
          )
          idg.ktrain_preproc = "mobilenet"
          idg.rescale = None
          idg.featurewise_center = False
          idg.samplewise_center = False
          idg.featurewise_std_normalization = False
          idg.samplewise_std_normalization = False
          idg.zca_whitening = False
      elif name == PRETRAINED_MOBILENETV3:
          idg.preprocessing_function = (
              keras.applications.mobilenet_v3.preprocess_input
          )
          idg.ktrain_preproc = "mobilenetv3"
          idg.rescale = None
          idg.featurewise_center = False
          idg.samplewise_center = False
          idg.featurewise_std_normalization = False
          idg.samplewise_std_normalization = False
          idg.zca_whitening = False
      elif name == PRETRAINED_INCEPTION:
          idg.preprocessing_function = (
              keras.applications.inception_v3.preprocess_input
          )
          idg.ktrain_preproc = "inception"
          idg.rescale = None
          idg.featurewise_center = False
          idg.samplewise_center = False
          idg.featurewise_std_normalization = False
          idg.samplewise_std_normalization = False
          idg.zca_whitening = False
      elif (
          name == PRETRAINED_EFFICIENTNETB1
          or name == PRETRAINED_EFFICIENTNETB7
      ):
          idg.preprocessing_function = (
              keras.applications.efficientnet.preprocess_input
          )
          idg.ktrain_preproc = "efficientnet"
          idg.rescale = None
          idg.featurewise_center = False
          idg.samplewise_center = False
          idg.featurewise_std_normalization = False
          idg.samplewise_std_normalization = False
          idg.zca_whitening = False
  
      return
  
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 3 for calling a global function with example usage: pretrained_datagen(data, name) without return types