class_docstr: Class object that fetches keras' VGG19 model trained on the imagenet dataset
  and declares <layers_to_extract> as output layers. Used as feature extractor
  for the perceptual loss function.
  
  Args:
      layers_to_extract: list of layers to be declared as output layers.
      patch_size: integer, defines the size of the input (patch_size x patch_size).
  
  Attributes:
      loss_model: multi-output vgg architecture with <layers_to_extract> as output layers.
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docstr: Loads pre-trained VGG, declares as output the intermediate
  layers selected by self.layers_to_extract.
docstr_tok: ["Loads","pre-trained","VGG",",","declares","as","output","the","intermediate","layers","selected","by","self.layers_to_extract","."]
code: def _cut_vgg(self):
      """
      Loads pre-trained VGG, declares as output the intermediate
      layers selected by self.layers_to_extract.
      """
  
      vgg = VGG19(
          weights="imagenet",
          include_top=False,
          input_shape=self.input_shape,
      )
      vgg.trainable = False
      outputs = [vgg.layers[i].output for i in self.layers_to_extract]
      self.model = Model([vgg.input], outputs)
      self.model._name = "feature_extractor"
      self.name = "vgg19"
  
imports: from tensorflow.keras.models import Model
  from tensorflow.keras.applications.vgg19 import VGG19
  from ISR.utils.logger import get_logger
label_desc: Use image node_id 2 for calling the Cut_VGG19 obj's underlying member method code with example usage: obj._cut_vgg() without return types