code: def loadModel(
      url="https://github.com/swghosh/DeepFace/releases/download/weights-vggface2-2d-aligned/VGGFace2_DeepFace_weights_val-0.9034.h5.zip",
  ) -> Model:
      base_model = Sequential()
      base_model.add(
          Convolution2D(
              32,
              (11, 11),
              activation="relu",
              name="C1",
              input_shape=(152, 152, 3),
          )
      )
      base_model.add(
          MaxPooling2D(
              pool_size=3, strides=2, padding="same", name="M2"
          )
      )
      base_model.add(
          Convolution2D(16, (9, 9), activation="relu", name="C3")
      )
      base_model.add(
          LocallyConnected2D(16, (9, 9), activation="relu", name="L4")
      )
      base_model.add(
          LocallyConnected2D(
              16, (7, 7), strides=2, activation="relu", name="L5"
          )
      )
      base_model.add(
          LocallyConnected2D(16, (5, 5), activation="relu", name="L6")
      )
      base_model.add(Flatten(name="F0"))
      base_model.add(Dense(4096, activation="relu", name="F7"))
      base_model.add(Dropout(rate=0.5, name="D0"))
      base_model.add(Dense(8631, activation="softmax", name="F8"))
  
      # ---------------------------------
  
      home = functions.get_deepface_home()
  
      if (
          os.path.isfile(
              home
              + "/.deepface/weights/VGGFace2_DeepFace_weights_val-0.9034.h5"
          )
          != True
      ):
          logger.info(
              "VGGFace2_DeepFace_weights_val-0.9034.h5 will be downloaded..."
          )
  
          output = (
              home
              + "/.deepface/weights/VGGFace2_DeepFace_weights_val-0.9034.h5.zip"
          )
  
          gdown.download(url, output, quiet=False)
  
          # unzip VGGFace2_DeepFace_weights_val-0.9034.h5.zip
          with zipfile.ZipFile(output, "r") as zip_ref:
              zip_ref.extractall(home + "/.deepface/weights/")
  
      base_model.load_weights(
          home
          + "/.deepface/weights/VGGFace2_DeepFace_weights_val-0.9034.h5"
      )
  
      # drop F8 and D0. F7 is the representation layer.
      deepface_model = Model(
          inputs=base_model.layers[0].input,
          outputs=base_model.layers[-3].output,
      )
  
      return deepface_model
  
imports: import os
  import zipfile
  import gdown
  import tensorflow
  from deepface.commons import functions
  from deepface.commons.logger import Logger
label_desc: Use image node_id 1 for calling a global function with example usage: loadModel(url) and returns: deepface_model