code: def loadModel(
      url="https://github.com/serengil/deepface_models/releases/download/v1.0/openface_weights.h5",
  ) -> Model:
      myInput = Input(shape=(96, 96, 3))
  
      x = ZeroPadding2D(padding=(3, 3), input_shape=(96, 96, 3))(
          myInput
      )
      x = Conv2D(64, (7, 7), strides=(2, 2), name="conv1")(x)
      x = BatchNormalization(axis=3, epsilon=0.00001, name="bn1")(x)
      x = Activation("relu")(x)
      x = ZeroPadding2D(padding=(1, 1))(x)
      x = MaxPooling2D(pool_size=3, strides=2)(x)
      x = Lambda(
          lambda x: tf.nn.lrn(x, alpha=1e-4, beta=0.75), name="lrn_1"
      )(x)
      x = Conv2D(64, (1, 1), name="conv2")(x)
      x = BatchNormalization(axis=3, epsilon=0.00001, name="bn2")(x)
      x = Activation("relu")(x)
      x = ZeroPadding2D(padding=(1, 1))(x)
      x = Conv2D(192, (3, 3), name="conv3")(x)
      x = BatchNormalization(axis=3, epsilon=0.00001, name="bn3")(x)
      x = Activation("relu")(x)
      x = Lambda(
          lambda x: tf.nn.lrn(x, alpha=1e-4, beta=0.75), name="lrn_2"
      )(
          x
      )  # x is equal added
      x = ZeroPadding2D(padding=(1, 1))(x)
      x = MaxPooling2D(pool_size=3, strides=2)(x)
  
      # Inception3a
      inception_3a_3x3 = Conv2D(
          96, (1, 1), name="inception_3a_3x3_conv1"
      )(x)
      inception_3a_3x3 = BatchNormalization(
          axis=3, epsilon=0.00001, name="inception_3a_3x3_bn1"
      )(inception_3a_3x3)
      inception_3a_3x3 = Activation("relu")(inception_3a_3x3)
      inception_3a_3x3 = ZeroPadding2D(padding=(1, 1))(inception_3a_3x3)
      inception_3a_3x3 = Conv2D(
          128, (3, 3), name="inception_3a_3x3_conv2"
      )(inception_3a_3x3)
      inception_3a_3x3 = BatchNormalization(
          axis=3, epsilon=0.00001, name="inception_3a_3x3_bn2"
      )(inception_3a_3x3)
      inception_3a_3x3 = Activation("relu")(inception_3a_3x3)
  
      inception_3a_5x5 = Conv2D(
          16, (1, 1), name="inception_3a_5x5_conv1"
      )(x)
      inception_3a_5x5 = BatchNormalization(
          axis=3, epsilon=0.00001, name="inception_3a_5x5_bn1"
      )(inception_3a_5x5)
      inception_3a_5x5 = Activation("relu")(inception_3a_5x5)
      inception_3a_5x5 = ZeroPadding2D(padding=(2, 2))(inception_3a_5x5)
      inception_3a_5x5 = Conv2D(
          32, (5, 5), name="inception_3a_5x5_conv2"
      )(inception_3a_5x5)
      inception_3a_5x5 = BatchNormalization(
          axis=3, epsilon=0.00001, name="inception_3a_5x5_bn2"
      )(inception_3a_5x5)
      inception_3a_5x5 = Activation("relu")(inception_3a_5x5)
  
      inception_3a_pool = MaxPooling2D(pool_size=3, strides=2)(x)
      inception_3a_pool = Conv2D(
          32, (1, 1), name="inception_3a_pool_conv"
      )(inception_3a_pool)
      inception_3a_pool = BatchNormalization(
          axis=3, epsilon=0.00001, name="inception_3a_pool_bn"
      )(inception_3a_pool)
      inception_3a_pool = Activation("relu")(inception_3a_pool)
      inception_3a_pool = ZeroPadding2D(padding=((3, 4), (3, 4)))(
          inception_3a_pool
      )
  
      inception_3a_1x1 = Conv2D(
          64, (1, 1), name="inception_3a_1x1_conv"
      )(x)
      inception_3a_1x1 = BatchNormalization(
          axis=3, epsilon=0.00001, name="inception_3a_1x1_bn"
      )(inception_3a_1x1)
      inception_3a_1x1 = Activation("relu")(inception_3a_1x1)
  
      inception_3a = concatenate(
          [
              inception_3a_3x3,
              inception_3a_5x5,
              inception_3a_pool,
              inception_3a_1x1,
          ],
          axis=3,
      )
  
      # Inception3b
      inception_3b_3x3 = Conv2D(
          96, (1, 1), name="inception_3b_3x3_conv1"
      )(inception_3a)
      inception_3b_3x3 = BatchNormalization(
          axis=3, epsilon=0.00001, name="inception_3b_3x3_bn1"
      )(inception_3b_3x3)
      inception_3b_3x3 = Activation("relu")(inception_3b_3x3)
      inception_3b_3x3 = ZeroPadding2D(padding=(1, 1))(inception_3b_3x3)
      inception_3b_3x3 = Conv2D(
          128, (3, 3), name="inception_3b_3x3_conv2"
      )(inception_3b_3x3)
      inception_3b_3x3 = BatchNormalization(
          axis=3, epsilon=0.00001, name="inception_3b_3x3_bn2"
      )(inception_3b_3x3)
      inception_3b_3x3 = Activation("relu")(inception_3b_3x3)
  
      inception_3b_5x5 = Conv2D(
          32, (1, 1), name="inception_3b_5x5_conv1"
      )(inception_3a)
      inception_3b_5x5 = BatchNormalization(
          axis=3, epsilon=0.00001, name="inception_3b_5x5_bn1"
      )(inception_3b_5x5)
      inception_3b_5x5 = Activation("relu")(inception_3b_5x5)
      inception_3b_5x5 = ZeroPadding2D(padding=(2, 2))(inception_3b_5x5)
      inception_3b_5x5 = Conv2D(
          64, (5, 5), name="inception_3b_5x5_conv2"
      )(inception_3b_5x5)
      inception_3b_5x5 = BatchNormalization(
          axis=3, epsilon=0.00001, name="inception_3b_5x5_bn2"
      )(inception_3b_5x5)
      inception_3b_5x5 = Activation("relu")(inception_3b_5x5)
  
      inception_3b_pool = Lambda(lambda x: x**2, name="power2_3b")(
          inception_3a
      )
      inception_3b_pool = AveragePooling2D(
          pool_size=(3, 3), strides=(3, 3)
      )(inception_3b_pool)
      inception_3b_pool = Lambda(lambda x: x * 9, name="mult9_3b")(
          inception_3b_pool
      )
      inception_3b_pool = Lambda(lambda x: K.sqrt(x), name="sqrt_3b")(
          inception_3b_pool
      )
      inception_3b_pool = Conv2D(
          64, (1, 1), name="inception_3b_pool_conv"
      )(inception_3b_pool)
      inception_3b_pool = BatchNormalization(
          axis=3, epsilon=0.00001, name="inception_3b_pool_bn"
      )(inception_3b_pool)
      inception_3b_pool = Activation("relu")(inception_3b_pool)
      inception_3b_pool = ZeroPadding2D(padding=(4, 4))(
          inception_3b_pool
      )
  
      inception_3b_1x1 = Conv2D(
          64, (1, 1), name="inception_3b_1x1_conv"
      )(inception_3a)
      inception_3b_1x1 = BatchNormalization(
          axis=3, epsilon=0.00001, name="inception_3b_1x1_bn"
      )(inception_3b_1x1)
      inception_3b_1x1 = Activation("relu")(inception_3b_1x1)
  
      inception_3b = concatenate(
          [
              inception_3b_3x3,
              inception_3b_5x5,
              inception_3b_pool,
              inception_3b_1x1,
          ],
          axis=3,
      )
  
      # Inception3c
      inception_3c_3x3 = Conv2D(
          128, (1, 1), strides=(1, 1), name="inception_3c_3x3_conv1"
      )(inception_3b)
      inception_3c_3x3 = BatchNormalization(
          axis=3, epsilon=0.00001, name="inception_3c_3x3_bn1"
      )(inception_3c_3x3)
      inception_3c_3x3 = Activation("relu")(inception_3c_3x3)
      inception_3c_3x3 = ZeroPadding2D(padding=(1, 1))(inception_3c_3x3)
      inception_3c_3x3 = Conv2D(
          256,
          (3, 3),
          strides=(2, 2),
          name="inception_3c_3x3_conv" + "2",
      )(inception_3c_3x3)
      inception_3c_3x3 = BatchNormalization(
          axis=3, epsilon=0.00001, name="inception_3c_3x3_bn" + "2"
      )(inception_3c_3x3)
      inception_3c_3x3 = Activation("relu")(inception_3c_3x3)
  
      inception_3c_5x5 = Conv2D(
          32, (1, 1), strides=(1, 1), name="inception_3c_5x5_conv1"
      )(inception_3b)
      inception_3c_5x5 = BatchNormalization(
          axis=3, epsilon=0.00001, name="inception_3c_5x5_bn1"
      )(inception_3c_5x5)
      inception_3c_5x5 = Activation("relu")(inception_3c_5x5)
      inception_3c_5x5 = ZeroPadding2D(padding=(2, 2))(inception_3c_5x5)
      inception_3c_5x5 = Conv2D(
          64, (5, 5), strides=(2, 2), name="inception_3c_5x5_conv" + "2"
      )(inception_3c_5x5)
      inception_3c_5x5 = BatchNormalization(
          axis=3, epsilon=0.00001, name="inception_3c_5x5_bn" + "2"
      )(inception_3c_5x5)
      inception_3c_5x5 = Activation("relu")(inception_3c_5x5)
  
      inception_3c_pool = MaxPooling2D(pool_size=3, strides=2)(
          inception_3b
      )
      inception_3c_pool = ZeroPadding2D(padding=((0, 1), (0, 1)))(
          inception_3c_pool
      )
  
      inception_3c = concatenate(
          [inception_3c_3x3, inception_3c_5x5, inception_3c_pool],
          axis=3,
      )
  
      # inception 4a
      inception_4a_3x3 = Conv2D(
          96, (1, 1), strides=(1, 1), name="inception_4a_3x3_conv" + "1"
      )(inception_3c)
      inception_4a_3x3 = BatchNormalization(
          axis=3, epsilon=0.00001, name="inception_4a_3x3_bn" + "1"
      )(inception_4a_3x3)
      inception_4a_3x3 = Activation("relu")(inception_4a_3x3)
      inception_4a_3x3 = ZeroPadding2D(padding=(1, 1))(inception_4a_3x3)
      inception_4a_3x3 = Conv2D(
          192,
          (3, 3),
          strides=(1, 1),
          name="inception_4a_3x3_conv" + "2",
      )(inception_4a_3x3)
      inception_4a_3x3 = BatchNormalization(
          axis=3, epsilon=0.00001, name="inception_4a_3x3_bn" + "2"
      )(inception_4a_3x3)
      inception_4a_3x3 = Activation("relu")(inception_4a_3x3)
  
      inception_4a_5x5 = Conv2D(
          32, (1, 1), strides=(1, 1), name="inception_4a_5x5_conv1"
      )(inception_3c)
      inception_4a_5x5 = BatchNormalization(
          axis=3, epsilon=0.00001, name="inception_4a_5x5_bn1"
      )(inception_4a_5x5)
      inception_4a_5x5 = Activation("relu")(inception_4a_5x5)
      inception_4a_5x5 = ZeroPadding2D(padding=(2, 2))(inception_4a_5x5)
      inception_4a_5x5 = Conv2D(
          64, (5, 5), strides=(1, 1), name="inception_4a_5x5_conv" + "2"
      )(inception_4a_5x5)
      inception_4a_5x5 = BatchNormalization(
          axis=3, epsilon=0.00001, name="inception_4a_5x5_bn" + "2"
      )(inception_4a_5x5)
      inception_4a_5x5 = Activation("relu")(inception_4a_5x5)
  
      inception_4a_pool = Lambda(lambda x: x**2, name="power2_4a")(
          inception_3c
      )
      inception_4a_pool = AveragePooling2D(
          pool_size=(3, 3), strides=(3, 3)
      )(inception_4a_pool)
      inception_4a_pool = Lambda(lambda x: x * 9, name="mult9_4a")(
          inception_4a_pool
      )
      inception_4a_pool = Lambda(lambda x: K.sqrt(x), name="sqrt_4a")(
          inception_4a_pool
      )
  
      inception_4a_pool = Conv2D(
          128,
          (1, 1),
          strides=(1, 1),
          name="inception_4a_pool_conv" + "",
      )(inception_4a_pool)
      inception_4a_pool = BatchNormalization(
          axis=3, epsilon=0.00001, name="inception_4a_pool_bn" + ""
      )(inception_4a_pool)
      inception_4a_pool = Activation("relu")(inception_4a_pool)
      inception_4a_pool = ZeroPadding2D(padding=(2, 2))(
          inception_4a_pool
      )
  
      inception_4a_1x1 = Conv2D(
          256, (1, 1), strides=(1, 1), name="inception_4a_1x1_conv" + ""
      )(inception_3c)
      inception_4a_1x1 = BatchNormalization(
          axis=3, epsilon=0.00001, name="inception_4a_1x1_bn" + ""
      )(inception_4a_1x1)
      inception_4a_1x1 = Activation("relu")(inception_4a_1x1)
  
      inception_4a = concatenate(
          [
              inception_4a_3x3,
              inception_4a_5x5,
              inception_4a_pool,
              inception_4a_1x1,
          ],
          axis=3,
      )
  
      # inception4e
      inception_4e_3x3 = Conv2D(
          160,
          (1, 1),
          strides=(1, 1),
          name="inception_4e_3x3_conv" + "1",
      )(inception_4a)
      inception_4e_3x3 = BatchNormalization(
          axis=3, epsilon=0.00001, name="inception_4e_3x3_bn" + "1"
      )(inception_4e_3x3)
      inception_4e_3x3 = Activation("relu")(inception_4e_3x3)
      inception_4e_3x3 = ZeroPadding2D(padding=(1, 1))(inception_4e_3x3)
      inception_4e_3x3 = Conv2D(
          256,
          (3, 3),
          strides=(2, 2),
          name="inception_4e_3x3_conv" + "2",
      )(inception_4e_3x3)
      inception_4e_3x3 = BatchNormalization(
          axis=3, epsilon=0.00001, name="inception_4e_3x3_bn" + "2"
      )(inception_4e_3x3)
      inception_4e_3x3 = Activation("relu")(inception_4e_3x3)
  
      inception_4e_5x5 = Conv2D(
          64, (1, 1), strides=(1, 1), name="inception_4e_5x5_conv" + "1"
      )(inception_4a)
      inception_4e_5x5 = BatchNormalization(
          axis=3, epsilon=0.00001, name="inception_4e_5x5_bn" + "1"
      )(inception_4e_5x5)
      inception_4e_5x5 = Activation("relu")(inception_4e_5x5)
      inception_4e_5x5 = ZeroPadding2D(padding=(2, 2))(inception_4e_5x5)
      inception_4e_5x5 = Conv2D(
          128,
          (5, 5),
          strides=(2, 2),
          name="inception_4e_5x5_conv" + "2",
      )(inception_4e_5x5)
      inception_4e_5x5 = BatchNormalization(
          axis=3, epsilon=0.00001, name="inception_4e_5x5_bn" + "2"
      )(inception_4e_5x5)
      inception_4e_5x5 = Activation("relu")(inception_4e_5x5)
  
      inception_4e_pool = MaxPooling2D(pool_size=3, strides=2)(
          inception_4a
      )
      inception_4e_pool = ZeroPadding2D(padding=((0, 1), (0, 1)))(
          inception_4e_pool
      )
  
      inception_4e = concatenate(
          [inception_4e_3x3, inception_4e_5x5, inception_4e_pool],
          axis=3,
      )
  
      # inception5a
      inception_5a_3x3 = Conv2D(
          96, (1, 1), strides=(1, 1), name="inception_5a_3x3_conv" + "1"
      )(inception_4e)
      inception_5a_3x3 = BatchNormalization(
          axis=3, epsilon=0.00001, name="inception_5a_3x3_bn" + "1"
      )(inception_5a_3x3)
      inception_5a_3x3 = Activation("relu")(inception_5a_3x3)
      inception_5a_3x3 = ZeroPadding2D(padding=(1, 1))(inception_5a_3x3)
      inception_5a_3x3 = Conv2D(
          384,
          (3, 3),
          strides=(1, 1),
          name="inception_5a_3x3_conv" + "2",
      )(inception_5a_3x3)
      inception_5a_3x3 = BatchNormalization(
          axis=3, epsilon=0.00001, name="inception_5a_3x3_bn" + "2"
      )(inception_5a_3x3)
      inception_5a_3x3 = Activation("relu")(inception_5a_3x3)
  
      inception_5a_pool = Lambda(lambda x: x**2, name="power2_5a")(
          inception_4e
      )
      inception_5a_pool = AveragePooling2D(
          pool_size=(3, 3), strides=(3, 3)
      )(inception_5a_pool)
      inception_5a_pool = Lambda(lambda x: x * 9, name="mult9_5a")(
          inception_5a_pool
      )
      inception_5a_pool = Lambda(lambda x: K.sqrt(x), name="sqrt_5a")(
          inception_5a_pool
      )
  
      inception_5a_pool = Conv2D(
          96, (1, 1), strides=(1, 1), name="inception_5a_pool_conv" + ""
      )(inception_5a_pool)
      inception_5a_pool = BatchNormalization(
          axis=3, epsilon=0.00001, name="inception_5a_pool_bn" + ""
      )(inception_5a_pool)
      inception_5a_pool = Activation("relu")(inception_5a_pool)
      inception_5a_pool = ZeroPadding2D(padding=(1, 1))(
          inception_5a_pool
      )
  
      inception_5a_1x1 = Conv2D(
          256, (1, 1), strides=(1, 1), name="inception_5a_1x1_conv" + ""
      )(inception_4e)
      inception_5a_1x1 = BatchNormalization(
          axis=3, epsilon=0.00001, name="inception_5a_1x1_bn" + ""
      )(inception_5a_1x1)
      inception_5a_1x1 = Activation("relu")(inception_5a_1x1)
  
      inception_5a = concatenate(
          [inception_5a_3x3, inception_5a_pool, inception_5a_1x1],
          axis=3,
      )
  
      # inception_5b
      inception_5b_3x3 = Conv2D(
          96, (1, 1), strides=(1, 1), name="inception_5b_3x3_conv" + "1"
      )(inception_5a)
      inception_5b_3x3 = BatchNormalization(
          axis=3, epsilon=0.00001, name="inception_5b_3x3_bn" + "1"
      )(inception_5b_3x3)
      inception_5b_3x3 = Activation("relu")(inception_5b_3x3)
      inception_5b_3x3 = ZeroPadding2D(padding=(1, 1))(inception_5b_3x3)
      inception_5b_3x3 = Conv2D(
          384,
          (3, 3),
          strides=(1, 1),
          name="inception_5b_3x3_conv" + "2",
      )(inception_5b_3x3)
      inception_5b_3x3 = BatchNormalization(
          axis=3, epsilon=0.00001, name="inception_5b_3x3_bn" + "2"
      )(inception_5b_3x3)
      inception_5b_3x3 = Activation("relu")(inception_5b_3x3)
  
      inception_5b_pool = MaxPooling2D(pool_size=3, strides=2)(
          inception_5a
      )
  
      inception_5b_pool = Conv2D(
          96, (1, 1), strides=(1, 1), name="inception_5b_pool_conv" + ""
      )(inception_5b_pool)
      inception_5b_pool = BatchNormalization(
          axis=3, epsilon=0.00001, name="inception_5b_pool_bn" + ""
      )(inception_5b_pool)
      inception_5b_pool = Activation("relu")(inception_5b_pool)
  
      inception_5b_pool = ZeroPadding2D(padding=(1, 1))(
          inception_5b_pool
      )
  
      inception_5b_1x1 = Conv2D(
          256, (1, 1), strides=(1, 1), name="inception_5b_1x1_conv" + ""
      )(inception_5a)
      inception_5b_1x1 = BatchNormalization(
          axis=3, epsilon=0.00001, name="inception_5b_1x1_bn" + ""
      )(inception_5b_1x1)
      inception_5b_1x1 = Activation("relu")(inception_5b_1x1)
  
      inception_5b = concatenate(
          [inception_5b_3x3, inception_5b_pool, inception_5b_1x1],
          axis=3,
      )
  
      av_pool = AveragePooling2D(pool_size=(3, 3), strides=(1, 1))(
          inception_5b
      )
      reshape_layer = Flatten()(av_pool)
      dense_layer = Dense(128, name="dense_layer")(reshape_layer)
      norm_layer = Lambda(
          lambda x: K.l2_normalize(x, axis=1), name="norm_layer"
      )(dense_layer)
  
      # Final Model
      model = Model(inputs=[myInput], outputs=norm_layer)
  
      # -----------------------------------
  
      home = functions.get_deepface_home()
  
      if (
          os.path.isfile(
              home + "/.deepface/weights/openface_weights.h5"
          )
          != True
      ):
          logger.info("openface_weights.h5 will be downloaded...")
  
          output = home + "/.deepface/weights/openface_weights.h5"
          gdown.download(url, output, quiet=False)
  
      # -----------------------------------
  
      model.load_weights(
          home + "/.deepface/weights/openface_weights.h5"
      )
  
      # -----------------------------------
  
      return model
  
imports: import os
  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: model