code: def InceptionResNetV2(dimension=128) -> Model:
      inputs = Input(shape=(160, 160, 3))
      x = Conv2D(
          32,
          3,
          strides=2,
          padding="valid",
          use_bias=False,
          name="Conv2d_1a_3x3",
      )(inputs)
      x = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Conv2d_1a_3x3_BatchNorm",
      )(x)
      x = Activation("relu", name="Conv2d_1a_3x3_Activation")(x)
      x = Conv2D(
          32,
          3,
          strides=1,
          padding="valid",
          use_bias=False,
          name="Conv2d_2a_3x3",
      )(x)
      x = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Conv2d_2a_3x3_BatchNorm",
      )(x)
      x = Activation("relu", name="Conv2d_2a_3x3_Activation")(x)
      x = Conv2D(
          64,
          3,
          strides=1,
          padding="same",
          use_bias=False,
          name="Conv2d_2b_3x3",
      )(x)
      x = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Conv2d_2b_3x3_BatchNorm",
      )(x)
      x = Activation("relu", name="Conv2d_2b_3x3_Activation")(x)
      x = MaxPooling2D(3, strides=2, name="MaxPool_3a_3x3")(x)
      x = Conv2D(
          80,
          1,
          strides=1,
          padding="valid",
          use_bias=False,
          name="Conv2d_3b_1x1",
      )(x)
      x = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Conv2d_3b_1x1_BatchNorm",
      )(x)
      x = Activation("relu", name="Conv2d_3b_1x1_Activation")(x)
      x = Conv2D(
          192,
          3,
          strides=1,
          padding="valid",
          use_bias=False,
          name="Conv2d_4a_3x3",
      )(x)
      x = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Conv2d_4a_3x3_BatchNorm",
      )(x)
      x = Activation("relu", name="Conv2d_4a_3x3_Activation")(x)
      x = Conv2D(
          256,
          3,
          strides=2,
          padding="valid",
          use_bias=False,
          name="Conv2d_4b_3x3",
      )(x)
      x = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Conv2d_4b_3x3_BatchNorm",
      )(x)
      x = Activation("relu", name="Conv2d_4b_3x3_Activation")(x)
  
      # 5x Block35 (Inception-ResNet-A block):
      branch_0 = Conv2D(
          32,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block35_1_Branch_0_Conv2d_1x1",
      )(x)
      branch_0 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block35_1_Branch_0_Conv2d_1x1_BatchNorm",
      )(branch_0)
      branch_0 = Activation(
          "relu", name="Block35_1_Branch_0_Conv2d_1x1_Activation"
      )(branch_0)
      branch_1 = Conv2D(
          32,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block35_1_Branch_1_Conv2d_0a_1x1",
      )(x)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block35_1_Branch_1_Conv2d_0a_1x1_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block35_1_Branch_1_Conv2d_0a_1x1_Activation"
      )(branch_1)
      branch_1 = Conv2D(
          32,
          3,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block35_1_Branch_1_Conv2d_0b_3x3",
      )(branch_1)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block35_1_Branch_1_Conv2d_0b_3x3_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block35_1_Branch_1_Conv2d_0b_3x3_Activation"
      )(branch_1)
      branch_2 = Conv2D(
          32,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block35_1_Branch_2_Conv2d_0a_1x1",
      )(x)
      branch_2 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block35_1_Branch_2_Conv2d_0a_1x1_BatchNorm",
      )(branch_2)
      branch_2 = Activation(
          "relu", name="Block35_1_Branch_2_Conv2d_0a_1x1_Activation"
      )(branch_2)
      branch_2 = Conv2D(
          32,
          3,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block35_1_Branch_2_Conv2d_0b_3x3",
      )(branch_2)
      branch_2 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block35_1_Branch_2_Conv2d_0b_3x3_BatchNorm",
      )(branch_2)
      branch_2 = Activation(
          "relu", name="Block35_1_Branch_2_Conv2d_0b_3x3_Activation"
      )(branch_2)
      branch_2 = Conv2D(
          32,
          3,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block35_1_Branch_2_Conv2d_0c_3x3",
      )(branch_2)
      branch_2 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block35_1_Branch_2_Conv2d_0c_3x3_BatchNorm",
      )(branch_2)
      branch_2 = Activation(
          "relu", name="Block35_1_Branch_2_Conv2d_0c_3x3_Activation"
      )(branch_2)
      branches = [branch_0, branch_1, branch_2]
      mixed = Concatenate(axis=3, name="Block35_1_Concatenate")(
          branches
      )
      up = Conv2D(
          256,
          1,
          strides=1,
          padding="same",
          use_bias=True,
          name="Block35_1_Conv2d_1x1",
      )(mixed)
      up = Lambda(
          scaling,
          output_shape=K.int_shape(up)[1:],
          arguments={"scale": 0.17},
      )(up)
      x = add([x, up])
      x = Activation("relu", name="Block35_1_Activation")(x)
  
      branch_0 = Conv2D(
          32,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block35_2_Branch_0_Conv2d_1x1",
      )(x)
      branch_0 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block35_2_Branch_0_Conv2d_1x1_BatchNorm",
      )(branch_0)
      branch_0 = Activation(
          "relu", name="Block35_2_Branch_0_Conv2d_1x1_Activation"
      )(branch_0)
      branch_1 = Conv2D(
          32,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block35_2_Branch_1_Conv2d_0a_1x1",
      )(x)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block35_2_Branch_1_Conv2d_0a_1x1_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block35_2_Branch_1_Conv2d_0a_1x1_Activation"
      )(branch_1)
      branch_1 = Conv2D(
          32,
          3,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block35_2_Branch_1_Conv2d_0b_3x3",
      )(branch_1)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block35_2_Branch_1_Conv2d_0b_3x3_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block35_2_Branch_1_Conv2d_0b_3x3_Activation"
      )(branch_1)
      branch_2 = Conv2D(
          32,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block35_2_Branch_2_Conv2d_0a_1x1",
      )(x)
      branch_2 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block35_2_Branch_2_Conv2d_0a_1x1_BatchNorm",
      )(branch_2)
      branch_2 = Activation(
          "relu", name="Block35_2_Branch_2_Conv2d_0a_1x1_Activation"
      )(branch_2)
      branch_2 = Conv2D(
          32,
          3,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block35_2_Branch_2_Conv2d_0b_3x3",
      )(branch_2)
      branch_2 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block35_2_Branch_2_Conv2d_0b_3x3_BatchNorm",
      )(branch_2)
      branch_2 = Activation(
          "relu", name="Block35_2_Branch_2_Conv2d_0b_3x3_Activation"
      )(branch_2)
      branch_2 = Conv2D(
          32,
          3,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block35_2_Branch_2_Conv2d_0c_3x3",
      )(branch_2)
      branch_2 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block35_2_Branch_2_Conv2d_0c_3x3_BatchNorm",
      )(branch_2)
      branch_2 = Activation(
          "relu", name="Block35_2_Branch_2_Conv2d_0c_3x3_Activation"
      )(branch_2)
      branches = [branch_0, branch_1, branch_2]
      mixed = Concatenate(axis=3, name="Block35_2_Concatenate")(
          branches
      )
      up = Conv2D(
          256,
          1,
          strides=1,
          padding="same",
          use_bias=True,
          name="Block35_2_Conv2d_1x1",
      )(mixed)
      up = Lambda(
          scaling,
          output_shape=K.int_shape(up)[1:],
          arguments={"scale": 0.17},
      )(up)
      x = add([x, up])
      x = Activation("relu", name="Block35_2_Activation")(x)
  
      branch_0 = Conv2D(
          32,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block35_3_Branch_0_Conv2d_1x1",
      )(x)
      branch_0 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block35_3_Branch_0_Conv2d_1x1_BatchNorm",
      )(branch_0)
      branch_0 = Activation(
          "relu", name="Block35_3_Branch_0_Conv2d_1x1_Activation"
      )(branch_0)
      branch_1 = Conv2D(
          32,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block35_3_Branch_1_Conv2d_0a_1x1",
      )(x)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block35_3_Branch_1_Conv2d_0a_1x1_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block35_3_Branch_1_Conv2d_0a_1x1_Activation"
      )(branch_1)
      branch_1 = Conv2D(
          32,
          3,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block35_3_Branch_1_Conv2d_0b_3x3",
      )(branch_1)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block35_3_Branch_1_Conv2d_0b_3x3_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block35_3_Branch_1_Conv2d_0b_3x3_Activation"
      )(branch_1)
      branch_2 = Conv2D(
          32,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block35_3_Branch_2_Conv2d_0a_1x1",
      )(x)
      branch_2 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block35_3_Branch_2_Conv2d_0a_1x1_BatchNorm",
      )(branch_2)
      branch_2 = Activation(
          "relu", name="Block35_3_Branch_2_Conv2d_0a_1x1_Activation"
      )(branch_2)
      branch_2 = Conv2D(
          32,
          3,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block35_3_Branch_2_Conv2d_0b_3x3",
      )(branch_2)
      branch_2 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block35_3_Branch_2_Conv2d_0b_3x3_BatchNorm",
      )(branch_2)
      branch_2 = Activation(
          "relu", name="Block35_3_Branch_2_Conv2d_0b_3x3_Activation"
      )(branch_2)
      branch_2 = Conv2D(
          32,
          3,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block35_3_Branch_2_Conv2d_0c_3x3",
      )(branch_2)
      branch_2 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block35_3_Branch_2_Conv2d_0c_3x3_BatchNorm",
      )(branch_2)
      branch_2 = Activation(
          "relu", name="Block35_3_Branch_2_Conv2d_0c_3x3_Activation"
      )(branch_2)
      branches = [branch_0, branch_1, branch_2]
      mixed = Concatenate(axis=3, name="Block35_3_Concatenate")(
          branches
      )
      up = Conv2D(
          256,
          1,
          strides=1,
          padding="same",
          use_bias=True,
          name="Block35_3_Conv2d_1x1",
      )(mixed)
      up = Lambda(
          scaling,
          output_shape=K.int_shape(up)[1:],
          arguments={"scale": 0.17},
      )(up)
      x = add([x, up])
      x = Activation("relu", name="Block35_3_Activation")(x)
  
      branch_0 = Conv2D(
          32,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block35_4_Branch_0_Conv2d_1x1",
      )(x)
      branch_0 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block35_4_Branch_0_Conv2d_1x1_BatchNorm",
      )(branch_0)
      branch_0 = Activation(
          "relu", name="Block35_4_Branch_0_Conv2d_1x1_Activation"
      )(branch_0)
      branch_1 = Conv2D(
          32,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block35_4_Branch_1_Conv2d_0a_1x1",
      )(x)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block35_4_Branch_1_Conv2d_0a_1x1_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block35_4_Branch_1_Conv2d_0a_1x1_Activation"
      )(branch_1)
      branch_1 = Conv2D(
          32,
          3,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block35_4_Branch_1_Conv2d_0b_3x3",
      )(branch_1)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block35_4_Branch_1_Conv2d_0b_3x3_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block35_4_Branch_1_Conv2d_0b_3x3_Activation"
      )(branch_1)
      branch_2 = Conv2D(
          32,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block35_4_Branch_2_Conv2d_0a_1x1",
      )(x)
      branch_2 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block35_4_Branch_2_Conv2d_0a_1x1_BatchNorm",
      )(branch_2)
      branch_2 = Activation(
          "relu", name="Block35_4_Branch_2_Conv2d_0a_1x1_Activation"
      )(branch_2)
      branch_2 = Conv2D(
          32,
          3,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block35_4_Branch_2_Conv2d_0b_3x3",
      )(branch_2)
      branch_2 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block35_4_Branch_2_Conv2d_0b_3x3_BatchNorm",
      )(branch_2)
      branch_2 = Activation(
          "relu", name="Block35_4_Branch_2_Conv2d_0b_3x3_Activation"
      )(branch_2)
      branch_2 = Conv2D(
          32,
          3,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block35_4_Branch_2_Conv2d_0c_3x3",
      )(branch_2)
      branch_2 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block35_4_Branch_2_Conv2d_0c_3x3_BatchNorm",
      )(branch_2)
      branch_2 = Activation(
          "relu", name="Block35_4_Branch_2_Conv2d_0c_3x3_Activation"
      )(branch_2)
      branches = [branch_0, branch_1, branch_2]
      mixed = Concatenate(axis=3, name="Block35_4_Concatenate")(
          branches
      )
      up = Conv2D(
          256,
          1,
          strides=1,
          padding="same",
          use_bias=True,
          name="Block35_4_Conv2d_1x1",
      )(mixed)
      up = Lambda(
          scaling,
          output_shape=K.int_shape(up)[1:],
          arguments={"scale": 0.17},
      )(up)
      x = add([x, up])
      x = Activation("relu", name="Block35_4_Activation")(x)
  
      branch_0 = Conv2D(
          32,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block35_5_Branch_0_Conv2d_1x1",
      )(x)
      branch_0 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block35_5_Branch_0_Conv2d_1x1_BatchNorm",
      )(branch_0)
      branch_0 = Activation(
          "relu", name="Block35_5_Branch_0_Conv2d_1x1_Activation"
      )(branch_0)
      branch_1 = Conv2D(
          32,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block35_5_Branch_1_Conv2d_0a_1x1",
      )(x)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block35_5_Branch_1_Conv2d_0a_1x1_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block35_5_Branch_1_Conv2d_0a_1x1_Activation"
      )(branch_1)
      branch_1 = Conv2D(
          32,
          3,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block35_5_Branch_1_Conv2d_0b_3x3",
      )(branch_1)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block35_5_Branch_1_Conv2d_0b_3x3_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block35_5_Branch_1_Conv2d_0b_3x3_Activation"
      )(branch_1)
      branch_2 = Conv2D(
          32,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block35_5_Branch_2_Conv2d_0a_1x1",
      )(x)
      branch_2 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block35_5_Branch_2_Conv2d_0a_1x1_BatchNorm",
      )(branch_2)
      branch_2 = Activation(
          "relu", name="Block35_5_Branch_2_Conv2d_0a_1x1_Activation"
      )(branch_2)
      branch_2 = Conv2D(
          32,
          3,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block35_5_Branch_2_Conv2d_0b_3x3",
      )(branch_2)
      branch_2 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block35_5_Branch_2_Conv2d_0b_3x3_BatchNorm",
      )(branch_2)
      branch_2 = Activation(
          "relu", name="Block35_5_Branch_2_Conv2d_0b_3x3_Activation"
      )(branch_2)
      branch_2 = Conv2D(
          32,
          3,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block35_5_Branch_2_Conv2d_0c_3x3",
      )(branch_2)
      branch_2 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block35_5_Branch_2_Conv2d_0c_3x3_BatchNorm",
      )(branch_2)
      branch_2 = Activation(
          "relu", name="Block35_5_Branch_2_Conv2d_0c_3x3_Activation"
      )(branch_2)
      branches = [branch_0, branch_1, branch_2]
      mixed = Concatenate(axis=3, name="Block35_5_Concatenate")(
          branches
      )
      up = Conv2D(
          256,
          1,
          strides=1,
          padding="same",
          use_bias=True,
          name="Block35_5_Conv2d_1x1",
      )(mixed)
      up = Lambda(
          scaling,
          output_shape=K.int_shape(up)[1:],
          arguments={"scale": 0.17},
      )(up)
      x = add([x, up])
      x = Activation("relu", name="Block35_5_Activation")(x)
  
      # Mixed 6a (Reduction-A block):
      branch_0 = Conv2D(
          384,
          3,
          strides=2,
          padding="valid",
          use_bias=False,
          name="Mixed_6a_Branch_0_Conv2d_1a_3x3",
      )(x)
      branch_0 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Mixed_6a_Branch_0_Conv2d_1a_3x3_BatchNorm",
      )(branch_0)
      branch_0 = Activation(
          "relu", name="Mixed_6a_Branch_0_Conv2d_1a_3x3_Activation"
      )(branch_0)
      branch_1 = Conv2D(
          192,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Mixed_6a_Branch_1_Conv2d_0a_1x1",
      )(x)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Mixed_6a_Branch_1_Conv2d_0a_1x1_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Mixed_6a_Branch_1_Conv2d_0a_1x1_Activation"
      )(branch_1)
      branch_1 = Conv2D(
          192,
          3,
          strides=1,
          padding="same",
          use_bias=False,
          name="Mixed_6a_Branch_1_Conv2d_0b_3x3",
      )(branch_1)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Mixed_6a_Branch_1_Conv2d_0b_3x3_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Mixed_6a_Branch_1_Conv2d_0b_3x3_Activation"
      )(branch_1)
      branch_1 = Conv2D(
          256,
          3,
          strides=2,
          padding="valid",
          use_bias=False,
          name="Mixed_6a_Branch_1_Conv2d_1a_3x3",
      )(branch_1)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Mixed_6a_Branch_1_Conv2d_1a_3x3_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Mixed_6a_Branch_1_Conv2d_1a_3x3_Activation"
      )(branch_1)
      branch_pool = MaxPooling2D(
          3,
          strides=2,
          padding="valid",
          name="Mixed_6a_Branch_2_MaxPool_1a_3x3",
      )(x)
      branches = [branch_0, branch_1, branch_pool]
      x = Concatenate(axis=3, name="Mixed_6a")(branches)
  
      # 10x Block17 (Inception-ResNet-B block):
      branch_0 = Conv2D(
          128,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block17_1_Branch_0_Conv2d_1x1",
      )(x)
      branch_0 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block17_1_Branch_0_Conv2d_1x1_BatchNorm",
      )(branch_0)
      branch_0 = Activation(
          "relu", name="Block17_1_Branch_0_Conv2d_1x1_Activation"
      )(branch_0)
      branch_1 = Conv2D(
          128,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block17_1_Branch_1_Conv2d_0a_1x1",
      )(x)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block17_1_Branch_1_Conv2d_0a_1x1_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block17_1_Branch_1_Conv2d_0a_1x1_Activation"
      )(branch_1)
      branch_1 = Conv2D(
          128,
          [1, 7],
          strides=1,
          padding="same",
          use_bias=False,
          name="Block17_1_Branch_1_Conv2d_0b_1x7",
      )(branch_1)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block17_1_Branch_1_Conv2d_0b_1x7_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block17_1_Branch_1_Conv2d_0b_1x7_Activation"
      )(branch_1)
      branch_1 = Conv2D(
          128,
          [7, 1],
          strides=1,
          padding="same",
          use_bias=False,
          name="Block17_1_Branch_1_Conv2d_0c_7x1",
      )(branch_1)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block17_1_Branch_1_Conv2d_0c_7x1_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block17_1_Branch_1_Conv2d_0c_7x1_Activation"
      )(branch_1)
      branches = [branch_0, branch_1]
      mixed = Concatenate(axis=3, name="Block17_1_Concatenate")(
          branches
      )
      up = Conv2D(
          896,
          1,
          strides=1,
          padding="same",
          use_bias=True,
          name="Block17_1_Conv2d_1x1",
      )(mixed)
      up = Lambda(
          scaling,
          output_shape=K.int_shape(up)[1:],
          arguments={"scale": 0.1},
      )(up)
      x = add([x, up])
      x = Activation("relu", name="Block17_1_Activation")(x)
  
      branch_0 = Conv2D(
          128,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block17_2_Branch_0_Conv2d_1x1",
      )(x)
      branch_0 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block17_2_Branch_0_Conv2d_1x1_BatchNorm",
      )(branch_0)
      branch_0 = Activation(
          "relu", name="Block17_2_Branch_0_Conv2d_1x1_Activation"
      )(branch_0)
      branch_1 = Conv2D(
          128,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block17_2_Branch_2_Conv2d_0a_1x1",
      )(x)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block17_2_Branch_2_Conv2d_0a_1x1_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block17_2_Branch_2_Conv2d_0a_1x1_Activation"
      )(branch_1)
      branch_1 = Conv2D(
          128,
          [1, 7],
          strides=1,
          padding="same",
          use_bias=False,
          name="Block17_2_Branch_2_Conv2d_0b_1x7",
      )(branch_1)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block17_2_Branch_2_Conv2d_0b_1x7_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block17_2_Branch_2_Conv2d_0b_1x7_Activation"
      )(branch_1)
      branch_1 = Conv2D(
          128,
          [7, 1],
          strides=1,
          padding="same",
          use_bias=False,
          name="Block17_2_Branch_2_Conv2d_0c_7x1",
      )(branch_1)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block17_2_Branch_2_Conv2d_0c_7x1_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block17_2_Branch_2_Conv2d_0c_7x1_Activation"
      )(branch_1)
      branches = [branch_0, branch_1]
      mixed = Concatenate(axis=3, name="Block17_2_Concatenate")(
          branches
      )
      up = Conv2D(
          896,
          1,
          strides=1,
          padding="same",
          use_bias=True,
          name="Block17_2_Conv2d_1x1",
      )(mixed)
      up = Lambda(
          scaling,
          output_shape=K.int_shape(up)[1:],
          arguments={"scale": 0.1},
      )(up)
      x = add([x, up])
      x = Activation("relu", name="Block17_2_Activation")(x)
  
      branch_0 = Conv2D(
          128,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block17_3_Branch_0_Conv2d_1x1",
      )(x)
      branch_0 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block17_3_Branch_0_Conv2d_1x1_BatchNorm",
      )(branch_0)
      branch_0 = Activation(
          "relu", name="Block17_3_Branch_0_Conv2d_1x1_Activation"
      )(branch_0)
      branch_1 = Conv2D(
          128,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block17_3_Branch_3_Conv2d_0a_1x1",
      )(x)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block17_3_Branch_3_Conv2d_0a_1x1_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block17_3_Branch_3_Conv2d_0a_1x1_Activation"
      )(branch_1)
      branch_1 = Conv2D(
          128,
          [1, 7],
          strides=1,
          padding="same",
          use_bias=False,
          name="Block17_3_Branch_3_Conv2d_0b_1x7",
      )(branch_1)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block17_3_Branch_3_Conv2d_0b_1x7_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block17_3_Branch_3_Conv2d_0b_1x7_Activation"
      )(branch_1)
      branch_1 = Conv2D(
          128,
          [7, 1],
          strides=1,
          padding="same",
          use_bias=False,
          name="Block17_3_Branch_3_Conv2d_0c_7x1",
      )(branch_1)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block17_3_Branch_3_Conv2d_0c_7x1_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block17_3_Branch_3_Conv2d_0c_7x1_Activation"
      )(branch_1)
      branches = [branch_0, branch_1]
      mixed = Concatenate(axis=3, name="Block17_3_Concatenate")(
          branches
      )
      up = Conv2D(
          896,
          1,
          strides=1,
          padding="same",
          use_bias=True,
          name="Block17_3_Conv2d_1x1",
      )(mixed)
      up = Lambda(
          scaling,
          output_shape=K.int_shape(up)[1:],
          arguments={"scale": 0.1},
      )(up)
      x = add([x, up])
      x = Activation("relu", name="Block17_3_Activation")(x)
  
      branch_0 = Conv2D(
          128,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block17_4_Branch_0_Conv2d_1x1",
      )(x)
      branch_0 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block17_4_Branch_0_Conv2d_1x1_BatchNorm",
      )(branch_0)
      branch_0 = Activation(
          "relu", name="Block17_4_Branch_0_Conv2d_1x1_Activation"
      )(branch_0)
      branch_1 = Conv2D(
          128,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block17_4_Branch_4_Conv2d_0a_1x1",
      )(x)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block17_4_Branch_4_Conv2d_0a_1x1_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block17_4_Branch_4_Conv2d_0a_1x1_Activation"
      )(branch_1)
      branch_1 = Conv2D(
          128,
          [1, 7],
          strides=1,
          padding="same",
          use_bias=False,
          name="Block17_4_Branch_4_Conv2d_0b_1x7",
      )(branch_1)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block17_4_Branch_4_Conv2d_0b_1x7_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block17_4_Branch_4_Conv2d_0b_1x7_Activation"
      )(branch_1)
      branch_1 = Conv2D(
          128,
          [7, 1],
          strides=1,
          padding="same",
          use_bias=False,
          name="Block17_4_Branch_4_Conv2d_0c_7x1",
      )(branch_1)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block17_4_Branch_4_Conv2d_0c_7x1_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block17_4_Branch_4_Conv2d_0c_7x1_Activation"
      )(branch_1)
      branches = [branch_0, branch_1]
      mixed = Concatenate(axis=3, name="Block17_4_Concatenate")(
          branches
      )
      up = Conv2D(
          896,
          1,
          strides=1,
          padding="same",
          use_bias=True,
          name="Block17_4_Conv2d_1x1",
      )(mixed)
      up = Lambda(
          scaling,
          output_shape=K.int_shape(up)[1:],
          arguments={"scale": 0.1},
      )(up)
      x = add([x, up])
      x = Activation("relu", name="Block17_4_Activation")(x)
  
      branch_0 = Conv2D(
          128,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block17_5_Branch_0_Conv2d_1x1",
      )(x)
      branch_0 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block17_5_Branch_0_Conv2d_1x1_BatchNorm",
      )(branch_0)
      branch_0 = Activation(
          "relu", name="Block17_5_Branch_0_Conv2d_1x1_Activation"
      )(branch_0)
      branch_1 = Conv2D(
          128,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block17_5_Branch_5_Conv2d_0a_1x1",
      )(x)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block17_5_Branch_5_Conv2d_0a_1x1_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block17_5_Branch_5_Conv2d_0a_1x1_Activation"
      )(branch_1)
      branch_1 = Conv2D(
          128,
          [1, 7],
          strides=1,
          padding="same",
          use_bias=False,
          name="Block17_5_Branch_5_Conv2d_0b_1x7",
      )(branch_1)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block17_5_Branch_5_Conv2d_0b_1x7_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block17_5_Branch_5_Conv2d_0b_1x7_Activation"
      )(branch_1)
      branch_1 = Conv2D(
          128,
          [7, 1],
          strides=1,
          padding="same",
          use_bias=False,
          name="Block17_5_Branch_5_Conv2d_0c_7x1",
      )(branch_1)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block17_5_Branch_5_Conv2d_0c_7x1_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block17_5_Branch_5_Conv2d_0c_7x1_Activation"
      )(branch_1)
      branches = [branch_0, branch_1]
      mixed = Concatenate(axis=3, name="Block17_5_Concatenate")(
          branches
      )
      up = Conv2D(
          896,
          1,
          strides=1,
          padding="same",
          use_bias=True,
          name="Block17_5_Conv2d_1x1",
      )(mixed)
      up = Lambda(
          scaling,
          output_shape=K.int_shape(up)[1:],
          arguments={"scale": 0.1},
      )(up)
      x = add([x, up])
      x = Activation("relu", name="Block17_5_Activation")(x)
  
      branch_0 = Conv2D(
          128,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block17_6_Branch_0_Conv2d_1x1",
      )(x)
      branch_0 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block17_6_Branch_0_Conv2d_1x1_BatchNorm",
      )(branch_0)
      branch_0 = Activation(
          "relu", name="Block17_6_Branch_0_Conv2d_1x1_Activation"
      )(branch_0)
      branch_1 = Conv2D(
          128,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block17_6_Branch_6_Conv2d_0a_1x1",
      )(x)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block17_6_Branch_6_Conv2d_0a_1x1_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block17_6_Branch_6_Conv2d_0a_1x1_Activation"
      )(branch_1)
      branch_1 = Conv2D(
          128,
          [1, 7],
          strides=1,
          padding="same",
          use_bias=False,
          name="Block17_6_Branch_6_Conv2d_0b_1x7",
      )(branch_1)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block17_6_Branch_6_Conv2d_0b_1x7_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block17_6_Branch_6_Conv2d_0b_1x7_Activation"
      )(branch_1)
      branch_1 = Conv2D(
          128,
          [7, 1],
          strides=1,
          padding="same",
          use_bias=False,
          name="Block17_6_Branch_6_Conv2d_0c_7x1",
      )(branch_1)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block17_6_Branch_6_Conv2d_0c_7x1_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block17_6_Branch_6_Conv2d_0c_7x1_Activation"
      )(branch_1)
      branches = [branch_0, branch_1]
      mixed = Concatenate(axis=3, name="Block17_6_Concatenate")(
          branches
      )
      up = Conv2D(
          896,
          1,
          strides=1,
          padding="same",
          use_bias=True,
          name="Block17_6_Conv2d_1x1",
      )(mixed)
      up = Lambda(
          scaling,
          output_shape=K.int_shape(up)[1:],
          arguments={"scale": 0.1},
      )(up)
      x = add([x, up])
      x = Activation("relu", name="Block17_6_Activation")(x)
  
      branch_0 = Conv2D(
          128,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block17_7_Branch_0_Conv2d_1x1",
      )(x)
      branch_0 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block17_7_Branch_0_Conv2d_1x1_BatchNorm",
      )(branch_0)
      branch_0 = Activation(
          "relu", name="Block17_7_Branch_0_Conv2d_1x1_Activation"
      )(branch_0)
      branch_1 = Conv2D(
          128,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block17_7_Branch_7_Conv2d_0a_1x1",
      )(x)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block17_7_Branch_7_Conv2d_0a_1x1_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block17_7_Branch_7_Conv2d_0a_1x1_Activation"
      )(branch_1)
      branch_1 = Conv2D(
          128,
          [1, 7],
          strides=1,
          padding="same",
          use_bias=False,
          name="Block17_7_Branch_7_Conv2d_0b_1x7",
      )(branch_1)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block17_7_Branch_7_Conv2d_0b_1x7_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block17_7_Branch_7_Conv2d_0b_1x7_Activation"
      )(branch_1)
      branch_1 = Conv2D(
          128,
          [7, 1],
          strides=1,
          padding="same",
          use_bias=False,
          name="Block17_7_Branch_7_Conv2d_0c_7x1",
      )(branch_1)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block17_7_Branch_7_Conv2d_0c_7x1_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block17_7_Branch_7_Conv2d_0c_7x1_Activation"
      )(branch_1)
      branches = [branch_0, branch_1]
      mixed = Concatenate(axis=3, name="Block17_7_Concatenate")(
          branches
      )
      up = Conv2D(
          896,
          1,
          strides=1,
          padding="same",
          use_bias=True,
          name="Block17_7_Conv2d_1x1",
      )(mixed)
      up = Lambda(
          scaling,
          output_shape=K.int_shape(up)[1:],
          arguments={"scale": 0.1},
      )(up)
      x = add([x, up])
      x = Activation("relu", name="Block17_7_Activation")(x)
  
      branch_0 = Conv2D(
          128,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block17_8_Branch_0_Conv2d_1x1",
      )(x)
      branch_0 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block17_8_Branch_0_Conv2d_1x1_BatchNorm",
      )(branch_0)
      branch_0 = Activation(
          "relu", name="Block17_8_Branch_0_Conv2d_1x1_Activation"
      )(branch_0)
      branch_1 = Conv2D(
          128,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block17_8_Branch_8_Conv2d_0a_1x1",
      )(x)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block17_8_Branch_8_Conv2d_0a_1x1_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block17_8_Branch_8_Conv2d_0a_1x1_Activation"
      )(branch_1)
      branch_1 = Conv2D(
          128,
          [1, 7],
          strides=1,
          padding="same",
          use_bias=False,
          name="Block17_8_Branch_8_Conv2d_0b_1x7",
      )(branch_1)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block17_8_Branch_8_Conv2d_0b_1x7_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block17_8_Branch_8_Conv2d_0b_1x7_Activation"
      )(branch_1)
      branch_1 = Conv2D(
          128,
          [7, 1],
          strides=1,
          padding="same",
          use_bias=False,
          name="Block17_8_Branch_8_Conv2d_0c_7x1",
      )(branch_1)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block17_8_Branch_8_Conv2d_0c_7x1_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block17_8_Branch_8_Conv2d_0c_7x1_Activation"
      )(branch_1)
      branches = [branch_0, branch_1]
      mixed = Concatenate(axis=3, name="Block17_8_Concatenate")(
          branches
      )
      up = Conv2D(
          896,
          1,
          strides=1,
          padding="same",
          use_bias=True,
          name="Block17_8_Conv2d_1x1",
      )(mixed)
      up = Lambda(
          scaling,
          output_shape=K.int_shape(up)[1:],
          arguments={"scale": 0.1},
      )(up)
      x = add([x, up])
      x = Activation("relu", name="Block17_8_Activation")(x)
  
      branch_0 = Conv2D(
          128,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block17_9_Branch_0_Conv2d_1x1",
      )(x)
      branch_0 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block17_9_Branch_0_Conv2d_1x1_BatchNorm",
      )(branch_0)
      branch_0 = Activation(
          "relu", name="Block17_9_Branch_0_Conv2d_1x1_Activation"
      )(branch_0)
      branch_1 = Conv2D(
          128,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block17_9_Branch_9_Conv2d_0a_1x1",
      )(x)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block17_9_Branch_9_Conv2d_0a_1x1_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block17_9_Branch_9_Conv2d_0a_1x1_Activation"
      )(branch_1)
      branch_1 = Conv2D(
          128,
          [1, 7],
          strides=1,
          padding="same",
          use_bias=False,
          name="Block17_9_Branch_9_Conv2d_0b_1x7",
      )(branch_1)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block17_9_Branch_9_Conv2d_0b_1x7_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block17_9_Branch_9_Conv2d_0b_1x7_Activation"
      )(branch_1)
      branch_1 = Conv2D(
          128,
          [7, 1],
          strides=1,
          padding="same",
          use_bias=False,
          name="Block17_9_Branch_9_Conv2d_0c_7x1",
      )(branch_1)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block17_9_Branch_9_Conv2d_0c_7x1_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block17_9_Branch_9_Conv2d_0c_7x1_Activation"
      )(branch_1)
      branches = [branch_0, branch_1]
      mixed = Concatenate(axis=3, name="Block17_9_Concatenate")(
          branches
      )
      up = Conv2D(
          896,
          1,
          strides=1,
          padding="same",
          use_bias=True,
          name="Block17_9_Conv2d_1x1",
      )(mixed)
      up = Lambda(
          scaling,
          output_shape=K.int_shape(up)[1:],
          arguments={"scale": 0.1},
      )(up)
      x = add([x, up])
      x = Activation("relu", name="Block17_9_Activation")(x)
  
      branch_0 = Conv2D(
          128,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block17_10_Branch_0_Conv2d_1x1",
      )(x)
      branch_0 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block17_10_Branch_0_Conv2d_1x1_BatchNorm",
      )(branch_0)
      branch_0 = Activation(
          "relu", name="Block17_10_Branch_0_Conv2d_1x1_Activation"
      )(branch_0)
      branch_1 = Conv2D(
          128,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block17_10_Branch_10_Conv2d_0a_1x1",
      )(x)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block17_10_Branch_10_Conv2d_0a_1x1_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block17_10_Branch_10_Conv2d_0a_1x1_Activation"
      )(branch_1)
      branch_1 = Conv2D(
          128,
          [1, 7],
          strides=1,
          padding="same",
          use_bias=False,
          name="Block17_10_Branch_10_Conv2d_0b_1x7",
      )(branch_1)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block17_10_Branch_10_Conv2d_0b_1x7_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block17_10_Branch_10_Conv2d_0b_1x7_Activation"
      )(branch_1)
      branch_1 = Conv2D(
          128,
          [7, 1],
          strides=1,
          padding="same",
          use_bias=False,
          name="Block17_10_Branch_10_Conv2d_0c_7x1",
      )(branch_1)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block17_10_Branch_10_Conv2d_0c_7x1_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block17_10_Branch_10_Conv2d_0c_7x1_Activation"
      )(branch_1)
      branches = [branch_0, branch_1]
      mixed = Concatenate(axis=3, name="Block17_10_Concatenate")(
          branches
      )
      up = Conv2D(
          896,
          1,
          strides=1,
          padding="same",
          use_bias=True,
          name="Block17_10_Conv2d_1x1",
      )(mixed)
      up = Lambda(
          scaling,
          output_shape=K.int_shape(up)[1:],
          arguments={"scale": 0.1},
      )(up)
      x = add([x, up])
      x = Activation("relu", name="Block17_10_Activation")(x)
  
      # Mixed 7a (Reduction-B block): 8 x 8 x 2080
      branch_0 = Conv2D(
          256,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Mixed_7a_Branch_0_Conv2d_0a_1x1",
      )(x)
      branch_0 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Mixed_7a_Branch_0_Conv2d_0a_1x1_BatchNorm",
      )(branch_0)
      branch_0 = Activation(
          "relu", name="Mixed_7a_Branch_0_Conv2d_0a_1x1_Activation"
      )(branch_0)
      branch_0 = Conv2D(
          384,
          3,
          strides=2,
          padding="valid",
          use_bias=False,
          name="Mixed_7a_Branch_0_Conv2d_1a_3x3",
      )(branch_0)
      branch_0 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Mixed_7a_Branch_0_Conv2d_1a_3x3_BatchNorm",
      )(branch_0)
      branch_0 = Activation(
          "relu", name="Mixed_7a_Branch_0_Conv2d_1a_3x3_Activation"
      )(branch_0)
      branch_1 = Conv2D(
          256,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Mixed_7a_Branch_1_Conv2d_0a_1x1",
      )(x)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Mixed_7a_Branch_1_Conv2d_0a_1x1_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Mixed_7a_Branch_1_Conv2d_0a_1x1_Activation"
      )(branch_1)
      branch_1 = Conv2D(
          256,
          3,
          strides=2,
          padding="valid",
          use_bias=False,
          name="Mixed_7a_Branch_1_Conv2d_1a_3x3",
      )(branch_1)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Mixed_7a_Branch_1_Conv2d_1a_3x3_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Mixed_7a_Branch_1_Conv2d_1a_3x3_Activation"
      )(branch_1)
      branch_2 = Conv2D(
          256,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Mixed_7a_Branch_2_Conv2d_0a_1x1",
      )(x)
      branch_2 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Mixed_7a_Branch_2_Conv2d_0a_1x1_BatchNorm",
      )(branch_2)
      branch_2 = Activation(
          "relu", name="Mixed_7a_Branch_2_Conv2d_0a_1x1_Activation"
      )(branch_2)
      branch_2 = Conv2D(
          256,
          3,
          strides=1,
          padding="same",
          use_bias=False,
          name="Mixed_7a_Branch_2_Conv2d_0b_3x3",
      )(branch_2)
      branch_2 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Mixed_7a_Branch_2_Conv2d_0b_3x3_BatchNorm",
      )(branch_2)
      branch_2 = Activation(
          "relu", name="Mixed_7a_Branch_2_Conv2d_0b_3x3_Activation"
      )(branch_2)
      branch_2 = Conv2D(
          256,
          3,
          strides=2,
          padding="valid",
          use_bias=False,
          name="Mixed_7a_Branch_2_Conv2d_1a_3x3",
      )(branch_2)
      branch_2 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Mixed_7a_Branch_2_Conv2d_1a_3x3_BatchNorm",
      )(branch_2)
      branch_2 = Activation(
          "relu", name="Mixed_7a_Branch_2_Conv2d_1a_3x3_Activation"
      )(branch_2)
      branch_pool = MaxPooling2D(
          3,
          strides=2,
          padding="valid",
          name="Mixed_7a_Branch_3_MaxPool_1a_3x3",
      )(x)
      branches = [branch_0, branch_1, branch_2, branch_pool]
      x = Concatenate(axis=3, name="Mixed_7a")(branches)
  
      # 5x Block8 (Inception-ResNet-C block):
  
      branch_0 = Conv2D(
          192,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block8_1_Branch_0_Conv2d_1x1",
      )(x)
      branch_0 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block8_1_Branch_0_Conv2d_1x1_BatchNorm",
      )(branch_0)
      branch_0 = Activation(
          "relu", name="Block8_1_Branch_0_Conv2d_1x1_Activation"
      )(branch_0)
      branch_1 = Conv2D(
          192,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block8_1_Branch_1_Conv2d_0a_1x1",
      )(x)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block8_1_Branch_1_Conv2d_0a_1x1_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block8_1_Branch_1_Conv2d_0a_1x1_Activation"
      )(branch_1)
      branch_1 = Conv2D(
          192,
          [1, 3],
          strides=1,
          padding="same",
          use_bias=False,
          name="Block8_1_Branch_1_Conv2d_0b_1x3",
      )(branch_1)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block8_1_Branch_1_Conv2d_0b_1x3_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block8_1_Branch_1_Conv2d_0b_1x3_Activation"
      )(branch_1)
      branch_1 = Conv2D(
          192,
          [3, 1],
          strides=1,
          padding="same",
          use_bias=False,
          name="Block8_1_Branch_1_Conv2d_0c_3x1",
      )(branch_1)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block8_1_Branch_1_Conv2d_0c_3x1_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block8_1_Branch_1_Conv2d_0c_3x1_Activation"
      )(branch_1)
      branches = [branch_0, branch_1]
      mixed = Concatenate(axis=3, name="Block8_1_Concatenate")(branches)
      up = Conv2D(
          1792,
          1,
          strides=1,
          padding="same",
          use_bias=True,
          name="Block8_1_Conv2d_1x1",
      )(mixed)
      up = Lambda(
          scaling,
          output_shape=K.int_shape(up)[1:],
          arguments={"scale": 0.2},
      )(up)
      x = add([x, up])
      x = Activation("relu", name="Block8_1_Activation")(x)
  
      branch_0 = Conv2D(
          192,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block8_2_Branch_0_Conv2d_1x1",
      )(x)
      branch_0 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block8_2_Branch_0_Conv2d_1x1_BatchNorm",
      )(branch_0)
      branch_0 = Activation(
          "relu", name="Block8_2_Branch_0_Conv2d_1x1_Activation"
      )(branch_0)
      branch_1 = Conv2D(
          192,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block8_2_Branch_2_Conv2d_0a_1x1",
      )(x)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block8_2_Branch_2_Conv2d_0a_1x1_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block8_2_Branch_2_Conv2d_0a_1x1_Activation"
      )(branch_1)
      branch_1 = Conv2D(
          192,
          [1, 3],
          strides=1,
          padding="same",
          use_bias=False,
          name="Block8_2_Branch_2_Conv2d_0b_1x3",
      )(branch_1)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block8_2_Branch_2_Conv2d_0b_1x3_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block8_2_Branch_2_Conv2d_0b_1x3_Activation"
      )(branch_1)
      branch_1 = Conv2D(
          192,
          [3, 1],
          strides=1,
          padding="same",
          use_bias=False,
          name="Block8_2_Branch_2_Conv2d_0c_3x1",
      )(branch_1)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block8_2_Branch_2_Conv2d_0c_3x1_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block8_2_Branch_2_Conv2d_0c_3x1_Activation"
      )(branch_1)
      branches = [branch_0, branch_1]
      mixed = Concatenate(axis=3, name="Block8_2_Concatenate")(branches)
      up = Conv2D(
          1792,
          1,
          strides=1,
          padding="same",
          use_bias=True,
          name="Block8_2_Conv2d_1x1",
      )(mixed)
      up = Lambda(
          scaling,
          output_shape=K.int_shape(up)[1:],
          arguments={"scale": 0.2},
      )(up)
      x = add([x, up])
      x = Activation("relu", name="Block8_2_Activation")(x)
  
      branch_0 = Conv2D(
          192,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block8_3_Branch_0_Conv2d_1x1",
      )(x)
      branch_0 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block8_3_Branch_0_Conv2d_1x1_BatchNorm",
      )(branch_0)
      branch_0 = Activation(
          "relu", name="Block8_3_Branch_0_Conv2d_1x1_Activation"
      )(branch_0)
      branch_1 = Conv2D(
          192,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block8_3_Branch_3_Conv2d_0a_1x1",
      )(x)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block8_3_Branch_3_Conv2d_0a_1x1_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block8_3_Branch_3_Conv2d_0a_1x1_Activation"
      )(branch_1)
      branch_1 = Conv2D(
          192,
          [1, 3],
          strides=1,
          padding="same",
          use_bias=False,
          name="Block8_3_Branch_3_Conv2d_0b_1x3",
      )(branch_1)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block8_3_Branch_3_Conv2d_0b_1x3_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block8_3_Branch_3_Conv2d_0b_1x3_Activation"
      )(branch_1)
      branch_1 = Conv2D(
          192,
          [3, 1],
          strides=1,
          padding="same",
          use_bias=False,
          name="Block8_3_Branch_3_Conv2d_0c_3x1",
      )(branch_1)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block8_3_Branch_3_Conv2d_0c_3x1_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block8_3_Branch_3_Conv2d_0c_3x1_Activation"
      )(branch_1)
      branches = [branch_0, branch_1]
      mixed = Concatenate(axis=3, name="Block8_3_Concatenate")(branches)
      up = Conv2D(
          1792,
          1,
          strides=1,
          padding="same",
          use_bias=True,
          name="Block8_3_Conv2d_1x1",
      )(mixed)
      up = Lambda(
          scaling,
          output_shape=K.int_shape(up)[1:],
          arguments={"scale": 0.2},
      )(up)
      x = add([x, up])
      x = Activation("relu", name="Block8_3_Activation")(x)
  
      branch_0 = Conv2D(
          192,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block8_4_Branch_0_Conv2d_1x1",
      )(x)
      branch_0 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block8_4_Branch_0_Conv2d_1x1_BatchNorm",
      )(branch_0)
      branch_0 = Activation(
          "relu", name="Block8_4_Branch_0_Conv2d_1x1_Activation"
      )(branch_0)
      branch_1 = Conv2D(
          192,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block8_4_Branch_4_Conv2d_0a_1x1",
      )(x)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block8_4_Branch_4_Conv2d_0a_1x1_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block8_4_Branch_4_Conv2d_0a_1x1_Activation"
      )(branch_1)
      branch_1 = Conv2D(
          192,
          [1, 3],
          strides=1,
          padding="same",
          use_bias=False,
          name="Block8_4_Branch_4_Conv2d_0b_1x3",
      )(branch_1)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block8_4_Branch_4_Conv2d_0b_1x3_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block8_4_Branch_4_Conv2d_0b_1x3_Activation"
      )(branch_1)
      branch_1 = Conv2D(
          192,
          [3, 1],
          strides=1,
          padding="same",
          use_bias=False,
          name="Block8_4_Branch_4_Conv2d_0c_3x1",
      )(branch_1)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block8_4_Branch_4_Conv2d_0c_3x1_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block8_4_Branch_4_Conv2d_0c_3x1_Activation"
      )(branch_1)
      branches = [branch_0, branch_1]
      mixed = Concatenate(axis=3, name="Block8_4_Concatenate")(branches)
      up = Conv2D(
          1792,
          1,
          strides=1,
          padding="same",
          use_bias=True,
          name="Block8_4_Conv2d_1x1",
      )(mixed)
      up = Lambda(
          scaling,
          output_shape=K.int_shape(up)[1:],
          arguments={"scale": 0.2},
      )(up)
      x = add([x, up])
      x = Activation("relu", name="Block8_4_Activation")(x)
  
      branch_0 = Conv2D(
          192,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block8_5_Branch_0_Conv2d_1x1",
      )(x)
      branch_0 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block8_5_Branch_0_Conv2d_1x1_BatchNorm",
      )(branch_0)
      branch_0 = Activation(
          "relu", name="Block8_5_Branch_0_Conv2d_1x1_Activation"
      )(branch_0)
      branch_1 = Conv2D(
          192,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block8_5_Branch_5_Conv2d_0a_1x1",
      )(x)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block8_5_Branch_5_Conv2d_0a_1x1_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block8_5_Branch_5_Conv2d_0a_1x1_Activation"
      )(branch_1)
      branch_1 = Conv2D(
          192,
          [1, 3],
          strides=1,
          padding="same",
          use_bias=False,
          name="Block8_5_Branch_5_Conv2d_0b_1x3",
      )(branch_1)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block8_5_Branch_5_Conv2d_0b_1x3_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block8_5_Branch_5_Conv2d_0b_1x3_Activation"
      )(branch_1)
      branch_1 = Conv2D(
          192,
          [3, 1],
          strides=1,
          padding="same",
          use_bias=False,
          name="Block8_5_Branch_5_Conv2d_0c_3x1",
      )(branch_1)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block8_5_Branch_5_Conv2d_0c_3x1_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block8_5_Branch_5_Conv2d_0c_3x1_Activation"
      )(branch_1)
      branches = [branch_0, branch_1]
      mixed = Concatenate(axis=3, name="Block8_5_Concatenate")(branches)
      up = Conv2D(
          1792,
          1,
          strides=1,
          padding="same",
          use_bias=True,
          name="Block8_5_Conv2d_1x1",
      )(mixed)
      up = Lambda(
          scaling,
          output_shape=K.int_shape(up)[1:],
          arguments={"scale": 0.2},
      )(up)
      x = add([x, up])
      x = Activation("relu", name="Block8_5_Activation")(x)
  
      branch_0 = Conv2D(
          192,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block8_6_Branch_0_Conv2d_1x1",
      )(x)
      branch_0 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block8_6_Branch_0_Conv2d_1x1_BatchNorm",
      )(branch_0)
      branch_0 = Activation(
          "relu", name="Block8_6_Branch_0_Conv2d_1x1_Activation"
      )(branch_0)
      branch_1 = Conv2D(
          192,
          1,
          strides=1,
          padding="same",
          use_bias=False,
          name="Block8_6_Branch_1_Conv2d_0a_1x1",
      )(x)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block8_6_Branch_1_Conv2d_0a_1x1_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block8_6_Branch_1_Conv2d_0a_1x1_Activation"
      )(branch_1)
      branch_1 = Conv2D(
          192,
          [1, 3],
          strides=1,
          padding="same",
          use_bias=False,
          name="Block8_6_Branch_1_Conv2d_0b_1x3",
      )(branch_1)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block8_6_Branch_1_Conv2d_0b_1x3_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block8_6_Branch_1_Conv2d_0b_1x3_Activation"
      )(branch_1)
      branch_1 = Conv2D(
          192,
          [3, 1],
          strides=1,
          padding="same",
          use_bias=False,
          name="Block8_6_Branch_1_Conv2d_0c_3x1",
      )(branch_1)
      branch_1 = BatchNormalization(
          axis=3,
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Block8_6_Branch_1_Conv2d_0c_3x1_BatchNorm",
      )(branch_1)
      branch_1 = Activation(
          "relu", name="Block8_6_Branch_1_Conv2d_0c_3x1_Activation"
      )(branch_1)
      branches = [branch_0, branch_1]
      mixed = Concatenate(axis=3, name="Block8_6_Concatenate")(branches)
      up = Conv2D(
          1792,
          1,
          strides=1,
          padding="same",
          use_bias=True,
          name="Block8_6_Conv2d_1x1",
      )(mixed)
      up = Lambda(
          scaling,
          output_shape=K.int_shape(up)[1:],
          arguments={"scale": 1},
      )(up)
      x = add([x, up])
  
      # Classification block
      x = GlobalAveragePooling2D(name="AvgPool")(x)
      x = Dropout(1.0 - 0.8, name="Dropout")(x)
      # Bottleneck
      x = Dense(dimension, use_bias=False, name="Bottleneck")(x)
      x = BatchNormalization(
          momentum=0.995,
          epsilon=0.001,
          scale=False,
          name="Bottleneck_BatchNorm",
      )(x)
  
      # Create model
      model = Model(inputs, x, name="inception_resnet_v1")
  
      return model
  
file_path: deepface/deepface/basemodels/Facenet.py
imports: import os
  import gdown
  import tensorflow
  from deepface.commons import functions
  from deepface.commons.logger import Logger
label_desc: Use image node_id 2 for calling a global function with example usage: InceptionResNetV2(dimension) and returns: model