base_model.trainable = False
# create the input layer (Same as the imageNetv2 input size)
inputs = tf.keras.Input(shape=input_shape)
# apply data augmentation to the inputs
x = data_augmentation(inputs)
# data preprocessing using the same weights the model was trained on
x = preprocess_input(inputs)
# set training to False to avoid keeping track of statistics in the batch norm layer
x = base_model(x, training=False)
# add the new Binary classification layers
# use global avg pooling to summarize the info in each channel
x = tf.keras.layers.GlobalAvgPool2D()(x)
# include dropout with probability of 0.2 to avoid overfitting
x = tf.keras.layers.Dropout(0.2)(x)
# use a prediction layer with one neuron (as a binary classifier only needs one)
prediction_layer = tf.keras.layers.Dense(1)
outputs=prediction_layer(x)
### END CODE HE
model = tf.keras.Model(inputs, outputs)
return model
pls help to write binary classification layers