I am not quite sure how to proceed, the instructions provided are not sufficient .

This is what I have done and stuck, please help me out!

### START CODE HERE

```
base_model = tf.keras.applications.MobileNetV2(input_shape=input_shape,
include_top=False, # <== Important!!!!
weights=None) # From imageNet
# freeze the base model by making it non trainable
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(x)
# 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.GlobalAveragePooling2D()(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)
outputs = tf.keras.layers.Dense(1)
### END CODE HERE
```

I have left weights value as None, what is it’s value?