Padding mask shape problem

Course Q&A Deep Learning Specialization Sequence Models
1

In 2.1 - Padding Mask the assiement shows us the difference between computing softmax directly and computing softmax with the addition of negative infinity.

print(tf.keras.activations.softmax(x))
print(tf.keras.activations.softmax(x + create_padding_mask(x) * -1.0e9))

The second line of code results in a shape problem ， the matrix A cannot be added to B
c,It should be changed to the following：
print(tf.keras.activations.softmax(x[:, tf.newaxis, tf.newaxis, :] + create_padding_mask(x) * -1.0e9))

2

I think you don’t need to create the padding mask. Isn’t it passed to the function as an argument?