According to the `compute_content_cost`

function description the `a_C`

is supposed to have 4 dimensions:

a_C – tensor of dimension (1, n_H, n_W, n_C), hidden layer activations representing content of the image C

However the test input has one more dimension:

`a_C = tf.random.normal([1, 1, 4, 4, 3], mean=1, stddev=4)`

The dimension is then lost inside the function by indexing the last (and only) element of the first dimension: `a_C = content_output[-1]`

Questions:

- Is the function argument description mistaken by stating the 4-dimensional shape, when it seems to actually expect 5-dimensional tensor?
- What does the lost dimension stand for? I understand the remaining four are
`m, h, w, c`

as usual. - Why do we use this fifth dimension only to immediately lose it at the function’s beginning?