A test for backward_propagation is erroring with:

```
<ipython-input-20-651db9ab4d4f> in backward_propagation(parameters, cache, X, Y)
53 db2 = 1/m * np.sum(dZ2, axis = 1, keepdims = True)
---> 54 dZ1 = np.multiply(1 - np.power(Z1, 2), np.dot(W2.T, dZ2))
55 dW1 = 1/m * np.dot(dZ1, X.T)
ValueError: operands could not be broadcast together with shapes (7,9) (9,7)
```

which is caused by Z1 having different dimensions than the other operand of the element-wise product in dZ1. From the lecture Gradient Descent for Neural Networks, the dimensions are:

dZ^{[1]} = \underbrace{W^{[2]T}dZ^{[2]}}_\text{(n[1], m)} * \underbrace{g'^{[1]}(Z^{[1]})}_\text{(n[1], m)}

I was thinking that my implementation is wrong, and still doubt it isnâ€™t, but after looking at the test code in `public_tests.py`

:

```
def backward_propagation_test(target):
[...]
cache = {'A1': np.random.randn(9, 7),
'A2': np.random.randn(1, 7),
'Z1': np.random.randn(7, 9), # <=== this right here
'Z2': np.random.randn(1, 7),}
```

shouldnâ€™t A1 and Z1 have same dimensions?