def conv_forward(A_prev, W, b, hparameters):

“”"

Implements the forward propagation for a convolution function

```
Arguments:
A_prev -- output activations of the previous layer,
numpy array of shape (m, n_H_prev, n_W_prev, n_C_prev)
W -- Weights, numpy array of shape (f, f, n_C_prev, n_C)
b -- Biases, numpy array of shape (1, 1, 1, n_C)
hparameters -- python dictionary containing "stride" and "pad"
Returns:
Z -- conv output, numpy array of shape (m, n_H, n_W, n_C)
cache -- cache of values needed for the conv_backward() function
"""
# Retrieve dimensions from A_prev's shape (≈1 line)
# (m, n_H_prev, n_W_prev, n_C_prev) = None
# Retrieve dimensions from W's shape (≈1 line)
# (m, n_H_prev, n_W_prev, n_C_prev) = None
```

(this is the code in the assignment that was visable)

It states that W – Weights, numpy array of shape (f, f, n_C_prev, n_C)

but then later refers in the hint to code for W shape

# Retrieve dimensions from W’s shape (≈1 line)

# (m, n_H_prev, n_W_prev, n_C_prev) = None

and defining W.shape using (m, n_H_prev, n_W_prev, n_C_prev) gave a wrong output, is there a problem with the assignment or am i misunderstanding anything? the assignment passed when i used (f,f n_C_prev, n_C)