Hi all! I want to implement Quantum convolution layer into my classical model. I am referring this tutorial (tutorial_quanvolution)to get started.

I am working on a model (Graph neural network) which is used to find the properties of molecule. The steps being followed in model are:

- Feature embedding
- 3D graph convolution
- readout
- fully-connected phases

```
def quanv(image):
"""Convolves the input image with many applications of the same quantum circuit."""
out = np.zeros((14, 14, 4))
# Loop over the coordinates of the top-left pixel of 2X2 squares
for j in range(0, 28, 2):
for k in range(0, 28, 2):
# Process a squared 2x2 region of the image with a quantum circuit
q_results = circuit(
[
image[j, k, 0],
image[j, k + 1, 0],
image[j + 1, k, 0],
image[j + 1, k + 1, 0]
]
)
# Assign expectation values to different channels of the output pixel (j/2, k/2)
for c in range(4):
out[j // 2, k // 2, c] = q_results[c]
return out
```

I was wondering how do I replace this code snippet and change as per my requirement.