I don’t think the larger kernel size, padding, and dilation would make any difference than applying a 1x1 kernel using the center kernel pixel.
Here is a small code snippet to show the equivalence:
# setup
x = torch.randn(1, 3, 24, 24)
avg_pool = nn.AdaptiveAvgPool2d(1)
conv3_3 = nn.Conv2d(3, 1, kernel_size=3, stride=1, padding=3, dilation=3)

# reference calculation
out_ref = conv3_3(avg_pool(x))

# using a kernel size of 1
conv_new = nn.Conv2d(3, 1, kernel_size=1)
with torch.no_grad():
    conv_new.weight.copy_(conv3_3.weight[:, :, 1:2, 1:2])
    conv_new.bias.copy_(conv3_3.bias)
out_new = conv_new(avg_pool(x))

# compare
print(torch.allclose(out_ref, out_new))
> True
print((out_ref - out_new).abs().max())
> tensor(0., grad_fn=<MaxBackward1>)