return Lamda[j:n1, i:n0](
Sum[sum_limit(r, w, n1, dj, j0, 1),
sum_limit(r, w, n0, di, i0, 0)](
x[i0 + di * r[0], j0 + dj * r[1]] @ w[di, dj]))
if batch_size:
batch_size = batch_size[0]
k = Symbol.k(integer=True)
return Lamda[k:batch_size](conv2d(x[k], w))
else:
return conv2d(x, w)
conv2d = Function.conv2d(real=True,
nargs=(2, ),
eval=conv2d,
shape=property(shape))
def conv3d(x, w, *limits):
if limits:
(r, ), *_ = limits
else:
r = (1, 1, 1)
l0, l1, l2, in_channels, out_channels = w.shape
*batch_size, n0, n1, n2, _in_channels = x.shape
assert in_channels == _in_channels
def conv3d(x, w):
