t = Symbol.t(integer=True)
dt = Symbol.d_t(integer=True)
d0 = initial_offset(r, w, 0)
d1 = initial_offset(r, w, 1)
d2 = initial_offset(r, w, 2)
i0 = i - d0
j0 = j - d1
t0 = t - d2
return Lamda[t:n2, j:n1,
i:n0](Sum[sum_limit(r, w, n2, dt, t0, 2),
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],
t0 + dt * r[2]] @ w[di, dj, dt]))
if batch_size:
batch_size = batch_size[0]
k = Symbol.k(integer=True)
return Lamda[k:batch_size](conv3d(x[k], w))
else:
return conv3d(x, w)
conv3d = Function.conv3d(real=True,
nargs=(2, ),
eval=conv3d,
shape=property(shape))
