Hi, I’m a newbie to TVM, and found TVM could be a general solution to performance tuning not only for NN model. I tried to start from color space transformation and need your help.

With Numpy, rgb2yuv could be implemented like below, how can it be done with TVM? Numpy version:

```
def rgb2yuv(rgb):
m = np.array([
[0.29900, -0.147108, 0.614777],
[0.58700, -0.288804, -0.514799],
[0.11400, 0.435912, -0.099978]
])
yuv = np.dot(rgb, m)
yuv[:,:,1:] += 0.5
return yuv
```

My not working version(wsn’t able to find a way to update [i,i,:] at once ):

```
w=h=32
c=3
rgb = te.placeholder((w,h,c), name='rgb')
def rgb2yuv(i,j,k):
return rgb[i,j,0],rgb[i,j,1],rgb[i,j,2] // caculation formula ignored
yuv = te.compute((w,h,c), lambda i,j,k:rgb2yuv(i,j,k), name='yuv')
```