I want to export my model to Tensorflow, which as I understand it means I need to export to ONNX first. (is that right?)

My model contains a torch.atan2() operation, which is not on the list of supported operators for ONNX export. Indeed, the list for ONNX itself only includes atan but not atan2, but Tensorflow has atan2.

So, if this were numpy I could just write my own little “my_atan2()” function that calls atan() and then uses some kind of `where()`

to decide how many factors of pi to add appropriately… maybe something like this?

```
def my_atan2(y, x):
pi = torch.from_numpy(np.array([np.pi])).to(y.device, y.dtype)
ans = torch.atan(y/x)
ans = torch.where( (y>0)*(x<0), ans+pi, ans) # upper left quadrant
ans = torch.where( (y<0)*(x<0), ans+pi, ans) # lower left quadrant
# upper right quadrant and lower right quadrant, do nothing
return ans
```

…But I’m guessing that won’t satisfy Autograd. And looking at the code for `torch.atan2.backward()`

… yea I don’t understand what’s going on there.

Any suggestions?