Toby Roseman

Looks like [torch.randn](https://pytorch.org/docs/stable/generated/torch.randn.html) is also not supported. This will need to be supported before we can support `torch.randn_like`. I think we can use [mb.random_normal](https://apple.github.io/coremltools/source/coremltools.converters.mil.mil.ops.defs.html#coremltools.converters.mil.mil.ops.defs.iOS15.random.random_normal) to support much of the functionality...

In order to help here, I need to be able to reproduce the problem. Could someone provide self-contained code to reproduce the issue (complete with any necessary links to the...

Since we have not received steps to reproduce this problem, I'm going to close this issue. If we get steps to reproduce the problem, I will reopen the issue.

Thanks for the pull request @odedzewi. Please also include a unit test, which fails without your fix but pass with your fix.

You'll need the following change to your unit test in order to get it to run: ``` -217,7 +216,8 @@ class BoostedTreeRegressorXGboostTest(unittest.TestCase): ) model = xgboost.train({}, dtrain, 1) - spec...

I can reproduce the issue using your code. However I don't think it's `x.to(torch.int32)` that is the issue. The following code: ```python import coremltools as ct import numpy as np...

Looking at [the documentation for `torch.frac`](https://pytorch.org/docs/stable/generated/torch.frac.html), I don't think `x - x_int` is right. I think this is the correct approach: ```python class Frac(torch.nn.Module): def forward(self, x): return x -...

@devalexqt - can you construct a simple PyTorch model that when converted gives this error?

@devalexqt - it looks like there are a few different models associated with that project. Can you provide a direct link to the model you're using? I can't find any...

It looks like `_weight_norm` is what [`weight_norm`](https://pytorch.org/docs/stable/generated/torch.nn.utils.weight_norm.html) gets lowered to. Here is a minimal example to reproduce the problem: ```python import coremltools as ct import torch m = torch.nn.utils.weight_norm(torch.nn.Linear(20, 40))...