Replace python random with torch.rand to enable dynamo.export
Author: BowenBao
Pull request: yes

# What does this PR do?

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Related and Fixes https://github.com/pytorch/pytorch/issues/102794

TL;DR dynamo graph breaks on python `random.uniform(0, 1)`. The graph break can be prevented by replacing with `torch.randn([])`.

Example repro script
```python
import torch
import torch._dynamo

from transformers import AutoTokenizer, BartForCausalLM

tokenizer = AutoTokenizer.from_pretrained("facebook/bart-base")
model = BartForCausalLM.from_pretrained("facebook/bart-base", add_cross_attention=False)
inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
outputs = model(**inputs)

torch._dynamo.export(model, return_dict=False, **inputs)
```

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Comments:
> You are touching multiple Flax model which shouldn't depend on torch, could you revert that?

Nice catch! Done.

_The documentation is not available anymore as the PR was closed or merged._

@BowenBao Does this really solve the export problem. We are seeing export issues here - https://github.com/pytorch/pytorch/issues/107587

If one peeks at the tensor value for the conditional, its a legit dynamic control flow. We might have to use `torch.where` or `torch.cond`.

@anijain2305 it does for inference export, since the actual condition is short circuited by `self.training` being False. Without the change, `random.uniform(0, 1)` leads to a graph break, although the value is unused.