[Flax] from_pretrained does not consider the passed dtype
## Environment info

When loading a flax model with `from_pretrained` the type argument is not used. The weights are initialized with the dtype of saved weights.

So if you do 
```python
model = FlaxGPT2ForCausalLM.from_pretrained("gpt2", dtype=jnp.dtype("bfloat16"))

# check the dtype of one of the params 
model.params["transformer"]["wpe"]["embedding"].dtype
=> dtype("float32")
```

We should probably cast the weights to `self.dtype`.

As a workaround for `bf16`,  one could manually cast the weighs with
```
def to_bf16(t):
    return jax.tree_map(lambda x: x.astype(jnp.bfloat16) if x.dtype == jnp.float32 else x, t)

model.params = to_bf16(model.params)
```

cc @patrickvonplaten 
I wonder whether this might be problematic for layer norm weights since those should usually always be of type `float32`, no?<|||||>Would love to hear what @avital @marcvanzee think here<|||||>I think it's fine to manually port weights to bfloat16 if you want to. In general all Flax layers accept a dtype attribute when it's safe to do intermediate computation in bloat16 and you can set dtype=bfloat16 for those layers. Keeping parameters as bfloat16 should only be necessary if the model is huge and the parameters can't fit on device memory, from what I know. I think it's tricky to get that right and requires careful attention to which parameters are safe to keep in bfloat16, but I don't have too much personal context here. I can ask others if that helps.

So I'm first curious whether indeed it's necessary to keep parameters as bfloat16 in this case, and if so, why<|||||>hello so <|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored.<|||||>will soon be taken care of by @patil-suraj :-) 