Can't convert Flax T5 model to PyTorch
### System Info

- `transformers` version: 4.18.0
- Platform: Linux-3.10.0-957.5.1.el7.x86_64-x86_64-with-centos-7.6.1810-Core
- Python version: 3.6.13
- Huggingface_hub version: 0.4.0
- PyTorch version (GPU?): 1.10.2 (False)
- Tensorflow version (GPU?): 2.6.2 (False)
- Flax version (CPU?/GPU?/TPU?): 0.3.5 (cpu)
- Jax version: 0.2.17
- JaxLib version: 0.1.69
- Using GPU in script?: NO
- Using distributed or parallel set-up in script?: NO

### Who can help?

@patrickvonplaten @patil-suraj

### Information

- [X] The official example scripts
- [ ] My own modified scripts

### Tasks

- [X] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)

### Reproduction

Failed to convert a T5 model from Flax to PyTorch

```python
import tempfile
from transformers import AutoTokenizer, FlaxT5ForConditionalGeneration, T5ForConditionalGeneration
tmp = tempfile.mkdtemp()

flax_model = FlaxT5ForConditionalGeneration.from_pretrained("t5-small")
flax_model.save_pretrained(tmp)
pt_model = T5ForConditionalGeneration.from_pretrained(tmp, from_flax=True)
```

### Expected behavior

Some weights of T5ForConditionalGeneration were not initialized from the Flax model: ['encoder.embed_tokens.weight', 'decoder.embed_tokens.weight', 'lm_head.weight']

```
WARNING:absl:No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)
/data/home/db72687/miniconda3/envs/decipher/lib/python3.6/site-packages/transformers/modeling_flax_pytorch_utils.py:240: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /opt/conda/conda-bld/pytorch_1640811805959/work/torch/csrc/utils/tensor_numpy.cpp:189.)
  pt_model_dict[flax_key] = torch.from_numpy(flax_tensor)
All Flax model weights were used when initializing T5ForConditionalGeneration.

Some weights of T5ForConditionalGeneration were not initialized from the Flax model and are newly initialized: ['encoder.embed_tokens.weight', 'decoder.embed_tokens.weight', 'lm_head.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
```
Sorry, I made a mistake. 
Actually the missing weights of above the above PyTorch model converted from Flax can be correctly initialized by accepting the Flax model's weight, `fx_model.params['shared']['embedding']`.