[FlaxSpeechEncoderDecoderModel] Ensure Input and Output Word Embeddings Are **Not** Tied
In a seq2seq Speech-Encoder-Text-Decoder Model, the word embeddings of the text decoder should  **not** be tied to the word embeddings of the speech encoder. The embedding matrices lie in two completely different vector spaces that have no affiliation. The embedding matrix for the decoder should be specific to the `XXXForCausalLM` decoder model used. Thus, the encoder and decoder word embeddings should not be constrained to being equal. This is observed in the PyTorch script for the `SpeechEncoderDecoderModel`:

https://github.com/huggingface/transformers/blob/e02f95b2298997016cd01fdb182442093b34e8d2/src/transformers/models/speech_encoder_decoder/modeling_speech_encoder_decoder.py#L437-L438

This PR makes adds the same change to the `config` for the `FlaxSpeechEncoderDecoderModel`, as well as adding tests to ensure that the `tie_word_embeddings` config is set correctly for both the `SpeechEncoderDecoderModel` and `FlaxSpeechEncoderDecoderModel`.


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