[WIP] EasyMP vllm-omni model definition

examples/tts/easymagpie_vllm_omni/README.md

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+WIP model definition of EasyMP for vllm-omni. Follows footsteps of qwen3tts:
+backbone and LT are compiled into a single cuda graph during uniform batch decoding,
+piecewise during mixed/prefill.
+
+Install:
+```
+pip install -e ".[all]"
+pip install ninja mamba_ssm causal_conv1d --no-build-isolation
+# install vllm
+pip install vllm==0.21.0 vllm_omni==0.21.0rc1
+# register vllm models
+pip install -e examples/tts/easymagpie_vllm_omni/
+```
+
+Conver the checkpoint from
+https://huggingface.co/nvidia/easymagpietts_NEXT/tree/main/2605_NemotronTTS_V0.2/v2
+```
+python examples/tts/easymagpie_vllm_omni/easy_magpietts_convert_to_vllm.py \
+  --nemo_file <ckpt>/2605_EMTTS_SmallMamba_Step150k_posttrained_epoch12.nemo \
+  --codec_model_path <ckpt>/25fps_spectral_codec_with_bandwidth_extension.nemo \
+  --outdir examples/tts/easymagpie_vllm_omni/easymp_vllm_model \
+  --context_audio english_sample.wav --speaker_name eng \
+  --phoneme_tokenizer_path <ckpt>/bpe_ipa_tokenizer_2048_en_de_es_fr_hi_it_vi_zh_ko-KR_pt-BR_ar.json
+```
+
+Finally run notebook `examples/tts/easymagpie_vllm_omni/easymagpie_inference_demo.ipynb`
+to predict acoustic tokens