- Enable content being an array, which can have image_url
- Introduce ModelType.VLM so that only VLM can handle non-string message content
- Thus pass in loadedModelType to postInitCheck, hence add loadedModelIdToModelType in Engine
- Change unit tests correspondingly

- getPromptArray element can now be an array itself
- Conversation.messages message can either be a string or an array of content parts
- Update compareConversationObject
- Next step is to update llm_chat.ts getInputTokens

- Implement getInputData to replace getInputTokens
  - Instead of returning a list of tokens, return a list of mixture of number[] and imageUrl
- Implement getChunkedPrefillInputData that transforms output of getInputData into chunks, thoroughly tested
- Replace forward with embedAndForward, which takes in a chunk, embeds it, and forward
- Within embedAndForward, we first embed all components in the chunk
- Note chunking is taken care of in getChunkedPrefillInputData, so embedAndForward has data length less than prefill chunk size
- TODOs: implement getImageEmbeddings, concatenation of embeddings, E2E test with image input
- Tested:
  - simple chat, logit processor, get_started, multi-round example
  - prefill multimple messages
  - 32 prefill chunk size, prefill 300 tokens

- Impelment getImageEmbedding that loads into ImageData and call embed_image kernel
- Use TVM global function concatEmbeddings to combine text and image embeddings
- Imlpement helper function that loads ImageData from url, either http or base64