community[patch]: Add param "task" to Databricks LLM to work around serialization of transform_output_fn
Labels: name: lgtm, name: auto:improvement, name: area: models, name: size:S

**What is the reproduce code?**

```python
from langchain.chains import LLMChain, load_chain
from langchain.llms import Databricks
from langchain.prompts import PromptTemplate

def transform_output(response):
    # Extract the answer from the responses.
    return str(response["candidates"][0]["text"])

def transform_input(**request):
    full_prompt = f"""{request["prompt"]}
    Be Concise.
    """
    request["prompt"] = full_prompt
    return request

chat_model = Databricks(
    endpoint_name="llama2-13B-chat-Brambles",
    transform_input_fn=transform_input,
    transform_output_fn=transform_output,
    verbose=True,
)
print(f"Test chat model: {chat_model('What is Apache Spark')}") # This works

llm_chain = LLMChain(llm=chat_model, prompt=PromptTemplate.from_template("{chat_input}"))
llm_chain("colorful socks") # this works
llm_chain.save("databricks_llm_chain.yaml") # transform_input_fn and transform_output_fn are not serialized into the model yaml file
loaded_chain = load_chain("databricks_llm_chain.yaml") # The Databricks LLM is recreated with transform_input_fn=None, transform_output_fn=None.
loaded_chain("colorful socks") # Thus this errors. The transform_output_fn is needed to produce the correct output
```


Error:
```
 File "/local_disk0/.ephemeral_nfs/envs/pythonEnv-6c34afab-3473-421d-877f-1ef18930ef4d/lib/python3.10/site-packages/pydantic/v1/main.py", line 341, in __init__
    raise validation_error
pydantic.v1.error_wrappers.ValidationError: 1 validation error for Generation
text
  str type expected (type=type_error.str)
 request payload: {'query': 'What is a databricks notebook?'}'}
```

**What does the error mean?**

When the LLM generates an answer, represented by a Generation data object. The Generation data object takes a str field called text, e.g. Generation(text=”blah”). However, the Databricks LLM tried to put a non-str to text, e.g. Generation(text={“candidates”:[{“text”: “blah”}]}) Thus, pydantic errors.

**Why the output format becomes incorrect after saving and loading the Databricks LLM?**

Databrick LLM does not support serializing transform_input_fn and transform_output_fn, so they are not serialized into the model yaml file. When the Databricks LLM is loaded, it is recreated with transform_input_fn=None, transform_output_fn=None. Without transform_output_fn, the output text is not unwrapped, thus errors.

Missing transform_output_fn causes this error.
Missing transform_input_fn causes the additional prompt “Be Concise.” to be lost after saving and loading.
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