docs: Update the adaptive batching doc

[Sherlock113]

What does this PR address?

Fixes #(issue)

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[frostming]

Hi, I would like suggest adding a more complex batching example.

Since BentoML only supports one argument for batchable API, in some cases users want to pass multiple arguments, this can be workarounded with a composite input type and a wrapper service:

from __future__ import annotations

from pathlib import Path

import bentoml
from pydantic import BaseModel


class BatchInput(BaseModel):
    image: Path
    threshold: float


@bentoml.service
class ImageService:
    @bentoml.api(batchable=True)
    def predict(self, inputs: list[BatchInput]) -> list[Path]:
        # inference logic here using the image and threshold
        return [input.image for input in inputs]


@bentoml.service
class MyService:
    batch = bentoml.depends(ImageService)

    @bentoml.api
    def generate(self, image: Path, threshold: float) -> Path:
        result = self.batch.predict([BatchInput(image=image, threshold=threshold)])
        return result[0]

[Sherlock113]

@frostming Thanks for the explanation. This use case is very helpful and I added another section to explain it. Please take a look.