

This class has a few needs:
- An attribute name, which corresponds to the name of the tool itself. To be in tune with other tools which have a
  performative name, we'll name it model_download_counter.
- An attribute description, which will be used to populate the prompt of the agent.
- inputs and outputs attributes. Defining this will help the python interpreter make educated choices about types,
  and will allow for a gradio-demo to be spawned when we push our tool to the Hub. They're both a list of expected
  values, which can be text, image, or audio.
- A __call__ method which contains the inference code. This is the code we've played with above!
Here's what our class looks like now:
thon
from transformers import Tool
from huggingface_hub import list_models
class HFModelDownloadsTool(Tool):
    name = "model_download_counter"
    description = (
        "This is a tool that returns the most downloaded model of a given task on the Hugging Face Hub. "
        "It takes the name of the category (such as text-classification, depth-estimation, etc), and "
        "returns the name of the checkpoint."
    )
inputs = ["text"]
outputs = ["text"]