def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
        """
        Convenience method to build a `LocalAgent` from a pretrained checkpoint.

        Args:
            pretrained_model_name_or_path (`str` or `os.PathLike`):
                The name of a repo on the Hub or a local path to a folder containing both model and tokenizer.
            kwargs (`Dict[str, Any]`, *optional*):
                Keyword arguments passed along to [`~PreTrainedModel.from_pretrained`].

        Example:

        ```py
        import torch
        from transformers import LocalAgent

        agent = LocalAgent.from_pretrained("bigcode/starcoder", device_map="auto", torch_dtype=torch.bfloat16)
        agent.run("Draw me a picture of rivers and lakes.")
        ```
        """
        model = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path, **kwargs)
        tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path, **kwargs)
        return cls(model, tokenizer)

        Convenience method to build a `LocalAgent` from a pretrained checkpoint.

        Args:
            pretrained_model_name_or_path (`str` or `os.PathLike`):
                The name of a repo on the Hub or a local path to a folder containing both model and tokenizer.
            kwargs (`Dict[str, Any]`, *optional*):
                Keyword arguments passed along to [`~PreTrainedModel.from_pretrained`].

        Example:

        ```py
        import torch
        from transformers import LocalAgent

        agent = LocalAgent.from_pretrained("bigcode/starcoder", device_map="auto", torch_dtype=torch.bfloat16)
        agent.run("Draw me a picture of rivers and lakes.")
        ```
        
dpkd/transformers/src/transformers/tools/agents.py