async def update_named_param(self, request: UpdateWeightsRequest):
        """
        Updates the model weights with the provided tensor.

        Once this endpoint is called, the client process should broadcast the updated weights to all server workers.

        Args:
            request (`UpdateWeightsRequest`):
                - `name` (`str`): Name of the weight tensor being updated.
                - `dtype` (`str`): Data type of the weight tensor (e.g., `"torch.float32"`).
                - `shape` (list of `int`): Shape of the weight

        """
        # The function update_named_param is called this way: update_named_param("name", torch.float32, (10, 10))
        # So with collective_rpc we need to call it this way:
        # llm.collective_rpc("update_named_param", args=("name", torch.float32, (10, 10)))
        dtype = torch.__getattribute__(request.dtype.split('.')[-1])
        kwargs = {'method': 'update_named_param', 'args': (request.name, dtype, tuple(request.shape))}
        for connection in self.connections:
            connection.send({'type': 'fire_and_forget', 'method': 'collective_rpc', 'kwargs': kwargs})

        return {'message': 'Request received, updating named parameter'}

        Updates the model weights with the provided tensor.

        Once this endpoint is called, the client process should broadcast the updated weights to all server workers.

        Args:
            request (`UpdateWeightsRequest`):
                - `name` (`str`): Name of the weight tensor being updated.
                - `dtype` (`str`): Data type of the weight tensor (e.g., `"torch.float32"`).
                - `shape` (list of `int`): Shape of the weight

        
swift/llm/infer/rollout.py