In [1]: from typing import Sequence
In [2]: a = 'asdfads'
In [3]: isinstance(a, Sequence)
Out[3]: True

        self.eval_steps = logs.get('steps', None)
        self.eval_metrics = logs.get('metrics', [])

    class _CallbackLogs(TypedDict):
        loss: float
        metrics: list[float]
        batch_size: int

    def on_eval_begin(self, logs=None):
        """Called at the start of evaluation.

        Args:
            logs (dict): The logs is a dict or None. The keys of logs
                passed by paddle.Model contains 'steps' and 'metrics',
                The `steps` is number of total steps of validation dataset.
                The `metrics` is a list of str including 'loss' and the names
                of paddle.metric.Metric.
        """