def _get_train_clips(self, num_frames):
        """Get clip offsets in train mode.

        It will calculate the average interval for selected frames,
        and randomly shift them within offsets between [0, avg_interval].
        If the total number of frames is smaller than clips num or origin
        frames length, it will return all zero indices.

        Args:
            num_frames (int): Total number of frame in the video.

        Returns:
            np.ndarray: Sampled frame indices in train mode.
        """
        ori_clip_len = self.clip_len * self.frame_interval   # TODO this is 1 * 1 default
        avg_interval = (num_frames - ori_clip_len + 1) // self.num_clips

        if avg_interval > 0:
            base_offsets = np.arange(self.num_clips) * avg_interval
            clip_offsets = base_offsets + np.random.randint(
                avg_interval, size=self.num_clips)
        elif num_frames > max(self.num_clips, ori_clip_len):
            clip_offsets = np.sort(
                np.random.randint(
                    num_frames - ori_clip_len + 1, size=self.num_clips))
        elif avg_interval == 0:
            ratio = (num_frames - ori_clip_len + 1.0) / self.num_clips
            clip_offsets = np.around(np.arange(self.num_clips) * ratio)
        else:
            clip_offsets = np.zeros((self.num_clips,), dtype=np.int)

        return clip_offsets
Get clip offsets in train mode.

        It will calculate the average interval for selected frames,
        and randomly shift them within offsets between [0, avg_interval].
        If the total number of frames is smaller than clips num or origin
        frames length, it will return all zero indices.

        Args:
            num_frames (int): Total number of frame in the video.

        Returns:
            np.ndarray: Sampled frame indices in train mode.
        
EVA-01/video/datasets/pipeline.py