def METHOD_NAME(
    self,
    x: "tf.Tensor",
    y: Optional[Union["tf.Tensor", List[Dict[str, "tf.Tensor"]]]] = None,
) -> Tuple["tf.Tensor", Optional[Union["tf.Tensor", List[Dict[str, "tf.Tensor"]]]]]:
    """
    Square pad `x` and adjust bounding boxes for labels `y` accordingly.
    :param x: Input samples. A list of samples is also supported.
    :param y: Label of the samples `x`.
    :return: Transformed samples and labels.
    """
    import tensorflow as tf
    x_preprocess = []
    y_preprocess: Optional[Union[tf.Tensor, List[Dict[str, tf.Tensor]]]]
    if y is not None and self.label_type == "object_detection":
        y_preprocess = []
    else:
        y_preprocess = y
    for i, x_i in enumerate(tqdm(x, desc="ImageSquarePadTensorFlowV2", disable=not self.verbose)):
        if self.channels_first:
            x_i = tf.transpose(x_i, (1, 2, 0))
        # Calculate padding
        height, width, _ = x_i.shape
        if height > width:
            pad_top = 0
            pad_bottom = 0
            pad_left = int(np.floor((height - width) / 2))
            pad_right = int(np.ceil((height - width) / 2))
        else:
            pad_top = int(np.floor((width - height) / 2))
            pad_bottom = int(np.ceil((width - height) / 2))
            pad_left = 0
            pad_right = 0
        # Pad image to square size
        padding = [[pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]
        x_pad = tf.pad(x_i, padding, mode=self.pad_mode, **self.pad_kwargs)
        if self.channels_first:
            x_pad = tf.transpose(x_pad, (2, 0, 1))
        if self.clip_values is not None:
            x_pad = tf.clip_by_value(x_pad, self.clip_values[0], self.clip_values[1])
        x_preprocess.append(x_pad)
        if self.label_type == "object_detection" and y is not None:
            y_pad: Dict[str, tf.Tensor] = {}
            # Copy labels and ensure types
            if isinstance(y, list) and isinstance(y_preprocess, list):
                y_i = y[i]
                if isinstance(y_i, dict):
                    y_pad = {k: tf.identity(v) for k, v in y_i.items()}
                else:
                    raise TypeError("Wrong type for `y` and label_type=object_detection.")
            else:
                raise TypeError("Wrong type for `y` and label_type=object_detection.")
            # Shift bounding boxes
            boxes = y_pad["boxes"]
            boxes += tf.constant([pad_left, pad_top, pad_left, pad_top], dtype=boxes.dtype)
            y_pad["boxes"] = boxes
            y_preprocess.append(y_pad)
    if isinstance(x, tf.Tensor):
        return tf.stack(x_preprocess, axis=0), y_preprocess
    return x_preprocess, y_preprocess