How to use the tensorpack.utils.logger.error function in tensorpack

Args:
            grad_list: list of list of tuples, shape is Ngpu x Nvar x 2
        """
        nvars = [len(k) for k in grad_list]

        def basename(x):
            return re.sub('tower[0-9]+/', '', x.op.name)

        if len(set(nvars)) != 1:
            names_per_gpu = [set([basename(k[1]) for k in grad_and_vars]) for grad_and_vars in grad_list]
            inters = copy.copy(names_per_gpu[0])
            for s in names_per_gpu:
                inters &= s
            for s in names_per_gpu:
                s -= inters
            logger.error("Unique trainable variables on towers: " + pprint.pformat(names_per_gpu))
            raise ValueError("Number of gradients from each tower is different! " + str(nvars))

def get_sublist_by_names(lst, names):
    """
    Args:
        lst (list): list of objects with "name" property.

    Returns:
        list: a sublist of objects, matching names
    """
    orig_names = [p.name for p in lst]
    ret = []
    for name in names:
        try:
            idx = orig_names.index(name)
        except ValueError:
            logger.error("Name {} doesn't appear in lst {}!".format(
                name, str(orig_names)))
            raise
        ret.append(lst[idx])
    return ret

def get_sublist_by_names(lst, names):
    """
    Args:
        lst (list): list of objects with "name" property.

    Returns:
        list: a sublist of objects, matching names
    """
    orig_names = [p.name for p in lst]
    ret = []
    for name in names:
        try:
            idx = orig_names.index(name)
        except ValueError:
            logger.error("Name {} doesn't appear in lst {}!".format(
                name, str(orig_names)))
            raise
        ret.append(lst[idx])
    return ret

w = np.clip(float(x1 + w), 0, width) - x1
            h = np.clip(float(y1 + h), 0, height) - y1
            # Require non-zero seg area and more than 1x1 box size
            if obj['area'] > 1 and w > 0 and h > 0 and w * h >= 4:
                obj['bbox'] = [x1, y1, x1 + w, y1 + h]
                valid_objs.append(obj)

                if add_mask:
                    segs = obj['segmentation']
                    if not isinstance(segs, list):
                        assert obj['iscrowd'] == 1
                        obj['segmentation'] = None
                    else:
                        valid_segs = [np.asarray(p).reshape(-1, 2).astype('float32') for p in segs if len(p) >= 6]
                        if len(valid_segs) == 0:
                            logger.error("Object {} in image {} has no valid polygons!".format(objid, img['file_name']))
                        elif len(valid_segs) < len(segs):
                            logger.warn("Object {} in image {} has invalid polygons!".format(objid, img['file_name']))

                        obj['segmentation'] = valid_segs

        # all geometrically-valid boxes are returned
        boxes = np.asarray([obj['bbox'] for obj in valid_objs], dtype='float32')  # (n, 4)
        cls = np.asarray([
            self.COCO_id_to_category_id[obj['category_id']]
            for obj in valid_objs], dtype='int32')  # (n,)
        is_crowd = np.asarray([obj['iscrowd'] for obj in valid_objs], dtype='int8')

        # add the keys
        img['boxes'] = boxes        # nx4
        img['class'] = cls          # n, always >0
        img['is_crowd'] = is_crowd  # n,

def get_savename_from_varname(
        varname, varname_prefix=None,
        savename_prefix=None):
    """
    :param varname: a variable name in the graph
    :param varname_prefix: an optional prefix that may need to be removed in varname
    :param savename_prefix: an optional prefix to append to all savename
    :returns: the name used to save the variable
    """
    name = varname
    if 'towerp/' in name:
        logger.error("No variable should be under 'towerp' name scope".format(v.name))
        # don't overwrite anything in the current prediction graph
        return None
    if 'tower' in name:
        name = re.sub('tower[p0-9]+/', '', name)
    if varname_prefix is not None \
            and name.startswith(varname_prefix):
        name = name[len(varname_prefix)+1:]
    if savename_prefix is not None:
        name = savename_prefix + '/' + name
    return name

def _inference_context():
    msg = "You might need to check your input implementation."
    try:
        yield
    except (StopIteration, tf.errors.CancelledError):
        logger.error(
            "[InferenceRunner] input stopped before reaching its __len__()! " + msg)
        raise
    except tf.errors.OutOfRangeError:   # tf.data reaches an end
        pass

def _trigger(self):
        try:
            v = {k: self.dic[k] for k in self.names}
        except KeyError:
            return
        cmd = self.command.format(**v)
        ret = os.system(cmd)
        if ret != 0:
            logger.error("Command '{}' failed with ret={}!".format(cmd, ret))
        self.dic = {}

elif type(data) == float:
            dtype = 'float32'
        elif isinstance(data, (six.binary_type, six.text_type)):
            dtype = 'str'
        else:
            try:
                dtype = data.dtype
            except AttributeError:
                raise TypeError("Unsupported type to batch: {}".format(type(data)))
        try:
            return np.asarray(data_list, dtype=dtype)
        except Exception as e:  # noqa
            logger.exception("Cannot batch data. Perhaps they are of inconsistent shape?")
            if isinstance(data, np.ndarray):
                s = pprint.pformat([x.shape for x in data_list])
                logger.error("Shape of all arrays to be batched: " + s)
            try:
                # open an ipython shell if possible
                import IPython as IP; IP.embed()    # noqa
            except ImportError:
                pass

w = np.clip(float(x1 + w), 0, width) - x1
            h = np.clip(float(y1 + h), 0, height) - y1
            # Require non-zero seg area and more than 1x1 box size
            if obj['area'] > 1 and w > 0 and h > 0 and w * h >= 4:
                obj['bbox'] = [x1, y1, x1 + w, y1 + h]
                valid_objs.append(obj)

                if add_mask:
                    segs = obj['segmentation']
                    if not isinstance(segs, list):
                        assert obj['iscrowd'] == 1
                        obj['segmentation'] = None
                    else:
                        valid_segs = [np.asarray(p).reshape(-1, 2).astype('float32') for p in segs if len(p) >= 6]
                        if len(valid_segs) == 0:
                            logger.error("Object {} in image {} has no valid polygons!".format(objid, img['file_name']))
                        elif len(valid_segs) < len(segs):
                            logger.warn("Object {} in image {} has invalid polygons!".format(objid, img['file_name']))

                        obj['segmentation'] = valid_segs

        # all geometrically-valid boxes are returned
        boxes = np.asarray([obj['bbox'] for obj in valid_objs], dtype='float32')  # (n, 4)
        cls = np.asarray([
            self.COCO_id_to_category_id[obj['category_id']]
            for obj in valid_objs], dtype='int32')  # (n,)
        is_crowd = np.asarray([obj['iscrowd'] for obj in valid_objs], dtype='int8')

        # add the keys
        img['boxes'] = boxes        # nx4
        img['class'] = cls          # n, always >0
        img['is_crowd'] = is_crowd  # n,

def proc_scale(self, idx, name, param):
        bottom_name = self.net.bottom_names[name][0]
        # find the bn layer before this scaling
        for i, layer in enumerate(self.net.layers):
            if layer.type == 'BatchNorm':
                name2 = self.layer_names[i]
                bottom_name2 = self.net.bottom_names[name2][0]
                if bottom_name2 == bottom_name:
                    # scaling and BN share the same bottom, should merge
                    logger.info("Merge {} and {} into one BatchNorm layer".format(
                        name, name2))
                    return {name2 + '/beta': param[1].data,
                            name2 + '/gamma': param[0].data}
        # assume this scaling layer is part of some BN
        logger.error("Could not find a BN layer corresponding to this Scale layer!")
        raise ValueError()