# Path: meta_dataset/models/experimental/reparameterizable_base.py
# def is_variable(obj):
# def is_trainable_variable(obj):
# def is_batch_norm_module(obj):
# def corner_case_setattr(target, attr, value):
# def corner_case_getattr(target, attr):
# def chained_getattr(obj, path):
# def _patch_getattribute(cls, new_getattribute):
# def _custom_getter(getter, classes=(tf.Module,)):
#     def new_getattribute(obj, name, orig_getattribute=orig_getattribute):
#   def reparameterize(self, replacements):
#     def getter(v_source):
#   def reparameterizables(self,
#                          variable_predicate=is_variable,
#                          module_predicate=None,
#                          with_path=False):
#       def path_prefixes(path):
#       def satisfies_module_predicate(variable_with_path):
# class ReparameterizableModule(tf.Module):


import functools
import gin.tf
import tensorflow.compat.v1 as tf
import tensorflow_addons as tfa
from meta_dataset.models.experimental import reparameterizable_base
# coding=utf-8
# Copyright 2022 The Meta-Dataset Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# Lint as: python3
"""Mappings from images to embeddings or embeddings to predictions."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function




@gin.configurable
class ReparameterizableBackbone(tf.keras.Sequential,

# Path: meta_dataset/models/experimental/reparameterizable_base.py
# def is_variable(obj):
# def is_trainable_variable(obj):
# def is_batch_norm_module(obj):
# def corner_case_setattr(target, attr, value):
# def corner_case_getattr(target, attr):
# def chained_getattr(obj, path):
# def _patch_getattribute(cls, new_getattribute):
# def _custom_getter(getter, classes=(tf.Module,)):
#     def new_getattribute(obj, name, orig_getattribute=orig_getattribute):
#   def reparameterize(self, replacements):
#     def getter(v_source):
#   def reparameterizables(self,
#                          variable_predicate=is_variable,
#                          module_predicate=None,
#                          with_path=False):
#       def path_prefixes(path):
#       def satisfies_module_predicate(variable_with_path):
# class ReparameterizableModule(tf.Module):


# Path: meta_dataset/models/experimental/reparameterizable_backbones.py
import functools
import gin.tf
import tensorflow.compat.v1 as tf
import tensorflow_addons as tfa
from meta_dataset.models.experimental import reparameterizable_base

# coding=utf-8
# Copyright 2022 The Meta-Dataset Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# Lint as: python3
"""Mappings from images to embeddings or embeddings to predictions."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function




@gin.configurable
class ReparameterizableBackbone(tf.keras.Sequential,

                                reparameterizable_base.ReparameterizableModule):