225 lines
7.6 KiB
Python
225 lines
7.6 KiB
Python
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""A LazyLoader class."""
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import importlib
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import os
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import types
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from tensorflow.python.platform import tf_logging as logging
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_TENSORFLOW_LAZY_LOADER_PREFIX = "_tfll"
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class LazyLoader(types.ModuleType):
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"""Lazily import a module, mainly to avoid pulling in large dependencies.
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`contrib`, and `ffmpeg` are examples of modules that are large and not always
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needed, and this allows them to only be loaded when they are used.
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"""
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# The lint error here is incorrect.
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def __init__(self, local_name, parent_module_globals, name, warning=None):
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self._tfll_local_name = local_name
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self._tfll_parent_module_globals = parent_module_globals
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self._tfll_warning = warning
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# These members allows doctest correctly process this module member without
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# triggering self._load(). self._load() mutates parant_module_globals and
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# triggers a dict mutated during iteration error from doctest.py.
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# - for from_module()
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super().__setattr__("__module__", name.rsplit(".", 1)[0])
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# - for is_routine()
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super().__setattr__("__wrapped__", None)
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super().__init__(name)
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def _load(self):
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"""Load the module and insert it into the parent's globals."""
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# Import the target module and insert it into the parent's namespace
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module = importlib.import_module(self.__name__)
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self._tfll_parent_module_globals[self._tfll_local_name] = module
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# Emit a warning if one was specified
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if self._tfll_warning:
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logging.warning(self._tfll_warning)
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# Make sure to only warn once.
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self._tfll_warning = None
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# Update this object's dict so that if someone keeps a reference to the
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# LazyLoader, lookups are efficient (__getattr__ is only called on lookups
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# that fail).
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self.__dict__.update(module.__dict__)
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return module
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def __getattr__(self, name):
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module = self._load()
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return getattr(module, name)
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def __setattr__(self, name, value):
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if name.startswith(_TENSORFLOW_LAZY_LOADER_PREFIX):
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super().__setattr__(name, value)
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else:
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module = self._load()
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setattr(module, name, value)
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self.__dict__[name] = value
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try:
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# check if the module has __all__
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if name not in self.__all__ and name != "__all__":
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self.__all__.append(name)
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except AttributeError:
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pass
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def __delattr__(self, name):
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if name.startswith(_TENSORFLOW_LAZY_LOADER_PREFIX):
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super().__delattr__(name)
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else:
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module = self._load()
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delattr(module, name)
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self.__dict__.pop(name)
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try:
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# check if the module has __all__
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if name in self.__all__:
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self.__all__.remove(name)
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except AttributeError:
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pass
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def __repr__(self):
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# Carefully to not trigger _load, since repr may be called in very
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# sensitive places.
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return f"<LazyLoader {self.__name__} as {self._tfll_local_name}>"
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def __dir__(self):
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module = self._load()
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return dir(module)
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def __reduce__(self):
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return importlib.import_module, (self.__name__,)
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class KerasLazyLoader(LazyLoader):
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"""LazyLoader that handles routing to different Keras version."""
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def __init__( # pylint: disable=super-init-not-called
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self, parent_module_globals, mode=None, submodule=None, name="keras"):
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self._tfll_parent_module_globals = parent_module_globals
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self._tfll_mode = mode
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self._tfll_submodule = submodule
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self._tfll_name = name
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self._tfll_initialized = False
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def _initialize(self):
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"""Resolve the Keras version to use and initialize the loader."""
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self._tfll_initialized = True
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package_name = None
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keras_version = None
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if os.environ.get("TF_USE_LEGACY_KERAS", None) in ("true", "True", "1"):
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try:
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import tf_keras # pylint: disable=g-import-not-at-top,unused-import
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keras_version = "tf_keras"
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if self._tfll_mode == "v1":
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package_name = "tf_keras.api._v1.keras"
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else:
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package_name = "tf_keras.api._v2.keras"
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except ImportError:
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logging.warning(
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"Your environment has TF_USE_LEGACY_KERAS set to True, but you "
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"do not have the tf_keras package installed. You must install it "
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"in order to use the legacy tf.keras. Install it via: "
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"`pip install tf_keras`"
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)
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else:
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try:
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import keras # pylint: disable=g-import-not-at-top
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if keras.__version__.startswith("3."):
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# This is the Keras 3.x case.
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keras_version = "keras_3"
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package_name = "keras._tf_keras.keras"
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else:
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# This is the Keras 2.x case.
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keras_version = "keras_2"
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if self._tfll_mode == "v1":
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package_name = "keras.api._v1.keras"
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else:
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package_name = "keras.api._v2.keras"
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except ImportError:
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raise ImportError( # pylint: disable=raise-missing-from
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"Keras cannot be imported. Check that it is installed."
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)
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self._tfll_keras_version = keras_version
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if keras_version is not None:
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if self._tfll_submodule is not None:
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package_name += "." + self._tfll_submodule
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super().__init__(
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self._tfll_name, self._tfll_parent_module_globals, package_name
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)
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else:
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raise ImportError( # pylint: disable=raise-missing-from
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"Keras cannot be imported. Check that it is installed."
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)
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def __getattr__(self, item):
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if item in ("_tfll_mode", "_tfll_initialized", "_tfll_name"):
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return super(types.ModuleType, self).__getattribute__(item)
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if not self._tfll_initialized:
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self._initialize()
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if self._tfll_keras_version == "keras_3":
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if (
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self._tfll_mode == "v1"
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and not self._tfll_submodule
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and item.startswith("compat.v1.")
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):
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raise AttributeError(
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"`tf.compat.v1.keras` is not available with Keras 3. Keras 3 has "
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"no support for TF 1 APIs. You can install the `tf_keras` package "
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"as an alternative, and set the environment variable "
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"`TF_USE_LEGACY_KERAS=True` to configure TensorFlow to route "
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"`tf.compat.v1.keras` to `tf_keras`."
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)
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elif (
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self._tfll_mode == "v2"
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and not self._tfll_submodule
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and item.startswith("compat.v2.")
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):
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raise AttributeError(
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"`tf.compat.v2.keras` is not available with Keras 3. Just use "
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"`import keras` instead."
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)
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elif self._tfll_submodule and self._tfll_submodule.startswith(
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"__internal__.legacy."
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):
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raise AttributeError(
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f"`{item}` is not available with Keras 3."
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)
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module = self._load()
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return getattr(module, item)
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def __repr__(self):
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if self._tfll_initialized:
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return (
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f"<KerasLazyLoader ({self._tfll_keras_version}) "
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f"{self.__name__} as {self._tfll_local_name} mode={self._tfll_mode}>"
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)
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return "<KerasLazyLoader>"
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def __dir__(self):
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if not self._tfll_initialized:
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self._initialize()
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return super().__dir__()
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