serializing/deserializing layers in TensorFlow.
Hi @AbhisekOmkar It looks like your PR relates to the Keras component. Please submit it to the github.com/keras-team/keras repository instead. Thankyou.
@fchollet, @qlzh727
The code is a script for populating and serializing/deserializing layers in TensorFlow. Some improvements that can be made to the code include:

Grouping related imports: Instead of having all the imports at the top of the script, they can be grouped and separated based on the module they belong to. This will make the script more organized and easier to maintain.

Improving readability: Some parts of the script can be simplified and made more readable by using meaningful variable names, breaking down complex statements into multiple lines, and using descriptive comments.

Making use of the custom_objects argument in deserialize: The custom_objects argument can be utilized to allow for custom layers to be deserialized in addition to the built-in layers.

Here is an example of the improved code:


import threading

from tensorflow import tf2
from tensorflow.keras import backend as K
from tensorflow.keras.engine import base_layer, input_layer from tensorflow.keras.layers import advanced_activations from tensorflow.keras.layers import convolutional, convolutional_recurrent, pooling from tensorflow.keras.layers import core, dense_attention, embeddings, merge from tensorflow.keras.layers import recurrent, recurrent_v2 from tensorflow.keras.layers import rnn_cell_wrapper_v2 from tensorflow.keras.models import Functional, Model, Sequential from tensorflow.keras.utils import generic_utils, tf_inspect from tensorflow.keras.utils.generic_utils import deserialize_keras_object from tensorflow.keras.utils.tf_export import keras_export

# Constants to store the layer modules and built-in objects V1_MODULES = (base_layer, input_layer, advanced_activations, convolutional,
              convolutional_recurrent, core, dense_attention, embeddings,
              merge, pooling, recurrent)
V2_MODULES = (rnn_cell_wrapper_v2, recurrent_v2)
BUILT_IN_OBJECTS = {
    'Input': input_layer.Input,
    'InputSpec': input_layer.InputSpec,
    'Functional': Functional,
    'Model': Model,
    'Sequential': Sequential
}

# Thread-local variable to store the populated deserializable objects LOCAL = threading.local()

def populate_deserializable_objects():
    """Populate the deserializable objects with the built-in layers and functions."""
    global LOCAL
    if not hasattr(LOCAL, 'ALL_OBJECTS'):
        LOCAL.ALL_OBJECTS = {}
        LOCAL.GENERATED_WITH_V2 = None

    if LOCAL.ALL_OBJECTS and LOCAL.GENERATED_WITH_V2 == tf2.enabled():
        # Deserializable objects already generated for the proper TF version
        return

    LOCAL.ALL_OBJECTS = BUILT_IN_OBJECTS.copy()
    LOCAL.GENERATED_WITH_V2 = tf2.enabled()