quantum_cog_gen.py

```python
import tensorflow as tf
from tensorflow.keras.layers import Dense, Conv2D, LSTM, Attention, SelfAttention
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.losses import CategoricalCrossentropy

class QuantumCogGen(Model):
    def __init__(self, num_classes):
        super(QuantumCogGen, self).__init__()
        self.genetic_layer = Dense(64, activation='sigmoid')
        self.liquid_layer = Conv2D(32, kernel_size=(3, 3), activation='relu')
        self.generational_layer = LSTM(128, return_sequences=True)
        self.conv_cognitive_layer = Conv2D(64, kernel_size=(3, 3), activation='relu')
        self.recurrent_cognitive_layer = LSTM(64, return_sequences=True)
        self.attentive_layer = Attention()
        self.adversarial_layer = Dense(32, activation='relu')
        self.progressive_layer = Dense(16, activation='relu')
        self.quantum_layer = Dense(32, activation='tanh')
        self.self_reflection_layer = Dense(64, activation='relu')
        self.self_attention_layer = SelfAttention(64)
        self.emotional_layer = Dense(32, activation='relu')
        self.logic_reasoning_layer = Dense(16, activation='relu')
        self.output_layer = Dense(num_classes, activation='softmax')

    def call(self, inputs):
        x = self.genetic_layer(inputs)
        x = self.liquid_layer(x)
        x = self.generational_layer(x)
        x = self.conv_cognitive_layer(x)
        x = self.recurrent_cognitive_layer(x)
        x = self.attentive_layer(x)
        x = self.adversarial_layer(x)
        x = self.progressive_layer(x)
        x = self.quantum_layer(x)
        x = self.self_reflection_layer(x)
        x = self.self_attention_layer(x)
        x = self.emotional_layer(x)
        x = self.logic_reasoning_layer(x)
        return self.output_layer(x)

# Example usage
quantum_cog_gen = QuantumCogGen(num_classes=10)
optimizer = Adam(learning_rate=0.001)
loss_fn = CategoricalCrossentropy()

# Training loop
for epoch in range(num_epochs):
    with tf.GradientTape() as tape:
        logits = quantum_cog_gen(inputs)
        loss_value = loss_fn(labels, logits)
    grads = tape.gradient(loss_value, quantum_cog_gen.trainable_variables)
    optimizer.apply_gradients(zip(grads, quantum_cog_gen.trainable_variables))
```