[QUESTION] Chapter 12, How to pass training=True argument used in call() of custom layer

Hi, on page 424 you show that we can control the layer based on argument training set to True or False - but I dont know how to set it to True (or how to pass a value True to the parameter training in call() moethod ??

I tries passing it to the layer, to the compile and to the fit - but I got errors.

Here is my example code, which I run and see that the default is used, because output of the layer is not changed, this can be seen in output of the custom model, where I input 3, and output is same - 3.

```python
# %%
import tensorflow as tf
from tensorflow import keras
import numpy as np

### DATA
# x values
x = tf.constant([[1.0], [2.0], [3.0]])

# y values
y = tf.constant([[2.0], [6.0], [4.0]])

### PLOT
# Plot all the data
import matplotlib.pyplot as plt

plt.scatter(x, y, c="b")
plt.show()

# %%
# CUSTOM LAYER
class AddGaussianNoise(keras.layers.Layer):
def __init__(self, stddev, **kwargs):
super().__init__(**kwargs)
self.stddev = stddev

def call(self, X, training=None):
if training:
# noise = tf.random.normal(tf.shape(X), stddev=self.stddev)
noise = 5
return X + noise
else:
return X

def compute_output_shape(self, batch_input_shape):
return batch_input_shape

class CustomCallback(keras.callbacks.Callback):
def on_epoch_end(self, epoch, logs=None):
print(model.layers[0].output)

tf.random.set_seed(42)
np.random.seed(42)

training_bool = True

model = keras.models.Sequential(
[
AddGaussianNoise(50, name="add_5"),
keras.layers.Dense(1),
]
)

lr0 = 0.1
optimizer = keras.optimizers.Nadam(learning_rate=lr0)
model.compile(
loss="mse",
optimizer=optimizer,
metrics=["mae"],
)

history = model.fit(
x,
y,
epochs=1,
validation_data=(x, y),
callbacks=[CustomCallback()],
)

# Visualize how the trained model performs
plt.scatter(x, y, c="b")
plt.scatter(x, model.predict(x), c="r")
plt.plot(x, model.predict(x), c="r")
plt.show()

### Model to Show Output of Some Layer
from keras.models import Model

layer_name = "add_5"
intermediate_layer_model = Model(
inputs=model.input,
outputs=model.get_layer(layer_name).output,
)
intermediate_output = intermediate_layer_model.predict([3])
intermediate_output
# array([[3.]], dtype=float32)
```

Can you help?
Thank you.