Keras feature extractor clarification - which layers does an input goes through - tensorflow

Question

When extracting a model layer output as in the Tensorflow sequential model document example below, does the input x in the code go through the my_first_layer as well before going into my_intermediate_layer layer? Or does it directly go into the my_intermediate_layer layer without going through the my_first_layer layer?
If it directly goes into the my_intermediate_layer, the input to the my_intermediate_layer does not have the transformation done by my_first_layer Conv2D. However, it seems not right to me because the input should go through all the preceding layers.
Please help understand what layers does x go through?
Feature extraction with a Sequential model
initial_model = keras.Sequential(
[
keras.Input(shape=(250, 250, 3)),
layers.Conv2D(32, 5, strides=2, activation="relu", name="my_first_layer"),
layers.Conv2D(32, 3, activation="relu", name="my_intermediate_layer"),
layers.Conv2D(32, 3, activation="relu"),
]
)
# The model goes through the training.
...
# Feature extractor
feature_extractor = keras.Model(
inputs=initial_model.inputs,
outputs=initial_model.get_layer(name="my_intermediate_layer").output,
)
# Call feature extractor on test input.
x = tf.ones((1, 250, 250, 3))
features = feature_extractor(x)

Answer 1

Keras offers higher level of API, which runs on top of the TensorFlow machine learning platform. Keras offers two types of class to define the neural network model, namely 'Sequential Class' and 'Model Class.'
Sequential Class:
It groups a linear stack of layers to form a model, such that each layer has one input and one output tensor. One can add required layers to the defined model (schema-1) as shown below to execute sequentially as name suggests Keras Sequential Class,
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(8, input_shape=(16,)))
model.add(tf.keras.layers.Dense(4))
The schema for defining a sequential model Keras-Sequential Class Definition has shown below (schema-2),
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
model = keras.Sequential(
[
layers.Dense(2, activation="relu", name="layer1"),
layers.Dense(3, activation="relu", name="layer2"),
layers.Dense(4, name="layer3"),
]
)
# Call model on a test input
x = tf.ones((3, 3))
y = model(x)
Model Class
It allows the user to build a custom model along with many layers as shown below,
import tensorflow as tf
inputs = tf.keras.Input(shape=(3,))
x = tf.keras.layers.Dense(4, activation=tf.nn.relu)(inputs)
outputs = tf.keras.layers.Dense(5, activation=tf.nn.softmax)(x)
model = tf.keras.Model(inputs=inputs, outputs=outputs)
It allows one to create a new functional API model with additional layers Keras - Model Class as follows,
inputs = keras.Input(shape=(None, None, 3))
processed = keras.layers.RandomCrop(width=32, height=32)(inputs)
conv = keras.layers.Conv2D(filters=2, kernel_size=3)(processed)
pooling = keras.layers.GlobalAveragePooling2D()(conv)
feature = keras.layers.Dense(10)(pooling)
Note: The input tensors supports only dicts, lists or tuples but not lists of list, or dicts of dict.
I hope that this helps.