chore: import upstream snapshot with attribution

tools/tensorflow-quantization/docs/source/qmodel.rst

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+.. _qmodel_api:
+
+**tensorflow_quantization.quantize_model**
+============================================
+
+.. automodule:: tensorflow_quantization.quantize
+   :members: quantize_model
+
+.. note:: Currently only Functional and Sequential models are supported.
+
+Examples
+
+.. code:: python
+
+   import tensorflow as tf
+   from tensorflow_quantization.quantize import quantize_model
+
+   # Simple full model quantization.
+   # 1. Create a simple network
+   input_img = tf.keras.layers.Input(shape=(28, 28))
+   r = tf.keras.layers.Reshape(target_shape=(28, 28, 1))(input_img)
+   x = tf.keras.layers.Conv2D(filters=2, kernel_size=(3, 3))(r)
+   x = tf.keras.layers.ReLU()(x)
+   x = tf.keras.layers.Conv2D(filters=2, kernel_size=(3, 3))(x)
+   x = tf.keras.layers.ReLU()(x)
+   x = tf.keras.layers.Flatten()(x)
+   model = tf.keras.Model(input_img, x)
+
+   print(model.summary())
+
+   # 2. Quantize the network
+   q_model = quantize_model(model)
+   print(q_model.summary())