Building models and estimators in tf2.0 without tf.keras - tensorflow-estimator

Question

Given that layers API has been deprecated, how do I build models in tf2 without using tf.keras (or what is the recommended way to build models)? Issue #30829 has the same question, but was closed without any answers.
Update:
I'm okay with using tf.keras.layers instead of tf.layers, but once I've built all the layers and I need to return the model, is there a way to NOT use keras model, compile, fit, predict and evaluate, and just do it the tensorflow's way?
If you were wondering why I would want to do something like that, it is that I would like to use estimators to train, rather than keras' fit function. There exists a keras_model_to_estimator, but it seems it's not mature enough yet

Answer 1

Google released migration guide from TF 1 to TF 2, section Converting Models.
Recommended way to build models
Guide (section "Models based on tf.layers") recommends to convert tf.layers models to tf.keras.layers:
The conversion was one-to-one because there is a direct mapping from v1.layers to tf.keras.layers.
Build models without Keras
The option is to provide own layer implementations (example from the guide):
W = tf.Variable(tf.ones(shape=(2,2)), name="W")
b = tf.Variable(tf.zeros(shape=(2)), name="b")
#tf.function
def forward(x):
return W * x + b
out_a = forward([1,0])
print(out_a)
But it is worth to consider tf.keras.layers.Layer (example), which gives some degree of freedom, but integrate with rest of Keras (and it's layers).
Even with layers written with tf.keras, you are able to write own training loop (example).
To sum up, in practice TF 2.0 requires you to use tf.keras.