serving_input_receiver_fn() function without the deprecated tf.placeholder method in TF 2.0 - python-2.7

Question

I have a functioning tf.estimator pipeline build in TF 1, but now I made the decision to move to TF 2.0, and I have problems in the end of my pipeline, when I want to save the model in the .pb format
I'm using this high level estimator export_saved_model method:
https://www.tensorflow.org/api_docs/python/tf/estimator/BoostedTreesRegressor#export_saved_model
I have two numeric features, 'age' and 'time_spent'
They're defined using tf.feature_column as such:
age = tf.feature_column.numeric_column('age')
time_spent = tf.feature_column.numeric_column('time_spent')
features = [age,time_spent]
After the model has been trained I turn the list of features into a dict using the method feature_column_make_parse_example_spec() and feed it to another method build_parsing_serving_input_receiver_fn() excactly as outlied on tensorflow's webpage, https://www.tensorflow.org/guide/saved_model under estimators.
columns_dict = tf.feature_column_make_parse_example_spec(features)
input_receiver_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn(columns_dict)
model.export_saved_model(export_dir,input_receiver_fn)
I then inspect the output using the CLI tools
saved_model_cli show --dir mydir --all:
Resulting in the following:
enter image description here
Somehow Tensorflow squashes my two usefull numeric features into a useless string input crap called "inputs".
In TF 1 this could be circumvented by creating a custom input_receiver_fn() function using some tf.placeholder method, and I'd get the correct output with two distinct numeric features. But tf.placeholder doesn't exist in TF 2, so now it's pretty useless.
Sorry about the raging, but Tensorflow is horribly documented, and I'm really working with high level API's and it should just be straight out on the horse, but no.
I'd really appreciate any help :)

Answer 1

Tensorflow squashes my two usefull numeric features into a useless
string input crap called "inputs"
is not exactly true, as the exported model expects a serialized tf.Example proto. So, you can warp your age and time_spent into two features which will look like:
features {
feature {
key: "age"
value {
float32_list {
value: 10.2
}
}
}
feature {
key: "time_spent"
value {
float32_list {
value: 40.3
}
}
}
}
you can then call your regress function with the serialized string.