The shape of a probabilistic layer throws and error

I am a building model with TensorFlow probability layers. When I do, model.output.shape, I get an error:

AttributeError: 'UserRegisteredSpec' object has no attribute '_shape'

If I do, output_shape = tf.shape(model.output) it gives a Keras Tensor:

<KerasTensor: shape=(5,) dtype=int32 inferred_value=[None, 3, 128, 128, 128] (created by layer 'tf.compat.v1.shape_15') 

How can I get the actual values [None, 3, 128, 128, 128]?
I tried output_shape.get_shape(), but that gives the Tensor shape [5].

code to reproduce:

import tensorflow as tf
import tensorflow_probability as tfp
from tensorflow_probability import distributions as tfd

tfd = tfp.distributions

model = tf.keras.Sequential()
model.add(tf.keras.layers.Input(10))

model.add(tf.keras.layers.Dense(2, activation="linear"))
model.add(
    tfp.layers.DistributionLambda(
        lambda t: tfd.Normal(
            loc=t[..., :1], scale=1e-3 + tf.math.softplus(0.1 * t[..., 1:])
        )
    )
)
model.compile(
    optimizer=tf.keras.optimizers.Adam(),
    loss="mean_absolute_error",
    # List of metrics to monitor
    metrics="mean_absolute_error",
)
model.save("tf_test_model.h5")

model = load_model("tf_test_model.h5")
model.output.shape

Use the reproduction code in the issue as the starting point; run it through save/load_model and compare the loaded model's output shape with tf.shape(model.output). Trace the TensorFlow Probability DistributionLambda shape handling. Done means the loaded model exposes the expected inferred shape without raising AttributeError, with coverage for this reproduction.