class_docstr: Probabilistic encoder for the time-variant latent variable `z_t`.
  
  The conditional distribution `q(z_t | x_t)` is a multivariate normal
  distribution on `R^{latent_size}` at each timestep `t`, conditioned on
  an intermediate representation of `x_t` from the convolutional
  encoder. The parameters are computed by a one-hidden layer neural
  net.
  
  In this formulation, we posit that the dynamic latent variable `z_t`
  is independent of static latent variable `f`.
  
  Together with the EncoderStatic class, we can formulate the factorized
  approximate latent posterior `q` inference ("encoder") model as
  
  ```none
  q(z_{1:T}, f | x_{1:T}) = q(f | x_{1:T}) prod_{t=1}^T q(z_t | x_t).
  ```
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docstr: Constructs a "factorized" encoder for `z_t`.
  
  Args:
    latent_size: An integer corresponding to the
      dimensionality of the distribution.
    hidden_size: Dimensionality of the affine function parameters.
code: def __init__(self, latent_size, hidden_size):
      """Constructs a "factorized" encoder for `z_t`.
  
      Args:
        latent_size: An integer corresponding to the
          dimensionality of the distribution.
        hidden_size: Dimensionality of the affine function parameters.
      """
      super(EncoderDynamicFactorized, self).__init__()
      self.latent_size = latent_size
      self.hidden_size = hidden_size
      self.dense = tf_keras.v1.layers.Dense(
          hidden_size, activation=tf1.nn.leaky_relu
      )
      self.output_layer = tf_keras.v1.layers.Dense(2 * latent_size)
  
imports: from datetime import datetime
  import functools
  from absl import app
  from absl import flags
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  import tensorflow_probability
  from tensorflow_probability.examples import sprites_dataset
  from tensorflow_probability.python.internal import tf_keras
label_desc: Use image node_id 1 to create a new EncoderDynamicFactorized object from inherited base classes: tf_keras.v1 with example: obj = EncoderDynamicFactorized(latent_size, hidden_size)