class_docstr: Feature extractor.
  
  This convolutional model aims to extract features corresponding to a
  sequence of image frames for use in downstream probabilistic encoders.
  The architecture is symmetric to that of the convolutional decoder.
class_docstr_tok: ["Feature","extractor",".","This","convolutional","model","aims","to","extract","features","corresponding","to","a","sequence","of","image","frames","for","use","in","downstream","probabilistic","encoders",".","The","architecture","is","symmetric","to","that","of","the","convolutional","decoder","."]
docstr: Constructs a convolutional compressor.
  
  This model takes as input `x_{1:T}` and outputs an intermediate
  representation for use in downstream probabilistic encoders.
  
  Args:
    hidden_size: Dimensionality of the intermediate representations.
code: def __init__(self, hidden_size):
      """Constructs a convolutional compressor.
  
      This model takes as input `x_{1:T}` and outputs an intermediate
      representation for use in downstream probabilistic encoders.
  
      Args:
        hidden_size: Dimensionality of the intermediate representations.
      """
      super(Compressor, self).__init__()
      self.hidden_size = hidden_size
      # Spatial sizes: (64,64) -> (32,32) -> (16,16) -> (8,8) -> (1,1).
      conv = functools.partial(
          tf_keras.v1.layers.Conv2D,
          padding="SAME",
          activation=tf1.nn.leaky_relu,
      )
      self.conv1 = conv(256, 3, 2)
      self.conv2 = conv(256, 3, 2)
      self.conv3 = conv(256, 3, 2)
      self.conv4 = conv(hidden_size, 8, padding="VALID")
  
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 Compressor object from inherited base classes: tf_keras.v1 with example: obj = Compressor(hidden_size)