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: Runs the model to generate an intermediate representation of x_t.
  
  Args:
    inputs: A batch of image sequences `x_{1:T}` of shape
      `[sample_shape, batch_size, timesteps, height, width,
      channels]`.
  
  Returns:
    A batch of intermediate representations of shape [sample_shape,
    batch_size, timesteps, hidden_size].
code: def call(self, inputs):
      """Runs the model to generate an intermediate representation of x_t.
  
      Args:
        inputs: A batch of image sequences `x_{1:T}` of shape
          `[sample_shape, batch_size, timesteps, height, width,
          channels]`.
  
      Returns:
        A batch of intermediate representations of shape [sample_shape,
        batch_size, timesteps, hidden_size].
      """
      image_shape = tf1.shape(input=inputs)[-3:]
      collapsed_shape = tf1.concat(([-1], image_shape), axis=0)
      out = tf1.reshape(
          inputs, collapsed_shape
      )  # (sample*batch*T, h, w, c)
      out = self.conv1(out)
      out = self.conv2(out)
      out = self.conv3(out)
      out = self.conv4(out)
      expanded_shape = tf1.concat(
          (tf1.shape(input=inputs)[:-3], [-1]), axis=0
      )
      return tf1.reshape(out, expanded_shape)
  
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 2 for calling the Compressor obj's underlying member method code with example usage: obj.call(inputs) and returns: tf1