class_docstr: The synthesis transform for the entropy model parameters.
class_docstr_tok: ["The","synthesis","transform","for","the","entropy","model","parameters","."]
code: def __init__(self):
      super().__init__()
      conv = functools.partial(
          tfc.SignalConv2D,
          corr=False,
          padding="same_zeros",
          use_bias=True,
          kernel_parameter="variable",
          activation=tf.nn.relu,
      )
  
      # Note that the output tensor is still latent (it represents means and
      # scales but it does NOT hold mean or scale values explicitly). Therefore,
      # the final activation is ReLU rather than None or Exp). For the same
      # reason, it is not a requirement that the final depth of this transform
      # matches the depth of `y`.
      layers = [
          conv(192, (5, 5), name="layer_0", strides_up=2),
          conv(256, (5, 5), name="layer_1", strides_up=2),
          conv(320, (3, 3), name="layer_2", strides_up=1),
      ]
      for layer in layers:
          self.add(layer)
  
imports: import argparse
  import functools
  import glob
  import sys
  from absl import app
  from absl.flags import argparse_flags
  import tensorflow
  import tensorflow_compression
  import tensorflow_datasets
label_desc: Use image node_id 1 to create a new HyperSynthesisTransform object from inherited base classes: tf.keras with example: obj = HyperSynthesisTransform()