class_docstr: The analysis transform for the entropy model parameters.
class_docstr_tok: ["The","analysis","transform","for","the","entropy","model","parameters","."]
code: def __init__(self, hyperprior_depth):
      super().__init__()
      conv = functools.partial(
          tfc.SignalConv2D, corr=True, padding="same_zeros"
      )
  
      # See Appendix C.2 for more information on using a small hyperprior.
      layers = [
          conv(
              320,
              (3, 3),
              name="layer_0",
              strides_down=1,
              use_bias=True,
              activation=tf.nn.relu,
          ),
          conv(
              256,
              (5, 5),
              name="layer_1",
              strides_down=2,
              use_bias=True,
              activation=tf.nn.relu,
          ),
          conv(
              hyperprior_depth,
              (5, 5),
              name="layer_2",
              strides_down=2,
              use_bias=False,
              activation=None,
          ),
      ]
      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 HyperAnalysisTransform object from inherited base classes: tf.keras with example: obj = HyperAnalysisTransform(hyperprior_depth)