class_docstr: HiFiC Model class.
class_docstr_tok: ["HiFiC","Model","class","."]
docstr: Create optimizers for encoder/decoder and entropy model.
docstr_tok: ["Create","optimizers","for","encoder\/decoder","and","entropy","model","."]
code: def _make_enc_dec_entropy_train_op(
      self, step, loss, entropy_vars, transform_vars
  ):
      """Create optimizers for encoder/decoder and entropy model."""
      minimize_ops = []
      assert len(self._entropy_model.losses) == 1
      for i, (name, vs, l) in enumerate(
          [
              ("transform", transform_vars, loss),
              ("entropy", entropy_vars, loss),
              ("aux", entropy_vars, self._entropy_model.losses[0]),
          ]
      ):
          optimizer = tf.train.AdamOptimizer(
              learning_rate=_scheduled_value(
                  self._config.lr,
                  self._config.lr_schedule,
                  step,
                  "lr_" + name,
                  summary=True,
              ),
              name="adam_" + name,
          )
          minimize = optimizer.minimize(
              l, var_list=vs, global_step=step if i == 0 else None
          )  # Only update step once.
          minimize_ops.append(minimize)
      return tf.group(minimize_ops, name="enc_dec_ent_train_op")
  
imports: import collections
  import glob
  import itertools
  from compare_gan.gans import loss_lib
  import tensorflow.compat.v1
  import tensorflow_datasets
  from hific import archs
  from hific import helpers
  from hific.helpers import ModelMode
  from hific.helpers import ModelType
  from hific.helpers import TFDSArguments
label_desc: Use image node_id 29 for calling the HiFiC obj's underlying member method code with example usage: obj._make_enc_dec_entropy_train_op(step, loss, entropy_vars, transform_vars) and returns: tf