class_docstr: Defines the GlyphAzznProblem class.
class_docstr_tok: ["Defines","the","GlyphAzznProblem","class","."]
code: def decode_hooks(self):
      to_img = svg_utils.create_image_conversion_fn(
          1, categorical=self.categorical
      )
  
      def sample_image(decode_hook_args):
          """Converts decoded predictions into summaries."""
          hparams = decode_hook_args.hparams
  
          if not hasattr(self, "mean_npz"):
              mean_filename = os.path.join(hparams.data_dir, "mean.npz")
              stdev_filename = os.path.join(
                  hparams.data_dir, "stdev.npz"
              )
              with tf.gfile.open(mean_filename, "r") as f:
                  self.mean_npz = np.load(f)
              with tf.gfile.open(stdev_filename, "r") as f:
                  self.stdev_npz = np.load(f)
  
          values = []
          for pred_dict in decode_hook_args.predictions[0]:
              if hparams.just_render:
                  # vae mode, outputs is image, just do image summary and continue
                  values.append(
                      svg_utils.make_image_summary(
                          pred_dict["outputs"], "rendered_outputs"
                      )
                  )
                  values.append(
                      svg_utils.make_image_summary(
                          pred_dict["targets"], "rendered_targets"
                      )
                  )
                  continue
  
              if common_layers.shape_list(pred_dict["targets"])[0] == 1:
                  continue
  
              # undo normalize (via gaussian)
              denorm_outputs = (
                  pred_dict["outputs"] * self.stdev_npz
              ) + self.mean_npz
              denorm_targets = (
                  pred_dict["targets"] * self.stdev_npz
              ) + self.mean_npz
  
              # simple cmds are 10 dim (4 one-hot, 6 args).
              # Convert to full SVG spec dimensionality so we can convert it to text.
              denorm_outputs = svg_utils.make_simple_cmds_long(
                  denorm_outputs
              )
              denorm_targets = svg_utils.make_simple_cmds_long(
                  denorm_targets
              )
  
              # sampled text summary
              output_svg = to_img(
                  [np.reshape(denorm_outputs, [-1, 30])]
              )
              values.append(
                  svg_utils.make_text_summary_value(
                      output_svg, "img/sampled"
                  )
              )
  
              # original text summary
              target_svg = to_img(
                  [np.reshape(denorm_targets, [-1, 30])]
              )
              values.append(
                  svg_utils.make_text_summary_value(
                      target_svg, "img/og"
                  )
              )
  
          return values
  
      return [sample_image]
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  from absl import logging
  from magenta.models.svg_vae import svg_utils
  import numpy
  from tensor2tensor.data_generators import generator_utils
  from tensor2tensor.data_generators import problem
  from tensor2tensor.layers import common_layers
  from tensor2tensor.layers import modalities
  from tensor2tensor.utils import registry
  import tensorflow.compat.v1
label_desc: Use image node_id 14 for calling the GlyphAzznProblem obj's underlying member method code with example usage: obj.decode_hooks() and returns: list, values