class_docstr: Defines the GlyphAzznProblem class.
class_docstr_tok: ["Defines","the","GlyphAzznProblem","class","."]
code: def generate_data(self, data_dir, tmp_dir, task_id=-1):
      del tmp_dir, task_id  # unused argument
      filepath_fns = {
          problem.DatasetSplit.TRAIN: self.training_filepaths,
          problem.DatasetSplit.EVAL: self.dev_filepaths,
          problem.DatasetSplit.TEST: self.test_filepaths,
      }
  
      split_paths = [
          (
              split["split"],
              filepath_fns[split["split"]](
                  data_dir, split["shards"], shuffled=False
              ),
          )
          for split in self.dataset_splits
      ]
      all_paths = []
      for _, paths in split_paths:
          all_paths.extend(paths)
  
      if self.is_generate_per_split:
          for split, paths in split_paths:
              generator_utils.generate_files(
                  self.generate_encoded_samples(
                      data_dir, tmp_dir, split
                  ),
                  paths,
              )
      else:
          generator_utils.generate_files(
              self.generate_encoded_samples(
                  data_dir, tmp_dir, problem.DatasetSplit.TRAIN
              ),
              all_paths,
          )
  
      generator_utils.shuffle_dataset(all_paths)
  
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 6 for calling the GlyphAzznProblem obj's underlying member method code with example usage: obj.generate_data(data_dir, tmp_dir, task_id) without return types