class_docstr: Registry of dataset registries.
  
  Each namespace is associated with one or more registers. Those registers can
  be any type of register, e.g. code on Github or datasets stored somewhere.
  
  Attributes:
    namespace_config: config where to find the datasets of a namespace.
    registers_per_namespace: per namespace a list of registers it consists of.
class_docstr_tok: ["Registry","of","dataset","registries",".","Each","namespace","is","associated","with","one","or","more","registers",".","Those","registers","can","be","any","type","of","register",",","e.g",".","code","on","Github","or","datasets","stored","somewhere",".","Attributes",":","namespace_config",":","config","where","to","find","the","datasets","of","a","namespace",".","registers_per_namespace",":","per","namespace","a","list","of","registers","it","consists","of","."]
code: def has_namespace(self, namespace: str) -> bool:
      if not namespace:
          return False
      return namespace in self.registers_per_namespace
  
imports: import dataclasses
  import difflib
  import functools
  import os
  from typing import Any, Iterable, List, Mapping, Type
  from absl import logging
  from etils import epath
  from tensorflow_datasets.core import dataset_builder
  from tensorflow_datasets.core import github_api
  from tensorflow_datasets.core import naming
  from tensorflow_datasets.core import registered
  from tensorflow_datasets.core import utils
  from tensorflow_datasets.core.community import register_base
  from tensorflow_datasets.core.community import register_package
  from tensorflow_datasets.core.community import register_path
  from tensorflow_datasets.core.utils import gcs_utils
  import toml
label_desc: Use image node_id 2 for calling the DatasetRegistry obj's underlying member method code with example usage: obj.has_namespace(namespace) and returns: unknown, False