class_docstr: A catalog that stores information about the datasets and how to obtain them.
  
  It contains a mapping from strings
  (which are names that identify a dataset, e.g. "coco_2014_train")
  to a function which parses the dataset and returns the samples in the
  format of `list[dict]`.
  
  The returned dicts should be in Detectron2 Dataset format (See DATASETS.md for details)
  if used with the data loader functionalities in `data/build.py,data/detection_transform.py`.
  
  The purpose of having this catalog is to make it easy to choose
  different datasets, by just using the strings in the config.
docstr: Call the registered function and return its results.
  
  Args:
      name (str): the name that identifies a dataset, e.g. "coco_2014_train".
  
  Returns:
      list[dict]: dataset annotations.0
docstr_tok: ["Call","the","registered","function","and","return","its","results",".","Args",":","name","(","str",")",":","the","name","that","identifies","a","dataset",",","e.g",".","``","coco_2014_train","''",".","Returns",":","list","[","dict","]",":","dataset","annotations.0"]
code: def get(name):
      """
      Call the registered function and return its results.
  
      Args:
          name (str): the name that identifies a dataset, e.g. "coco_2014_train".
  
      Returns:
          list[dict]: dataset annotations.0
      """
      try:
          f = DatasetCatalog._REGISTERED[name]
      except KeyError:
          raise KeyError(
              "Dataset '{}' is not registered! Available datasets are: {}".format(
                  name, ", ".join(DatasetCatalog._REGISTERED.keys())
              )
          )
      return f()
  
imports: import copy
  import logging
  import types
  from typing import List
label_desc: Use image node_id 2 for calling the DatasetCatalog obj's underlying member method code with example usage: obj.get(name) and returns: f