class_docstr: English Gigaword summarization dataset.
class_docstr_tok: ["English","Gigaword","summarization","dataset","."]
docstr: Dataset constructor, loads the data into memory.
docstr_tok: ["Dataset","constructor",",","loads","the","data","into","memory","."]
code: def __init__(
      self, split="validation", max_examples: Optional[int] = None
  ):
      """Dataset constructor, loads the data into memory."""
      ds = tfds.load("gigaword", split=split)
  
      self._examples = []  # populate this with data records
      if max_examples is not None:
          ds = ds.take(max_examples)
      for record in ds:
          self._examples.append(
              {
                  "document": record["document"]
                  .numpy()
                  .decode("utf-8"),
                  "reference": record["summary"]
                  .numpy()
                  .decode("utf-8"),
              }
          )
  
file_path: lit/lit_nlp/examples/datasets/summarization.py
imports: from typing import Optional
  from lit_nlp.api import dataset
  from lit_nlp.api import types
  from lit_nlp.lib import file_cache
  import pandas
  import tensorflow_datasets
label_desc: Use image node_id 1 to create a new GigawordData object from inherited base classes: lit_dataset with example: obj = GigawordData(split, max_examples)