class_docstr: Reads data in the "machine-readable derivation" format of the CCGbank dataset.
  (see https://catalog.ldc.upenn.edu/docs/LDC2005T13/CCGbankManual.pdf, section D.2)
  
  In particular, it pulls out the leaf nodes, which are represented as
  
      (<L ccg_category modified_pos original_pos token predicate_arg_category>)
  
  The tarballed version of the dataset contains many files worth of this data,
  in files /data/AUTO/xx/wsj_xxxx.auto
  
  This dataset reader expects a single text file. Accordingly, if you're using that dataset,
  you'll need to first concatenate some of those files into a training set, a validation set,
  and a test set.
  
  Parameters
  ----------
  token_indexers : ``Dict[str, TokenIndexer]``, optional (default=``{"tokens": SingleIdTokenIndexer()}``)
      We use this to define the input representation for the text.  See :class:`TokenIndexer`.
      Note that the `output` tags will always correspond to single token IDs based on how they
      are pre-tokenised in the data file.
  lazy : ``bool``, optional, (default = ``False``)
      Whether or not instances can be consumed lazily.
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code: def _read(self, file_path):
      # if `file_path` is a URL, redirect to the cache
      file_path = cached_path(file_path)
      logger.info(
          "Reading instances from lines in file at: %s", file_path
      )
  
      with open(file_path) as input_file:
          for line in input_file:
              if line.startswith("(<"):
                  # Each leaf looks like
                  # (<L ccg_category modified_pos original_pos token predicate_arg_category>)
                  leaves = re.findall("<L (.*?)>", line)
  
                  # Use magic unzipping trick to split into tuples
                  tuples = izip(*[leaf.split() for leaf in leaves])
  
                  # Convert to lists and assign to variables.
                  (
                      ccg_categories,
                      modified_pos_tags,
                      original_pos_tags,
                      tokens,
                      predicate_arg_categories,
                  ) = [list(result) for result in tuples]
  
                  yield self.text_to_instance(
                      tokens,
                      ccg_categories,
                      modified_pos_tags,
                      original_pos_tags,
                      predicate_arg_categories,
                  )
  
file_path: magnitude/pymagnitude/third_party/allennlp/data/dataset_readers/ccgbank.py
imports: from __future__ import with_statement
  from __future__ import absolute_import
  import logging
  import re
  from allennlp.common.file_utils import cached_path
  from allennlp.data.dataset_readers.dataset_reader import DatasetReader
  from allennlp.data.fields import Field, TextField, SequenceLabelField
  from allennlp.data.instance import Instance
  from allennlp.data.token_indexers import TokenIndexer, SingleIdTokenIndexer
  from allennlp.data.tokenizers import Token
  from io import open
label_desc: Use image node_id 2 for calling the CcgBankDatasetReader obj's underlying member method code with example usage: obj._read(file_path) without return types