class_docstr: A ``DatasetReader`` knows how to turn a file containing a dataset into a collection
  of ``Instance`` s.  To implement your own, just override the `_read(file_path)` method
  to return an ``Iterable`` of the instances. This could be a list containing the instances
  or a lazy generator that returns them one at a time.
  
  All parameters necessary to _read the data apart from the filepath should be passed
  to the constructor of the ``DatasetReader``.
  
  Parameters
  ----------
  lazy : ``bool``, optional (default=False)
      If this is true, ``instances()`` will return an object whose ``__iter__`` method
      reloads the dataset each time it's called. Otherwise, ``instances()`` returns a list.
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docstr: Does whatever tokenization or processing is necessary to go from textual input to an
  ``Instance``.  The primary intended use for this is with a
  :class:`~allennlp.service.predictors.predictor.Predictor`, which gets text input as a JSON
  object and needs to process it to be input to a model.
  
  The intent here is to share code between :func:`_read` and what happens at
  model serving time, or any other time you want to make a prediction from new data.  We need
  to process the data in the same way it was done at training time.  Allowing the
  ``DatasetReader`` to process new text lets us accomplish this, as we can just call
  ``DatasetReader.text_to_instance`` when serving predictions.
  
  The input type here is rather vaguely specified, unfortunately.  The ``Predictor`` will
  have to make some assumptions about the kind of ``DatasetReader`` that it's using, in order
  to pass it the right information.
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code: def text_to_instance(self, *inputs):
      """
      Does whatever tokenization or processing is necessary to go from textual input to an
      ``Instance``.  The primary intended use for this is with a
      :class:`~allennlp.service.predictors.predictor.Predictor`, which gets text input as a JSON
      object and needs to process it to be input to a model.
  
      The intent here is to share code between :func:`_read` and what happens at
      model serving time, or any other time you want to make a prediction from new data.  We need
      to process the data in the same way it was done at training time.  Allowing the
      ``DatasetReader`` to process new text lets us accomplish this, as we can just call
      ``DatasetReader.text_to_instance`` when serving predictions.
  
      The input type here is rather vaguely specified, unfortunately.  The ``Predictor`` will
      have to make some assumptions about the kind of ``DatasetReader`` that it's using, in order
      to pass it the right information.
      """
      raise NotImplementedError
  
file_path: magnitude/pymagnitude/third_party/allennlp/data/dataset_readers/dataset_reader.py
imports: from __future__ import absolute_import
  import logging
  from allennlp.data.instance import Instance
  from allennlp.common import Tqdm
  from allennlp.common.checks import ConfigurationError
  from allennlp.common.registrable import Registrable
label_desc: Use image node_id 4 for calling the DatasetReader obj's underlying member method code with example usage: obj.text_to_instance() without return types