class_docstr: >>> from nltk.parse.stanford import StanfordNeuralDependencyParser # doctest: +SKIP
  >>> dep_parser=StanfordNeuralDependencyParser(java_options='-mx4g')# doctest: +SKIP
  
  >>> [parse.tree() for parse in dep_parser.raw_parse("The quick brown fox jumps over the lazy dog.")] # doctest: +NORMALIZE_WHITESPACE +SKIP
  [Tree('jumps', [Tree('fox', ['The', 'quick', 'brown']), Tree('dog', ['over', 'the', 'lazy']), '.'])]
  
  >>> [list(parse.triples()) for parse in dep_parser.raw_parse("The quick brown fox jumps over the lazy dog.")] # doctest: +NORMALIZE_WHITESPACE +SKIP
  [[((u'jumps', u'VBZ'), u'nsubj', (u'fox', u'NN')), ((u'fox', u'NN'), u'det',
  (u'The', u'DT')), ((u'fox', u'NN'), u'amod', (u'quick', u'JJ')), ((u'fox', u'NN'),
  u'amod', (u'brown', u'JJ')), ((u'jumps', u'VBZ'), u'nmod', (u'dog', u'NN')),
  ((u'dog', u'NN'), u'case', (u'over', u'IN')), ((u'dog', u'NN'), u'det',
  (u'the', u'DT')), ((u'dog', u'NN'), u'amod', (u'lazy', u'JJ')), ((u'jumps', u'VBZ'),
  u'punct', (u'.', u'.'))]]
  
  >>> sum([[parse.tree() for parse in dep_graphs] for dep_graphs in dep_parser.raw_parse_sents((
  ...     "The quick brown fox jumps over the lazy dog.",
  ...     "The quick grey wolf jumps over the lazy fox."
  ... ))], []) # doctest: +NORMALIZE_WHITESPACE +SKIP
  [Tree('jumps', [Tree('fox', ['The', 'quick', 'brown']), Tree('dog', ['over',
  'the', 'lazy']), '.']), Tree('jumps', [Tree('wolf', ['The', 'quick', 'grey']),
  Tree('fox', ['over', 'the', 'lazy']), '.'])]
  
  >>> sum([[parse.tree() for parse in dep_graphs] for dep_graphs in dep_parser.parse_sents((
  ...     "I 'm a dog".split(),
  ...     "This is my friends ' cat ( the tabby )".split(),
  ... ))], []) # doctest: +NORMALIZE_WHITESPACE +SKIP
  [Tree('dog', ['I', "'m", 'a']), Tree('cat', ['This', 'is', Tree('friends',
  ['my', "'"]), Tree('tabby', ['-LRB-', 'the', '-RRB-'])])]
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docstr: Currently unimplemented because the neural dependency parser (and
  the StanfordCoreNLP pipeline class) doesn't support passing in pre-
  tagged tokens.
docstr_tok: ["Currently","unimplemented","because","the","neural","dependency","parser","(","and","the","StanfordCoreNLP","pipeline","class",")","does","n't","support","passing","in","pre-","tagged","tokens","."]
code: def tagged_parse_sents(self, sentences, verbose=False):
      """
      Currently unimplemented because the neural dependency parser (and
      the StanfordCoreNLP pipeline class) doesn't support passing in pre-
      tagged tokens.
      """
      raise NotImplementedError(
          "tagged_parse[_sents] is not supported by "
          "StanfordNeuralDependencyParser; use "
          "parse[_sents] or raw_parse[_sents] instead."
      )
  
imports: import os
  import tempfile
  import warnings
  from subprocess import PIPE
  from nltk.internals import _java_options, config_java, find_jar_iter, find_jars_within_path, java
  from nltk.parse.api import ParserI
  from nltk.parse.dependencygraph import DependencyGraph
  from nltk.tree import Tree
label_desc: Use image node_id 2 for calling the StanfordNeuralDependencyParser obj's underlying member method code with example usage: obj.tagged_parse_sents(sentences, verbose) without return types