class_docstr: Interface to the Stanford Parser
class_docstr_tok: ["Interface","to","the","Stanford","Parser"]
docstr: Use StanfordParser to parse multiple sentences. Takes multiple sentences as a
  list of strings.
  Each sentence will be automatically tokenized and tagged by the Stanford Parser.
  
  :param sentences: Input sentences to parse
  :type sentences: list(str)
  :rtype: iter(iter(Tree))
docstr_tok: ["Use","StanfordParser","to","parse","multiple","sentences",".","Takes","multiple","sentences","as","a","list","of","strings",".","Each","sentence","will","be","automatically","tokenized","and","tagged","by","the","Stanford","Parser",".",":","param","sentences",":","Input","sentences","to","parse",":","type","sentences",":","list","(","str",")",":","rtype",":","iter","(","iter","(","Tree",")",")"]
code: def raw_parse_sents(self, sentences, verbose=False):
      """
      Use StanfordParser to parse multiple sentences. Takes multiple sentences as a
      list of strings.
      Each sentence will be automatically tokenized and tagged by the Stanford Parser.
  
      :param sentences: Input sentences to parse
      :type sentences: list(str)
      :rtype: iter(iter(Tree))
      """
      cmd = [
          self._MAIN_CLASS,
          "-model",
          self.model_path,
          "-sentences",
          "newline",
          "-outputFormat",
          self._OUTPUT_FORMAT,
      ]
      return self._parse_trees_output(
          self._execute(cmd, "\n".join(sentences), verbose)
      )
  
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 5 for calling the GenericStanfordParser obj's underlying member method code with example usage: obj.raw_parse_sents(sentences, verbose) and returns: self