docstr: Builds the document-topic distribution showing the topic probability distirbution
  for each document in <texts> with respect to the learned topic space.
  Args:
      texts (list of str): list of text documents
      threshold (float): If not None, documents with whose highest topic probability
                         is less than threshold are filtered out.
docstr_tok: ["Builds","the","document-topic","distribution","showing","the","topic","probability","distirbution","for","each","document","in","<","texts",">","with","respect","to","the","learned","topic","space",".","Args",":","texts","(","list","of","str",")",":","list","of","text","documents","threshold","(","float",")",":","If","not","None",",","documents","with","whose","highest","topic","probability","is","less","than","threshold","are","filtered","out","."]
code: def build(self, texts, threshold=None):
      """
      Builds the document-topic distribution showing the topic probability distirbution
      for each document in <texts> with respect to the learned topic space.
      Args:
          texts (list of str): list of text documents
          threshold (float): If not None, documents with whose highest topic probability
                             is less than threshold are filtered out.
      """
      if threshold is not None:
          doc_topics, bool_array = self.predict(
              texts, threshold=threshold
          )
      else:
          doc_topics = self.predict(texts)
          bool_array = np.array([True] * len(texts))
  
      self.doc_topics = doc_topics
      self.bool_array = bool_array
  
      texts = [text for i, text in enumerate(texts) if bool_array[i]]
      self.topic_dict = self._rank_documents(
          texts, doc_topics=doc_topics
      )
      return
  
imports: import time
  from ..None import utils
  from ..imports import *
  from .None import preprocessor
  from .None import textutils
label_desc: Use image node_id 9 for calling the TopicModel obj's underlying member method code with example usage: obj.build(texts, threshold) without return types