docstr: Analyze text and return pretty format.
  
  Args:
      text: string, the input text.
      tokenizer: Tokenize input sentence. Default tokenizer is `str.split`.
  
  Returns:
      res: dict.
docstr_tok: ["Analyze","text","and","return","pretty","format",".","Args",":","text",":","string",",","the","input","text",".","tokenizer",":","Tokenize","input","sentence",".","Default","tokenizer","is","`","str.split","`",".","Returns",":","res",":","dict","."]
code: def analyze(self, text, tokenizer=str.split):
      """Analyze text and return pretty format.
  
      Args:
          text: string, the input text.
          tokenizer: Tokenize input sentence. Default tokenizer is `str.split`.
  
      Returns:
          res: dict.
      """
      if not self.tagger:
          self.tagger = Tagger(
              self.model, preprocessor=self.p, tokenizer=tokenizer
          )
  
      return self.tagger.analyze(text)
  
file_path: anago/anago/wrapper.py
imports: from seqeval.metrics import f1_score
  from anago.models import BiLSTMCRF, save_model, load_model
  from anago.preprocessing import IndexTransformer
  from anago.tagger import Tagger
  from anago.trainer import Trainer
  from anago.utils import filter_embeddings
label_desc: Use image node_id 5 for calling the Sequence obj's underlying member method code with example usage: obj.analyze(text, tokenizer) and returns: self