docstr: Encode a list of gts sequences into a np array and gives the corresponding*
  sequence lengths.
  
  Args:
  ----
      gts: list of ground-truth labels
  
  Returns:
  -------
      A tuple of 2 tensors: Encoded labels and sequence lengths (for each entry of the batch)
docstr_tok: ["Encode","a","list","of","gts","sequences","into","a","np","array","and","gives","the","corresponding","*","sequence","lengths",".","Args",":","--","--","gts",":","list","of","ground-truth","labels","Returns",":","--","--","--","-","A","tuple","of","2","tensors",":","Encoded","labels","and","sequence","lengths","(","for","each","entry","of","the","batch",")"]
code: def build_target(
      self,
      gts: List[str],
  ) -> Tuple[np.ndarray, List[int]]:
      """Encode a list of gts sequences into a np array and gives the corresponding*
      sequence lengths.
  
      Args:
      ----
          gts: list of ground-truth labels
  
      Returns:
      -------
          A tuple of 2 tensors: Encoded labels and sequence lengths (for each entry of the batch)
      """
      encoded = encode_sequences(
          sequences=gts,
          vocab=self.vocab,
          target_size=self.max_length,
          eos=len(self.vocab),
          sos=len(self.vocab) + 1,
          pad=len(self.vocab) + 2,
      )
      seq_len = [len(word) for word in gts]
      return encoded, seq_len
  
file_path: doctr/doctr/models/recognition/parseq/base.py
imports: from typing import List, Tuple
  import numpy
  from ....datasets import encode_sequences
  from ..core import RecognitionPostProcessor
label_desc: Use image node_id 1 for calling the _PARSeq obj's underlying member method code with example usage: obj.build_target(gts) and returns: encoded,  seq_len