class_docstr: Convert between text-label and text-index 
class_docstr_tok: ["Convert","between","text-label","and","text-index"]
docstr: convert text-label into text-index.
  input:
      text: text labels of each image. [batch_size]
  
  output:
      text: concatenated text index for CTCLoss.
              [sum(text_lengths)] = [text_index_0 + text_index_1 + ... + text_index_(n - 1)]
      length: length of each text. [batch_size]
docstr_tok: ["convert","text-label","into","text-index",".","input",":","text",":","text","labels","of","each","image",".","[","batch_size","]","output",":","text",":","concatenated","text","index","for","CTCLoss",".","[","sum","(","text_lengths",")","]","=","[","text_index_0","+","text_index_1","+","...","+","text_index_","(","n","-","1",")","]","length",":","length","of","each","text",".","[","batch_size","]"]
code: def encode(self, text, batch_max_length=25):
      """convert text-label into text-index.
      input:
          text: text labels of each image. [batch_size]
  
      output:
          text: concatenated text index for CTCLoss.
                  [sum(text_lengths)] = [text_index_0 + text_index_1 + ... + text_index_(n - 1)]
          length: length of each text. [batch_size]
      """
      length = [len(s) for s in text]
      text = "".join(text)
      text = [self.dict[char] for char in text]
  
      return (torch.IntTensor(text), torch.IntTensor(length))
  
imports: from __future__ import print_function
  import torch
  import pickle
  import numpy
  import math
  import cv2
  from PIL import Image, JpegImagePlugin
  from scipy import ndimage
  import hashlib
  import sys, os
  from zipfile import ZipFile
  from .imgproc import loadImage
label_desc: Use image node_id 2 for calling the CTCLabelConverter obj's underlying member method code with example usage: obj.encode(text, batch_max_length) and returns: torch,  torch