class_docstr: ## Arithmetic Dataset
  
  This creates arithmetic addition problems and solutions with workings.
  We've only implemented addition so far.
  
  It's based on a character level tokenization.
class_docstr_tok: ["#","#","Arithmetic","Dataset","This","creates","arithmetic","addition","problems","and","solutions","with","workings",".","We","'ve","only","implemented","addition","so","far",".","It","'s","based","on","a","character","level","tokenization","."]
docstr: Generates an integer with `n_digit` number of digits
docstr_tok: ["Generates","an","integer","with","`","n_digit","`","number","of","digits"]
code: def make_int(n_digits: int):
      """
      Generates an integer with `n_digit` number of digits
      """
      res = 0
      for i in range(n_digits):
          d = (
              random.randrange(1, 11)
              if i == 0
              else random.randrange(0, 11)
          )
          res = res * 10 + d
  
      return res
  
imports: import random
  import string
  from typing import List
  import torch
  from labml.logger import Text
  from torch.utils.data import DataLoader, Dataset
  from labml import monit, logger, tracker
  from labml.configs import option
  from labml_nn.experiments.nlp_autoregression import NLPAutoRegressionConfigs, transpose_batch
label_desc: Use image node_id 2 for calling the ArithmeticDataset obj's underlying member method code with example usage: obj.make_int(n_digits) and returns: res