class_docstr: A fixed-sized Dictionary that operates on integer-valued tokens
  wth a trivial (identity) token <-> id mapping.
  Special symbols (bos, eos, ...) have ids above n_units.
class_docstr_tok: ["A","fixed-sized","Dictionary","that","operates","on","integer-valued","tokens","wth","a","trivial","(","identity",")","token","<","-",">","id","mapping",".","Special","symbols","(","bos",",","eos",",","...",")","have","ids","above","n_units","."]
code: def encode_line(
      self, line, append_eos=True, prepend_bos=False
  ) -> torch.IntTensor:
      words = [int(x) for x in line.split()]
      if self.clip:
          words = [min(self.n_units - 1, word) for word in words]
      if prepend_bos:
          words = [self.bos_index] + words
      if append_eos:
          words.append(self.eos_index)
      ids = torch.IntTensor(words)
      return ids
  
imports: import logging
  import sys
  import torch
  from dataclasses import dataclass, field
  from typing import List, Optional, Tuple
  from fairseq.data import Dictionary
  from fairseq.data.codedataset import ExpressiveCodeDataConfig, CodeDataset
  from fairseq.dataclass.configs import FairseqDataclass
  from fairseq.tasks import register_task
  from fairseq.tasks.fairseq_task import FairseqTask
  from omegaconf import MISSING, DictConfig
label_desc: Use image node_id 2 for calling the UnitDictionary obj's underlying member method code with example usage: obj.encode_line(line, append_eos, prepend_bos) and returns: ids