class_docstr: Filter samples based on text perplexity
  
  Example::
  
      sample 1: "I love textflint", score: 6.7
      sample 2: "I love TextFlinet", score: 6.34
code: def __init__(
      self, intervals=["0%", "20%"], device="cpu", max_sent_size=512
  ):
      if intervals is None:
          raise ValueError(
              "Intervals should be initialized for LMSubPopulation"
          )
      super().__init__(intervals=intervals)
      self.tokenizer = None
      self.model = None
      self.device = device
      self.max_sent_size = max_sent_size
  
file_path: textflint/textflint/generation/subpopulation/UTCN/lm.py
imports: import torch
  import math
  from ..subpopulation import SubPopulation
label_desc: Use image node_id 1 to create a new LMSubPopulation object from inherited base classes: SubPopulation with example: obj = LMSubPopulation(intervals, device, max_sent_size)