code: def get_clip_length(self, src_lengths: Tensor):
      predicted = (
          torch.tensor(
              self.targetlen_a,
              dtype=src_lengths.dtype,
              device=src_lengths.device,
          )
          * src_lengths
          * src_lengths
          + torch.tensor(
              self.targetlen_b,
              dtype=src_lengths.dtype,
              device=src_lengths.device,
          )
          * src_lengths
          + torch.tensor(
              self.targetlen_c,
              dtype=src_lengths.dtype,
              device=src_lengths.device,
          )
      )
      capped = torch.min(
          predicted,
          torch.tensor(
              self.targetlen_cap,
              dtype=src_lengths.dtype,
              device=src_lengths.device,
          ),
      )
      return capped
  
imports: from enum import Enum
  from typing import Dict, Optional, Tuple
  import torch
  from pytext.common.constants import TORCH_VERSION
  from pytext.config import ConfigBase
  from pytext.models.module import Module
  from pytext.torchscript.vocab import ScriptVocabulary
  from torch import Tensor
label_desc: Use image node_id 9 for calling the MaskedSequenceGenerator obj's underlying member method code with example usage: obj.get_clip_length(src_lengths) and returns: capped