code: def get_norms(self):
      lines = []
      skip = set()
      if (
          self.constituency_composition
          == ConstituencyComposition.UNTIED_MAX
      ):
          skip = {"reduce_linear_weight", "reduce_linear_bias"}
          lines.append("reduce_linear:")
          for c_idx, c_open in enumerate(self.constituent_opens):
              lines.append(
                  "  %s weight %.6g bias %.6g"
                  % (
                      c_open,
                      torch.norm(
                          self.reduce_linear_weight[c_idx]
                      ).item(),
                      torch.norm(self.reduce_linear_bias[c_idx]).item(),
                  )
              )
      max_name_len = max(
          len(name)
          for name, param in self.named_parameters()
          if param.requires_grad and name not in skip
      )
      max_norm_len = max(
          len("%.6g" % torch.norm(param).item())
          for name, param in self.named_parameters()
          if param.requires_grad and name not in skip
      )
      format_string = (
          "%-"
          + str(max_name_len)
          + "s   norm %"
          + str(max_norm_len)
          + "s  zeros %d / %d"
      )
      for name, param in self.named_parameters():
          if param.requires_grad and name not in skip:
              zeros = torch.sum(param.abs() < 0.000001).item()
              norm = "%.6g" % torch.norm(param).item()
              lines.append(
                  format_string % (name, norm, zeros, param.nelement())
              )
      return lines
  
imports: from collections import namedtuple
  from enum import Enum
  import logging
  import math
  import random
  import torch
  import torch.nn
  from torch.nn.utils.rnn import pack_padded_sequence
  from stanza.models.common.bert_embedding import extract_bert_embeddings
  from stanza.models.common.maxout_linear import MaxoutLinear
  from stanza.models.common.utils import unsort
  from stanza.models.common.vocab import PAD_ID, UNK_ID
  from stanza.models.constituency.base_model import BaseModel
  from stanza.models.constituency.label_attention import LabelAttentionModule
  from stanza.models.constituency.lstm_tree_stack import LSTMTreeStack
  from stanza.models.constituency.parse_transitions import TransitionScheme
  from stanza.models.constituency.parse_tree import Tree
  from stanza.models.constituency.partitioned_transformer import PartitionedTransformerModule
  from stanza.models.constituency.positional_encoding import ConcatSinusoidalEncoding
  from stanza.models.constituency.transformer_tree_stack import TransformerTreeStack
  from stanza.models.constituency.tree_stack import TreeStack
  from stanza.models.constituency.utils import build_nonlinearity, initialize_linear
label_desc: Use image node_id 12 for calling the LSTMModel obj's underlying member method code with example usage: obj.get_norms() and returns: lines