code: def __init__(self, flair_embeddings: List[FlairEmbeddings]) -> None:
      super().__init__()
  
      self.embeddings = flair_embeddings
      self.name = "document_lm"
  
      # IMPORTANT: add embeddings as torch modules
      for i, embedding in enumerate(flair_embeddings):
          self.add_module(f"lm_embedding_{i}", embedding)
          if not embedding.static_embeddings:
              self.static_embeddings = False
  
      self._embedding_length: int = sum(
          embedding.embedding_length for embedding in flair_embeddings
      )
      self.eval()
  
imports: import logging
  from typing import Any, Dict, List, Optional, Union, cast
  import torch
  from sklearn.feature_extraction.text import TfidfVectorizer
  from torch.nn import RNNBase
  from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
  import flair
  from flair.data import Sentence
  from flair.embeddings.base import DocumentEmbeddings, load_embeddings, register_embeddings
  from flair.embeddings.token import FlairEmbeddings, StackedEmbeddings, TokenEmbeddings
  from flair.embeddings.transformer import TransformerEmbeddings, TransformerOnnxDocumentEmbeddings
  from flair.nn import LockedDropout, WordDropout
label_desc: Use image node_id 1 to create a new DocumentLMEmbeddings object from inherited base classes: DocumentEmbeddings with example: obj = DocumentLMEmbeddings(flair_embeddings)