code: def __init__(
      self,
      pretrained_embeddings_path="",
      embed_dim=0,
      embedding_init_strategy=EmbedInitStrategy.RANDOM,
      vocab_file="",
      vocab_size="",
      vocab_from_train_data=True,
      vocab_from_all_data=False,
      vocab_from_pretrained_embeddings=False,
      postprocessing=None,
      use_vocab=True,
      include_lengths=True,
      pad_token=VocabMeta.PAD_SEQ,
      init_token=None,
      eos_token=None,
      tokenize=data_utils.no_tokenize,
      nesting_field=None,
      **kwargs,
  ):
      super().__init__(
          pretrained_embeddings_path=pretrained_embeddings_path,
          embed_dim=embed_dim,
          embedding_init_strategy=embedding_init_strategy,
          vocab_file=vocab_file,
          vocab_size=vocab_size,
          vocab_from_train_data=vocab_from_train_data,
          vocab_from_all_data=vocab_from_all_data,
          vocab_from_pretrained_embeddings=vocab_from_pretrained_embeddings,
          postprocessing=postprocessing,
          use_vocab=use_vocab,
          include_lengths=include_lengths,
          pad_token=pad_token,
          init_token=init_token,
          eos_token=eos_token,
          tokenize=tokenize,
          nesting_field=nesting_field
          if nesting_field is not None
          else TextFeatureField(include_lengths=False, lower=False),
      )
  
imports: import re
  from typing import Any, Dict, Tuple, Union
  import torch
  from pytext.common.constants import Padding, VocabMeta
  from pytext.legacy import data
  from pytext.legacy.vocab import Vocab
  from pytext.utils import data, precision
  from pytorch.text.fb.utils.config_utils import EmbedInitStrategy
label_desc: Use image node_id 1 to create a new SeqFeatureField object from inherited base classes: VocabUsingNestedField with example: obj = SeqFeatureField(pretrained_embeddings_path, embed_dim, embedding_init_strategy, vocab_file, vocab_size, vocab_from_train_data, vocab_from_all_data, vocab_from_pretrained_embeddings, postprocessing, use_vocab, include_lengths, pad_token, init_token, eos_token, tokenize, nesting_field)