code: def output_shape(self):
      return torch.Size([self.max_sequence_length, self.vocab_size])
  
file_path: ludwig/ludwig/decoders/sequence_tagger.py
imports: import logging
  from typing import Dict
  import torch
  from ludwig.api_annotations import DeveloperAPI
  from ludwig.constants import HIDDEN, LOGITS, PREDICTIONS, PROBABILITIES, SEQUENCE, TEXT
  from ludwig.decoders.base import Decoder
  from ludwig.decoders.registry import register_decoder
  from ludwig.modules.attention_modules import MultiHeadSelfAttention
  from ludwig.schema.decoders.sequence_decoders import SequenceTaggerDecoderConfig
  from ludwig.utils.torch_utils import Dense
label_desc: Use image node_id 6 for calling the SequenceTaggerDecoder obj's underlying member method code with example usage: obj.output_shape() and returns: torch