class_docstr: Base decoder for text recognition, build the loss and postprocessor.
  
  Args:
      dictionary (dict or :obj:`Dictionary`): The config for `Dictionary` or
          the instance of `Dictionary`.
      loss (dict, optional): Config to build loss. Defaults to None.
      postprocessor (dict, optional): Config to build postprocessor.
          Defaults to None.
      max_seq_len (int): Maximum sequence length. The
          sequence is usually generated from decoder. Defaults to 40.
      init_cfg (dict or list[dict], optional): Initialization configs.
          Defaults to None.
docstr: Perform forward propagation of the decoder and postprocessor.
  
  Args:
      feat (Tensor, optional): Features from the backbone. Defaults
          to None.
      out_enc (Tensor, optional): Features from the encoder. Defaults
          to None.
      data_samples (list[TextRecogDataSample]): A list of N datasamples,
          containing meta information and gold annotations for each of
          the images. Defaults to None.
  
  Returns:
      list[TextRecogDataSample]:  A list of N datasamples of prediction
      results. Results are stored in ``pred_text``.
docstr_tok: ["Perform","forward","propagation","of","the","decoder","and","postprocessor",".","Args",":","feat","(","Tensor",",","optional",")",":","Features","from","the","backbone",".","Defaults","to","None",".","out_enc","(","Tensor",",","optional",")",":","Features","from","the","encoder",".","Defaults","to","None",".","data_samples","(","list","[","TextRecogDataSample","]",")",":","A","list","of","N","datasamples",",","containing","meta","information","and","gold","annotations","for","each","of","the","images",".","Defaults","to","None",".","Returns",":","list","[","TextRecogDataSample","]",":","A","list","of","N","datasamples","of","prediction","results",".","Results","are","stored","in","``","pred_text","``","."]
code: def predict(
      self,
      feat: Optional[torch.Tensor] = None,
      out_enc: Optional[torch.Tensor] = None,
      data_samples: Optional[Sequence[TextRecogDataSample]] = None,
  ) -> Sequence[TextRecogDataSample]:
      """Perform forward propagation of the decoder and postprocessor.
  
      Args:
          feat (Tensor, optional): Features from the backbone. Defaults
              to None.
          out_enc (Tensor, optional): Features from the encoder. Defaults
              to None.
          data_samples (list[TextRecogDataSample]): A list of N datasamples,
              containing meta information and gold annotations for each of
              the images. Defaults to None.
  
      Returns:
          list[TextRecogDataSample]:  A list of N datasamples of prediction
          results. Results are stored in ``pred_text``.
      """
      out_dec = self(feat, out_enc, data_samples)
      return self.postprocessor(out_dec, data_samples)
  
file_path: mmocr/mmocr/models/textrecog/decoders/base.py
imports: from typing import Dict, List, Optional, Sequence, Union
  import torch
  from mmengine.model import BaseModule
  from mmocr.models.common.dictionary import Dictionary
  from mmocr.registry import MODELS, TASK_UTILS
  from mmocr.structures import TextRecogDataSample
label_desc: Use image node_id 5 for calling the BaseDecoder obj's underlying member method code with example usage: obj.predict(feat, out_enc, data_samples) and returns: self