class_docstr: Base class for encode-decode text spotter.
  
  Args:
      preprocessor (dict, optional): Config dict for preprocessor. Defaults
          to None.
      backbone (dict, optional): Backbone config. Defaults to None.
      encoder (dict, optional): Encoder config. If None, the output from
          backbone will be directly fed into ``decoder``. Defaults to None.
      decoder (dict, optional): Decoder config. Defaults to None.
      data_preprocessor (dict, optional): Model preprocessing config
          for processing the input image data. Keys allowed are
          ``to_rgb``(bool), ``pad_size_divisor``(int), ``pad_value``(int or
          float), ``mean``(int or float) and ``std``(int or float).
          Preprcessing order: 1. to rgb; 2. normalization 3. pad.
          Defaults to None.
      init_cfg (dict or list[dict], optional): Initialization configs.
          Defaults to None.
docstr: Predict results from a batch of inputs and data samples with post-
  processing.
  
  Args:
      inputs (torch.Tensor): Image input tensor.
      data_samples (list[TextRecogDataSample]): A list of N datasamples,
          containing meta information and gold annotations for each of
          the images.
  
  Returns:
      list[TextRecogDataSample]:  A list of N datasamples of prediction
      results. Results are stored in ``pred_text``.
code: def predict(
      self, inputs: torch.Tensor, data_samples: RecSampleList, **kwargs
  ) -> RecSampleList:
      """Predict results from a batch of inputs and data samples with post-
      processing.
  
      Args:
          inputs (torch.Tensor): Image input tensor.
          data_samples (list[TextRecogDataSample]): A list of N datasamples,
              containing meta information and gold annotations for each of
              the images.
  
      Returns:
          list[TextRecogDataSample]:  A list of N datasamples of prediction
          results. Results are stored in ``pred_text``.
      """
      feat = self.extract_feat(inputs)
      out_enc = None
      if self.with_encoder:
          out_enc = self.encoder(feat, data_samples)
      return self.decoder.predict(feat, out_enc, data_samples)
  
file_path: mmocr/projects/SPTS/spts/model/encoder_decoder_text_spotter.py
imports: from typing import Dict
  import torch
  from mmocr.registry import MODELS
  from mmocr.utils.typing_utils import ConfigType, InitConfigType, OptConfigType, OptRecSampleList, RecForwardResults, RecSampleList
  from .base_text_spotter import BaseTextSpotter
label_desc: Use image node_id 4 for calling the EncoderDecoderTextSpotter obj's underlying member method code with example usage: obj.predict(inputs, data_samples) and returns: self