class_docstr: Decoder module in `SVTR <https://arxiv.org/abs/2205.00159>`_.
  
  Args:
      in_channels (int): The num of input channels.
      dictionary (Union[Dict, Dictionary]): The config for `Dictionary` or
          the instance of `Dictionary`. Defaults to None.
      module_loss (Optional[Dict], optional): Cfg to build module_loss.
          Defaults to None.
      postprocessor (Optional[Dict], optional): Cfg to build postprocessor.
          Defaults to None.
      max_seq_len (int, optional): Maximum output sequence length :math:`T`.
          Defaults to 25.
      init_cfg (dict or list[dict], optional): Initialization configs.
          Defaults to None.
docstr: Forward for testing.
  
  Args:
      feat (torch.Tensor, optional): The feature map. Defaults to None.
      out_enc (torch.Tensor, optional): Encoder output from encoder of
          shape :math:`(N, 1, H, W)`. Defaults to None.
      data_samples (Sequence[TextRecogDataSample]): Batch of
          TextRecogDataSample, containing gt_text information. Defaults
          to None.
  Returns:
      Tensor: Character probabilities. of shape
      :math:`(N, self.max_seq_len, C)` where :math:`C` is
      ``num_classes``.
code: def forward_test(
      self,
      feat: Optional[torch.Tensor] = None,
      out_enc: Optional[torch.Tensor] = None,
      data_samples: Optional[Sequence[TextRecogDataSample]] = None,
  ) -> torch.Tensor:
      """Forward for testing.
  
      Args:
          feat (torch.Tensor, optional): The feature map. Defaults to None.
          out_enc (torch.Tensor, optional): Encoder output from encoder of
              shape :math:`(N, 1, H, W)`. Defaults to None.
          data_samples (Sequence[TextRecogDataSample]): Batch of
              TextRecogDataSample, containing gt_text information. Defaults
              to None.
      Returns:
          Tensor: Character probabilities. of shape
          :math:`(N, self.max_seq_len, C)` where :math:`C` is
          ``num_classes``.
      """
      return self.softmax(
          self.forward_train(feat, out_enc, data_samples)
      )
  
file_path: mmocr/mmocr/models/textrecog/decoders/svtr_decoder.py
imports: from typing import Dict, List, Optional, Sequence, Union
  import torch
  import torch.nn
  from mmocr.models.common.dictionary import Dictionary
  from mmocr.registry import MODELS
  from mmocr.structures import TextRecogDataSample
  from .base import BaseDecoder
label_desc: Use image node_id 3 for calling the SVTRDecoder obj's underlying member method code with example usage: obj.forward_test(feat, out_enc, data_samples) and returns: self