class_docstr: The class for implementing single stage text detector.
  
  Single-stage text detectors directly and densely predict bounding boxes or
  polygons on the output features of the backbone + neck (optional).
  
  Args:
      backbone (dict): Backbone config.
      neck (dict, optional): Neck config. If None, the output from backbone
          will be directly fed into ``det_head``.
      det_head (dict): Head config.
      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): Images of shape (N, C, H, W).
      data_samples (list[TextDetDataSample]): A list of N
          datasamples, containing meta information and gold annotations
          for each of the images.
  
  Returns:
      list[TextDetDataSample]: A list of N datasamples of prediction
      results.  Each DetDataSample usually contain
      'pred_instances'. And the ``pred_instances`` usually
      contains following keys.
  
          - scores (Tensor): Classification scores, has a shape
              (num_instance, )
          - labels (Tensor): Labels of bboxes, has a shape
              (num_instances, ).
          - bboxes (Tensor): Has a shape (num_instances, 4),
              the last dimension 4 arrange as (x1, y1, x2, y2).
          - polygons (list[np.ndarray]): The length is num_instances.
              Each element represents the polygon of the
              instance, in (xn, yn) order.
code: def predict(
      self,
      inputs: torch.Tensor,
      data_samples: Sequence[TextDetDataSample],
  ) -> Sequence[TextDetDataSample]:
      """Predict results from a batch of inputs and data samples with post-
      processing.
  
      Args:
          inputs (torch.Tensor): Images of shape (N, C, H, W).
          data_samples (list[TextDetDataSample]): A list of N
              datasamples, containing meta information and gold annotations
              for each of the images.
  
      Returns:
          list[TextDetDataSample]: A list of N datasamples of prediction
          results.  Each DetDataSample usually contain
          'pred_instances'. And the ``pred_instances`` usually
          contains following keys.
  
              - scores (Tensor): Classification scores, has a shape
                  (num_instance, )
              - labels (Tensor): Labels of bboxes, has a shape
                  (num_instances, ).
              - bboxes (Tensor): Has a shape (num_instances, 4),
                  the last dimension 4 arrange as (x1, y1, x2, y2).
              - polygons (list[np.ndarray]): The length is num_instances.
                  Each element represents the polygon of the
                  instance, in (xn, yn) order.
      """
      x = self.extract_feat(inputs)
      return self.det_head.predict(x, data_samples)
  
file_path: mmocr/mmocr/models/textdet/detectors/single_stage_text_detector.py
imports: from typing import Dict, Optional, Sequence
  import torch
  from mmocr.registry import MODELS
  from mmocr.structures import TextDetDataSample
  from .base import BaseTextDetector
label_desc: Use image node_id 4 for calling the SingleStageTextDetector obj's underlying member method code with example usage: obj.predict(inputs, data_samples) and returns: self