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: Calculate losses from a batch of inputs and data samples.
  
  Args:
      inputs (torch.Tensor): Input images of shape (N, C, H, W).
          Typically these should be mean centered and std scaled.
      data_samples (list[TextDetDataSample]): A list of N
          datasamples, containing meta information and gold annotations
          for each of the images.
  Returns:
      dict[str, Tensor]: A dictionary of loss components.
code: def loss(
      self,
      inputs: torch.Tensor,
      data_samples: Sequence[TextDetDataSample],
  ) -> Dict:
      """Calculate losses from a batch of inputs and data samples.
  
      Args:
          inputs (torch.Tensor): Input images of shape (N, C, H, W).
              Typically these should be mean centered and std scaled.
          data_samples (list[TextDetDataSample]): A list of N
              datasamples, containing meta information and gold annotations
              for each of the images.
      Returns:
          dict[str, Tensor]: A dictionary of loss components.
      """
      inputs = self.extract_feat(inputs)
      return self.det_head.loss(inputs, 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 3 for calling the SingleStageTextDetector obj's underlying member method code with example usage: obj.loss(inputs, data_samples) and returns: self