docstr: Saving segmentation results in pd.DataFrame format (per image)
  
  Parameters
  ----------
  pred
      List containing segmentation results for one image
  data
      Pandas data frame or dict containing the image information to be tested
  result_path
      Path to save result
  Returns
  -------
  The paths of the segmentation results as pandas DataFrame
code: def save_segmentation_result(
      self,
      pred: Iterable,
      data: Union[pd.DataFrame, Dict],
      result_path: str,
  ):
      """
      Saving segmentation results in pd.DataFrame format (per image)
  
      Parameters
      ----------
      pred
          List containing segmentation results for one image
      data
          Pandas data frame or dict containing the image information to be tested
      result_path
          Path to save result
      Returns
      -------
      The paths of the segmentation results as pandas DataFrame
      """
  
      def show_mask(mask, ax):
          color = np.concatenate(
              [np.random.random(3), np.array([0.6])], axis=0
          )
          h, w = mask.shape[-2:]
          mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
          ax.imshow(mask_image)
  
      image_column_name = self.get_image_column_name(data)
      if isinstance(data, dict):
          image_names = data[image_column_name]
      else:
          image_names = data[image_column_name].to_list()
      results = []
  
      mask_path = os.path.join(result_path, "masks")
      txt_path = os.path.join(result_path, "result.txt")
      os.makedirs(mask_path, exist_ok=True)
      for image_pred, image_name in zip(pred, image_names):
          if self._output_shape == 1:
              mask = Image.fromarray(image_pred.squeeze(axis=0))
              per_mask_path = os.path.join(
                  mask_path, os.path.basename(image_name)
              )
              mask.save(per_mask_path)
          else:
              masks = []
              classes = np.unique(image_pred)
              for class_id in classes:
                  if class_id == 0:  # bg
                      continue
                  masks.append(image_pred == class_id)
  
              for mask in masks:
                  show_mask(mask, plt.gca())
              mask_name = ""
              for i in os.path.basename(image_name).split(".")[:-1]:
                  mask_name += i
              per_mask_path = os.path.join(
                  mask_path, os.path.basename(image_name)
              )
              plt.axis("off")
              plt.savefig(
                  per_mask_path,
                  bbox_inches="tight",
                  dpi=300,
                  pad_inches=0.0,
              )
  
          results.append([image_name, per_mask_path])
  
      result_df = pd.DataFrame(results, columns=["image", "mask"])
      result_df.to_csv(txt_path, index=False)
      return result_df
  
imports: import logging
  import os
  from typing import Dict, Iterable, List, Optional, Union
  import matplotlib.pyplot
  import numpy
  import pandas
  import torch
  import torch.nn.functional
  from omegaconf import OmegaConf
  from PIL import Image
  from scipy.special import softmax
  from autogluon.core.metrics import Scorer
  from ..constants import LABEL, LOGITS, SEMANTIC_MASK, SEMANTIC_SEGMENTATION, SEMANTIC_SEGMENTATION_IMG
  from ..optimization.lit_semantic_seg import SemanticSegmentationLitModule
  from ..optimization.semantic_seg_metrics import Balanced_Error_Rate_Pred
  from ..optimization.semantic_seg_metrics import Binary_IoU_Pred
  from ..optimization.semantic_seg_metrics import COD_METRICS_NAMES_Pred
  from ..optimization.semantic_seg_metrics import Multiclass_IoU_Pred
  from ..optimization.utils import get_loss_func, get_norm_layer_param_names, get_trainable_params_efficient_finetune
  from ..utils import extract_from_output, setup_save_path
  from .base import BaseLearner
  from ..constants import BER, EM, FM, IOU, MAE, SEMANTIC_SEGMENTATION, SM
label_desc: Use image node_id 13 for calling the SemanticSegmentationLearner obj's underlying member method code with example usage: obj.save_segmentation_result(pred, data, result_path) and returns: result_df