class_docstr: Adds Click Signal to the input image
  
  Args:
      image: source image, defaults to ``"image"``
      foreground: 2D click indices as list, defaults to ``"foreground"``
      bb_size: single integer size, defines a bounding box like (bb_size, bb_size)
      gaussian: add gaussian
      sigma: sigma value for gaussian
      truncated: spreads how many stds for gaussian
      add_exclusion_map: add exclusion map/signal
class_docstr_tok: ["Adds","Click","Signal","to","the","input","image","Args",":","image",":","source","image",",","defaults","to","``","``","image","''","``","foreground",":","2D","click","indices","as","list",",","defaults","to","``","``","foreground","''","``","bb_size",":","single","integer","size",",","defines","a","bounding","box","like","(","bb_size",",","bb_size",")","gaussian",":","add","gaussian","sigma",":","sigma","value","for","gaussian","truncated",":","spreads","how","many","stds","for","gaussian","add_exclusion_map",":","add","exclusion","map\/signal"]
code: def _apply_gaussian(self, t):
      if not self.gaussian or torch.count_nonzero(t) == 0:
          return t
      x = GaussianFilter(
          spatial_dims=2, truncated=self.truncated, sigma=self.sigma
      )(t.unsqueeze(0).unsqueeze(0))
      return x.squeeze(0).squeeze(0)
  
imports: from __future__ import annotations
  import math
  from typing import Any
  import numpy
  import torch
  from monai.config import KeysCollection, NdarrayOrTensor
  from monai.networks.layers import GaussianFilter
  from monai.transforms import MapTransform, Randomizable, SpatialPad
  from monai.utils import StrEnum, convert_to_numpy, optional_import
label_desc: Use image node_id 5 for calling the AddClickSignalsd obj's underlying member method code with example usage: obj._apply_gaussian(t) and returns: x, t