class_docstr: Generate Anomaly Heatmap.
  
  Args:
      image_size (ListConfig, tuple): Size of the input image. The anomaly map is upsampled to this dimension.
      sigma (int, optional): Standard deviation for Gaussian Kernel. Defaults to 4.
docstr: Apply gaussian smoothing to the anomaly map.
  
  Args:
      anomaly_map (Tensor): Anomaly score for the test image(s).
  
  Returns:
      Filtered anomaly scores
docstr_tok: ["Apply","gaussian","smoothing","to","the","anomaly","map",".","Args",":","anomaly_map","(","Tensor",")",":","Anomaly","score","for","the","test","image","(","s",")",".","Returns",":","Filtered","anomaly","scores"]
code: def smooth_anomaly_map(self, anomaly_map: Tensor) -> Tensor:
      """Apply gaussian smoothing to the anomaly map.
  
      Args:
          anomaly_map (Tensor): Anomaly score for the test image(s).
  
      Returns:
          Filtered anomaly scores
      """
  
      blurred_anomaly_map = self.blur(anomaly_map)
      return blurred_anomaly_map
  
file_path: anomalib/src/anomalib/models/padim/anomaly_map.py
imports: from __future__ import annotations
  import torch
  import torch.nn.functional
  from omegaconf import ListConfig
  from torch import Tensor, nn
  from anomalib.models.components import GaussianBlur2d
label_desc: Use image node_id 4 for calling the AnomalyMapGenerator obj's underlying member method code with example usage: obj.smooth_anomaly_map(anomaly_map) and returns: blurred_anomaly_map