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: Up sample anomaly score to match the input image size.
  
  Args:
      distance (Tensor): Anomaly score computed via the mahalanobis distance.
  
  Returns:
      Resized distance matrix matching the input image size
docstr_tok: ["Up","sample","anomaly","score","to","match","the","input","image","size",".","Args",":","distance","(","Tensor",")",":","Anomaly","score","computed","via","the","mahalanobis","distance",".","Returns",":","Resized","distance","matrix","matching","the","input","image","size"]
code: def up_sample(self, distance: Tensor) -> Tensor:
      """Up sample anomaly score to match the input image size.
  
      Args:
          distance (Tensor): Anomaly score computed via the mahalanobis distance.
  
      Returns:
          Resized distance matrix matching the input image size
      """
  
      score_map = F.interpolate(
          distance,
          size=self.image_size,
          mode="bilinear",
          align_corners=False,
      )
      return score_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 3 for calling the AnomalyMapGenerator obj's underlying member method code with example usage: obj.up_sample(distance) and returns: score_map