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.
code: def __init__(
      self, image_size: ListConfig | tuple, sigma: int = 4
  ) -> None:
      super().__init__()
      self.image_size = (
          image_size
          if isinstance(image_size, tuple)
          else tuple(image_size)
      )
      kernel_size = 2 * int(4.0 * sigma + 0.5) + 1
      self.blur = GaussianBlur2d(
          kernel_size=(kernel_size, kernel_size),
          sigma=(sigma, sigma),
          channels=1,
      )
  
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 1 to create a new AnomalyMapGenerator object from inherited base classes: nn with example: obj = AnomalyMapGenerator(image_size, sigma)