class_docstr: Model Gaussian distribution over a set of points.
class_docstr_tok: ["Model","Gaussian","distribution","over","a","set","of","points","."]
code: def __init__(self):
      super().__init__()
      self.register_buffer("mean_vec", Tensor())
      self.register_buffer("u_mat", Tensor())
      self.register_buffer("sigma_mat", Tensor())
  
      self.mean_vec: Tensor
      self.u_mat: Tensor
      self.sigma_mat: Tensor
  
file_path: anomalib/src/anomalib/models/dfm/torch_model.py
imports: from __future__ import annotations
  import math
  import torch
  import torch.nn.functional
  from torch import Tensor, nn
  from anomalib.models.components import PCA, DynamicBufferModule, FeatureExtractor
label_desc: Use image node_id 1 to create a new SingleClassGaussian object from inherited base classes: DynamicBufferModule with example: obj = SingleClassGaussian()