class_docstr: Model Gaussian distribution over a set of points.
class_docstr_tok: ["Model","Gaussian","distribution","over","a","set","of","points","."]
docstr: Provides the same functionality as `fit`.
  
  Transforms the input dataset based on singular values calculated earlier.
  
  Args:
      dataset (Tensor): Input dataset
docstr_tok: ["Provides","the","same","functionality","as","`","fit","`",".","Transforms","the","input","dataset","based","on","singular","values","calculated","earlier",".","Args",":","dataset","(","Tensor",")",":","Input","dataset"]
code: def forward(self, dataset: Tensor) -> None:
      """Provides the same functionality as `fit`.
  
      Transforms the input dataset based on singular values calculated earlier.
  
      Args:
          dataset (Tensor): Input dataset
      """
      self.fit(dataset)
  
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 4 for calling the SingleClassGaussian obj's underlying member method code with example usage: obj.forward(dataset) without return types