class_docstr: Principle Component Analysis (PCA).
  
  Args:
      n_components (float): Number of components. Can be either integer number of components
        or a ratio between 0-1.
docstr: Transforms the features.
  
  Args:
    features (Tensor): Input features
  
  Returns:
    Transformed features
code: def forward(self, features: Tensor) -> Tensor:
      """Transforms the features.
  
      Args:
        features (Tensor): Input features
  
      Returns:
        Transformed features
      """
      return self.transform(features)
  
file_path: anomalib/src/anomalib/models/components/dimensionality_reduction/pca.py
imports: from __future__ import annotations
  import torch
  from torch import Tensor
  from anomalib.models.components.base import DynamicBufferModule
label_desc: Use image node_id 6 for calling the PCA obj's underlying member method code with example usage: obj.forward(features) and returns: self