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: Inverses the transformed features.
  
  Args:
    features (Tensor): Transformed features
  
  Returns: Inverse features
code: def inverse_transform(self, features: Tensor) -> Tensor:
      """Inverses the transformed features.
  
      Args:
        features (Tensor): Transformed features
  
      Returns: Inverse features
      """
      inv_features = torch.matmul(
          features, self.singular_vectors.transpose(-2, -1)
      )
      return inv_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 5 for calling the PCA obj's underlying member method code with example usage: obj.inverse_transform(features) and returns: inv_features