code: def run_pca(x, output_dimensionality):
      device = x.device
      x = c_f.to_numpy(x).astype(np.float32)
      mat = faiss.PCAMatrix(x.shape[1], output_dimensionality)
      mat.train(x)
      assert mat.is_trained
      return c_f.to_device(
          torch.from_numpy(mat.apply_py(x)), device=device
      )
  
imports: import numpy
  import torch
  from ..distances import BatchedDistance, CosineSimilarity
  from .None import common_functions
label_desc: Use image node_id 5 for calling a global function with example usage: run_pca(x, output_dimensionality) and returns: c_f