class_docstr: Reduce dimensions using PCA.
  
  This nn.Modue subclass reduces the dimensions of the inputs
  specified by `n_components`.
  
  The input is a 2-dimensional `Tensor` of size `batch` x `features`,
  the output is a `Tensor` of size `batch` x `n_components`.
code: def _load_from_state_dict(
      self,
      state_dict,
      prefix,
      local_metadata,
      strict,
      missing_keys,
      unexpected_keys,
      error_msgs,
  ):
      for bname in ("mean", "comp"):
          if prefix + bname in state_dict:
              setattr(self, bname, state_dict[prefix + bname])
      super()._load_from_state_dict(
          state_dict,
          prefix,
          local_metadata,
          strict,
          missing_keys,
          unexpected_keys,
          error_msgs,
      )
  
file_path: TorchDrift/torchdrift/reducers/pca.py
imports: import torch
  from .None import Reducer
  import torchdrift.utils
label_desc: Use image node_id 5 for calling the PCAReducer obj's underlying member method code with example usage: obj._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) without return types