class_docstr: LIT model API implementation for PCA.
code: def predict_minibatch(self, inputs, **unused_kw):
      if not self._fitted:
          return ({"z": [0, 0, 0]} for _ in inputs)
      x = np.stack([i["x"] for i in inputs])
      x = x - self._mean
      zs = np.dot(x, self._evecs)
      return ({"z": utils.coerce_real(z)} for z in zs)
  
file_path: lit/lit_nlp/components/pca.py
imports: from absl import logging
  from lit_nlp.api import model
  from lit_nlp.lib import utils
  import numpy
label_desc: Use image node_id 3 for calling the PCAModel obj's underlying member method code with example usage: obj.predict_minibatch(inputs) and returns: unknown, unknown