code: def predict(self, X, queue):
      policy = self._get_policy(queue, X)
      model = self._create_model()
  
      X = _convert_to_supported(policy, X)
      params = self.get_onedal_params(X)
      result = _backend.decomposition.dim_reduction.infer(
          policy, params, model, to_table(X)
      )
      return from_table(result.transformed_data)
  
file_path: scikit-learn-intelex/onedal/decomposition/pca.py
imports: import numpy
  from daal4py.sklearn._utils import daal_check_version, sklearn_check_version
  from onedal import _backend
  from ..common._policy import _get_policy
  from ..common.hyperparameters import get_hyperparameters
  from ..datatypes import _convert_to_supported, from_table, to_table
label_desc: Use image node_id 6 for calling the PCA obj's underlying member method code with example usage: obj.predict(X, queue) and returns: from_table