code: def _modin_compute_run_ipca(data, n_components, column_names):
      """Performs Incremental PCA on the provided dataset.
  
      Args:
          data: The dataframe
          n_components: Desired dimensionality for the output dataset
          column_names: Names for the columns in the output dataset
  
      Returns:
          The dimensionally-reduced Modin dataframe
          ipca: The applied IncrementalPCA object
      """
      ipca = IncrementalPCA(n_components)
      reduc = ipca.fit_transform(data)
      return (
          _compat["modin.pandas"].DataFrame(
              reduc, columns=column_names
          ),
          ipca,
      )
  
imports: import pandas
  import numpy
  from sklearn.decomposition import PCA, IncrementalPCA
  from sklearn.manifold import TSNE
  from sklearn.decomposition import TruncatedSVD
  from data_describe.compat import _is_dataframe, _compat, _requires
  from data_describe.backends._backends import _get_compute_backend
label_desc: Use image node_id 11 for calling a global function with example usage: _modin_compute_run_ipca(data, n_components, column_names) and returns: _compat,  ipca