code: def run_pca(data, n_components, compute_backend=None):
      """Reduces the number of dimensions of the input data using PCA.
  
      Args:
          data: The dataframe
          n_components: Desired dimensionality for the data set prior to modeling
  
      Returns:
          reduc_df: The dimensionally-reduced dataframe
          pca: The applied PCA object
      """
      fname = [
          "component_{}".format(i) for i in range(1, n_components + 1)
      ]
      return _get_compute_backend(
          compute_backend, data
      ).compute_run_pca(data, n_components, column_names=fname)
  
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 2 for calling a global function with example usage: run_pca(data, n_components, compute_backend) and returns: _get_compute_backend