code: def _pandas_compute_run_tsvd(data, n_components, column_names):
      """Performs dimensionality reduction using TSVD 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 Pandas dataframe
          t_svd: The applied TSVD object
      """
      with np.errstate(invalid="ignore"):
          t_svd = TruncatedSVD(n_components, random_state=0)
          reduc = t_svd.fit_transform(data)
      return pd.DataFrame(reduc, columns=column_names), t_svd
  
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 9 for calling a global function with example usage: _pandas_compute_run_tsvd(data, n_components, column_names) and returns: pd,  t_svd