code: def _pandas_compute_run_tsne(data, n_components, apply_tsvd):
      """Performs dimensionality reduction using t-SNE on the provided dataset.
  
      Args:
          data: The dataframe
          n_components: Desired dimensionality for the output dataset
          apply_tsvd: If True, TSVD will be run before t-SNE. This is highly recommended when running t-SNE
  
      Returns:
          The dimensionally-reduced Pandas dataframe
          tsne: The applied t-SNE object
      """
      if apply_tsvd:
          fname = [
              "component_{}".format(i)
              for i in range(1, n_components + 1)
          ]
          data = _pandas_compute_run_tsvd(data, n_components, fname)[0]
      tsne = TSNE(n_components, random_state=0)
      reduc = tsne.fit_transform(data)
      return pd.DataFrame(reduc, columns=["ts1", "ts2"]), tsne
  
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 8 for calling a global function with example usage: _pandas_compute_run_tsne(data, n_components, apply_tsvd) and returns: pd,  tsne