code: def run_tsne(
      data, n_components, apply_tsvd=True, compute_backend=None
  ):
      """Reduces the number of dimensions of the input data using t-SNE.
  
      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:
          reduc_df: The dimensionally-reduced dataframe
          tsne: The applied t-SNE object
      """
      return _get_compute_backend(
          compute_backend, data
      ).compute_run_tsne(data, n_components, apply_tsvd)
  
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 4 for calling a global function with example usage: run_tsne(data, n_components, apply_tsvd, compute_backend) and returns: _get_compute_backend