code: def make_transformer(self, decompose='svd', decompose_by=50, tsne_kwargs={}):
          
  
          # TODO: detect decompose by inferring from sparse matrix or dense or
          # If number of features > 50 etc.
          decompositions = {
              'svd': TruncatedSVD,
              'pca': PCA,
          }
  
          if decompose and decompose.lower() not in decompositions:
              raise YellowbrickValueError(
                  "'{}' is not a valid decomposition, use {}, or None".format(
                      decompose, ", ".join(decompositions.keys())
                  )
              )
  
          # Create the pipeline steps
          steps = []
  
          # Add the pre-decomposition
          if decompose:
              klass = decompositions[decompose]
              steps.append((decompose, klass(
                  n_components=decompose_by, random_state=self.random_state)))
  
          # Add the TSNE manifold
          steps.append(('tsne', TSNE(
              n_components=2, random_state=self.random_state, **tsne_kwargs)))
  
          # return the pipeline
          return Pipeline(steps)
docs: Creates an internal transformer pipeline to project the data set into
          2D space using TSNE, applying an pre-decomposition technique ahead of
          embedding if necessary. This method will reset the transformer on the
          class, and can be used to explore different decompositions.
  
          Parameters
          ----------
  
          decompose : string or None, default: ``'svd'``
              A preliminary decomposition is often used prior to TSNE to make
              the projection faster. Specify ``"svd"`` for sparse data or ``"pca"``
              for dense data. If decompose is None, the original data set will
              be used.
  
          decompose_by : int, default: 50
              Specify the number of components for preliminary decomposition, by
              default this is 50; the more components, the slower TSNE will be.
  
          Returns
          -------
  
          transformer : Pipeline
              Pipelined transformer for TSNE projections