docs: def multi_component_layout(
      data,
      graph,
      n_components,
      component_labels,
      dim,
      random_state,
      metric="euclidean",
      metric_kwds={},
  ):
      """Specialised layout algorithm for dealing with graphs with many connected components.
      This will first fid relative positions for the components by spectrally embedding
      their centroids, then spectrally embed each individual connected component positioning
      them according to the centroid embeddings. This provides a decent embedding of each
      component while placing the components in good relative positions to one another.
  
      Parameters
      ----------
      data: array of shape (n_samples, n_features)
          The source data -- required so we can generate centroids for each
          connected component of the graph.
  
      graph: sparse matrix
          The adjacency matrix of the graph to be emebdded.
  
      n_components: int
          The number of distinct components to be layed out.
  
      component_labels: array of shape (n_samples)
          For each vertex in the graph the label of the component to
          which the vertex belongs.
  
      dim: int
          The chosen embedding dimension.
  
      metric: string or callable (optional, default 'euclidean')
          The metric used to measure distances among the source data points.
  
      metric_kwds: dict (optional, default {})
          Keyword arguments to be passed to the metric function.
  
  
      Returns
      -------
      embedding: array of shape (n_samples, dim)
          The initial embedding of ``graph``.
      """
  
      result = np.empty((graph.shape[0], dim), dtype=np.float32)
  
      if n_components > 2 * dim:
          meta_embedding = component_layout(
              data,
              n_components,
              component_labels,
              dim,
              metric=metric,
              metric_kwds=metric_kwds,
          )
      else:
          k = int(np.ceil(n_components / 2.0))
          base = np.hstack([np.eye(k), np.zeros((k, dim - k))])
          meta_embedding = np.vstack([base, -base])[:n_components]
  
      for label in range(n_components):
          component_graph = graph.tocsr()[component_labels == label, :].tocsc()
          component_graph = component_graph[:, component_labels == label].tocoo()
  
          distances = pairwise_distances([meta_embedding[label]], meta_embedding)
          data_range = distances[distances > 0.0].min() / 2.0
  
          if component_graph.shape[0] < 2 * dim:
              result[component_labels == label] = (
                  random_state.uniform(
                      low=-data_range,
                      high=data_range,
                      size=(component_graph.shape[0], dim),
                  )
                  + meta_embedding[label]
              )
              continue
  
          diag_data = np.asarray(component_graph.sum(axis=0))
          # standard Laplacian
          # D = scipy.sparse.spdiags(diag_data, 0, graph.shape[0], graph.shape[0])
          # L = D - graph
          # Normalized Laplacian
          I = scipy.sparse.identity(component_graph.shape[0], dtype=np.float64)
          D = scipy.sparse.spdiags(
              1.0 / np.sqrt(diag_data),
              0,
              component_graph.shape[0],
              component_graph.shape[0],
          )
          L = I - D * component_graph * D
  
          k = dim + 1
          num_lanczos_vectors = max(2 * k + 1, int(np.sqrt(component_graph.shape[0])))
          try:
              eigenvalues, eigenvectors = scipy.sparse.linalg.eigsh(
                  L,
                  k,
                  which="SM",
                  ncv=num_lanczos_vectors,
                  tol=1e-4,
                  v0=np.ones(L.shape[0]),
                  maxiter=graph.shape[0] * 5,
              )
              order = np.argsort(eigenvalues)[1:k]
              component_embedding = eigenvectors[:, order]
              expansion = data_range / np.max(np.abs(component_embedding))
              component_embedding *= expansion
              result[component_labels == label] = (
                  component_embedding + meta_embedding[label]
              )
          except scipy.sparse.linalg.ArpackError:
              warn(
                  "WARNING: spectral initialisation failed! The eigenvector solver\n"
                  "failed. This is likely due to too small an eigengap. Consider\n"
                  "adding some noise or jitter to your data.\n\n"
                  "Falling back to random initialisation!"
              )
              result[component_labels == label] = (
                  random_state.uniform(
                      low=-data_range,
                      high=data_range,
                      size=(component_graph.shape[0], dim),
                  )
                  + meta_embedding[label]
              )
  
      return result