text: import scanpy as sc
  # function copied from scanpy. default parameters changed
  
  def neighbors(adata,
      n_neighbors = 15,
      n_pcs = None,
      use_rep = None,
      knn = True,
      random_state = 0,
      method = 'gauss',
      metric = 'euclidean',
      metric_kwds = {},
      copy = False):
  
      """\
      Compute a neighborhood graph of observations [McInnes18]_.
  
      The neighbor search efficiency of this heavily relies on UMAP [McInnes18]_,
      which also provides a method for estimating connectivities of data points -
      the connectivity of the manifold (`method=='umap'`). If `method=='gauss'`,
      connectivities are computed according to [Coifman05]_, in the adaption of
      [Haghverdi16]_.
  
      Parameters
      ----------
      adata
          Annotated data matrix.
      n_neighbors
          The size of local neighborhood (in terms of number of neighboring data
          points) used for manifold approximation. Larger values result in more
          global views of the manifold, while smaller values result in more local
          data being preserved. In general values should be in the range 2 to 100.
          If `knn` is `True`, number of nearest neighbors to be searched. If `knn`
          is `False`, a Gaussian kernel width is set to the distance of the
          `n_neighbors` neighbor.
      {n_pcs}
      {use_rep}
      knn
          If `True`, use a hard threshold to restrict the number of neighbors to
          `n_neighbors`, that is, consider a knn graph. Otherwise, use a Gaussian
          Kernel to assign low weights to neighbors more distant than the
          `n_neighbors` nearest neighbor.
      random_state
          A numpy random seed.
      method : {{`'umap'`, `'gauss'`, `'rapids'`, `None`}}  (default: `'gauss'`)
          Use 'umap' [McInnes18]_ or 'gauss' (Gauss kernel following [Coifman05]_
          with adaptive width [Haghverdi16]_) for computing connectivities.
          Use 'rapids' for the RAPIDS implementation of UMAP (experimental, GPU
          only).
      metric
          A known metric’s name or a callable that returns a distance.
      metric_kwds
          Options for the metric.
      copy
          Return a copy instead of writing to adata.
  
      Returns
      -------
      Depending on `copy`, updates or returns `adata` with the following:
  
      **connectivities** : sparse matrix (`.uns['neighbors']`, dtype `float32`)
          Weighted adjacency matrix of the neighborhood graph of data
          points. Weights should be interpreted as connectivities.
      **distances** : sparse matrix (`.uns['neighbors']`, dtype `float32`)
          Instead of decaying weights, this stores distances for each pair of
          neighbors.
      """
      if copy:
          return(sc.pp.neighbors(adata, n_neighbors, n_pcs, use_rep, knn, random_state, 
              method, metric, metric_kwds, copy))
      else:
          sc.pp.neighbors(adata, n_neighbors, n_pcs, use_rep, knn, random_state, 
              method, metric, metric_kwds, copy)
  
      