original_docstring: 
          Computes and saves a set of nearest neighbors in this embedding according
          to the Field.POSITION values. This can be accessed after completing this
          step through the neighbors property. If the metric or n_neighbors
          is not provided, the default metric and n_neighobrs for this Embedding
          object is used.
          
          If this Embedding is copied or projected, it will inherit the same
          Neighbors.
          
docstring: Computes and saves a set of nearest neighbors in this embedding according
  to the Field.POSITION values. This can be accessed after completing this
  step through the neighbors property. If the metric or n_neighbors
  is not provided, the default metric and n_neighobrs for this Embedding
  object is used.
  
  If this Embedding is copied or projected, it will inherit the same
  Neighbors.
docstring_tokens: Computes and saves a set of nearest neighbors in this embedding according to the Field . POSITION values . This can be accessed after completing this step through the neighbors property . If the metric or n_neighbors is not provided the default metric and n_neighobrs for this Embedding object is used . If this Embedding is copied or projected it will inherit the same Neighbors .
code: def compute_neighbors(self, n_neighbors=None, metric=None):
          """
          Computes and saves a set of nearest neighbors in this embedding according
          to the Field.POSITION values. This can be accessed after completing this
          step through the neighbors property. If the metric or n_neighbors
          is not provided, the default metric and n_neighobrs for this Embedding
          object is used.
          
          If this Embedding is copied or projected, it will inherit the same
          Neighbors.
          """
          pos = self.field(Field.POSITION)
          # Save the metric and n_neighbors here so that they can be used to
          # re-generate the Neighbors later if needed
          self.metric = metric or self.metric
          self.n_neighbors = n_neighbors or self.n_neighbors
          self.neighbors = Neighbors.compute(pos,
                                               ids=self.ids,
                                               metric=metric or self.metric,
                                               n_neighbors=self.n_neighbors)
code_tokens: def compute_neighbors ( self , n_neighbors = None , metric = None ) : pos = self . field ( Field . POSITION ) self . metric = metric or self . metric self . n_neighbors = n_neighbors or self . n_neighbors self . neighbors = Neighbors . compute ( pos , ids = self . ids , metric = metric or self . metric , n_neighbors = self . n_neighbors )
short_docstring: Computes and saves a set of nearest neighbors in this embedding according
  to the Field.POSITION values.
short_docstring_tokens: Computes and saves a set of nearest neighbors in this embedding according to the Field . POSITION values .
comment: """
          Computes and saves a set of nearest neighbors in this embedding according
          to the Field.POSITION values. This can be accessed after completing this
          step through the neighbors property. If the metric or n_neighbors
          is not provided, the default metric and n_neighobrs for this Embedding
          object is used.
          
          If this Embedding is copied or projected, it will inherit the same
          Neighbors.
          """$SEP$# Save the metric and n_neighbors here so that they can be used to$SEP$# re-generate the Neighbors later if needed