parameters: (self, index)
return_statement: return Blob.Blob(self._internal.get_embeddings(index))
docstring: Gets the representation table with the given index 
          as a blob of the dimensions:
  
              - BatchLength * BatchWidth * ListSize is dimensions[i].VectorCount
              - Height * Width * Depth * Channels is dimensions[i].VectorSize
docstring_summary: Gets the representation table with the given index 
          as a blob of the dimensions:
docstring_tokens: Gets the representation table with the given index as a blob of the dimensions :
function: def get_embeddings(self, index):
          """Gets the representation table with the given index 
          as a blob of the dimensions:
  
              - BatchLength * BatchWidth * ListSize is dimensions[i].VectorCount
              - Height * Width * Depth * Channels is dimensions[i].VectorSize
          """
          return Blob.Blob(self._internal.get_embeddings(index))
function_tokens: def get_embeddings ( self , index ) : return Blob . Blob ( self . _internal . get_embeddings ( index ) )