class_docstr: Immutable dictionary used to represent a Fermi sentence, a linear combination of Fermi words, with the keys
  as FermiWord instances and the values correspond to coefficients.
  
  >>> w1 = FermiWord({(0, 0) : '+', (1, 1) : '-'})
  >>> w2 = FermiWord({(0, 1) : '+', (1, 2) : '-'})
  >>> s = FermiSentence({w1 : 1.2, w2: 3.1})
  >>> s
  1.2 * a⁺(0) a(1)
  + 3.1 * a⁺(1) a(2)
class_docstr_tok: ["Immutable","dictionary","used","to","represent","a","Fermi","sentence",",","a","linear","combination","of","Fermi","words",",","with","the","keys","as","FermiWord","instances","and","the","values","correspond","to","coefficients",".",">",">",">","w1","=","FermiWord","(","{","(","0",",","0",")",":","'+","'",",","(","1",",","1",")",":","'-","'","}",")",">",">",">","w2","=","FermiWord","(","{","(","0",",","1",")",":","'+","'",",","(","1",",","2",")",":","'-","'","}",")",">",">",">","s","=","FermiSentence","(","{","w1",":","1.2",",","w2",":","3.1","}",")",">",">",">","s","1.2","*","a\u207a","(","0",")","a","(","1",")","+","3.1","*","a\u207a","(","1",")","a","(","2",")"]
docstr: Return wires of the FermiSentence.
docstr_tok: ["Return","wires","of","the","FermiSentence","."]
code: def wires(self):
      r"""Return wires of the FermiSentence."""
      return set().union(*(fw.wires for fw in self.keys()))
  
imports: import re
  from copy import copy
  from numbers import Number
  from numpy import ndarray
  import pennylane
label_desc: Use image node_id 2 for calling the FermiSentence obj's underlying member method code with example usage: obj.wires() and returns: set