class_docstr: Measures the expectation value of an operator using the classical shadow measurement process.
  
  Please refer to :func:`shadow_expval` for detailed documentation.
  
  Args:
      H (Operator, Sequence[Operator]): Operator or list of Operators to compute the expectation value over.
      seed (Union[int, None]): The seed used to generate the random measurements
      k (int): Number of equal parts to split the shadow's measurements to compute the median of means.
          ``k=1`` corresponds to simply taking the mean over all measurements.
      id (str): custom label given to a measurement instance, can be useful for some applications
          where the instance has to be identified
docstr: The wires the measurement process acts on.
  
  This is the union of all the Wires objects of the measurement.
docstr_tok: ["The","wires","the","measurement","process","acts","on",".","This","is","the","union","of","all","the","Wires","objects","of","the","measurement","."]
code: def wires(self):
      r"""The wires the measurement process acts on.
  
      This is the union of all the Wires objects of the measurement.
      """
      if isinstance(self.H, Iterable):
          return Wires.all_wires([h.wires for h in self.H])
  
      return self.H.wires
  
imports: import copy
  from collections.abc import Iterable
  from typing import Optional, Union, Sequence
  from string import ascii_letters
  import numpy
  import pennylane
  from pennylane.operation import Operator
  from pennylane.wires import Wires
  from .measurements import MeasurementShapeError, MeasurementTransform, Shadow, ShadowExpval
label_desc: Use image node_id 10 for calling the ShadowExpvalMP obj's underlying member method code with example usage: obj.wires() and returns: self, Wires