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: Append the measurement process to an annotated queue, making sure
  the observable is not queued
docstr_tok: ["Append","the","measurement","process","to","an","annotated","queue",",","making","sure","the","observable","is","not","queued"]
code: def queue(self, context=qml.QueuingManager):
      """Append the measurement process to an annotated queue, making sure
      the observable is not queued"""
      Hs = self.H if isinstance(self.H, Iterable) else [self.H]
      for H in Hs:
          context.remove(H)
      context.append(self)
  
      return self
  
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 11 for calling the ShadowExpvalMP obj's underlying member method code with example usage: obj.queue(context) and returns: self