class_docstr: Abstract base class for PennyLane devices.
  
  Args:
      wires (int or Iterable[Number, str]]): Number of subsystems represented by the device,
          or iterable that contains unique labels for the subsystems as numbers (i.e., ``[-1, 0, 2]``)
          or strings (``['ancilla', 'q1', 'q2']``). Default 1 if not specified.
      shots (int): Number of circuit evaluations/random samples used to estimate
          expectation values of observables. Defaults to 1000 if not specified.
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docstr: Execute a batch of quantum circuits on the device, and return both the
  results and the gradients.
  
  The circuits are represented by tapes, and they are executed
  one-by-one using the device's ``execute`` method. The results and the
  corresponding Jacobians are collected in a list.
  
  For plugin developers: This method should be overwritten if the device
  can efficiently run multiple circuits on a backend, for example using
  parallel and/or asynchronous executions, and return both the results and the
  Jacobians.
  
  Args:
      circuits (list[.tape.QuantumTape]): circuits to execute on the device
      method (str): the device method to call to compute the Jacobian of a single circuit
      **kwargs: keyword argument to pass when calling ``method``
  
  Returns:
      tuple[list[array[float]], list[array[float]]]: Tuple containing list of measured value(s)
      and list of Jacobians. Returned Jacobians should be of shape ``(output_shape, num_params)``.
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code: def execute_and_gradients(
      self, circuits, method="jacobian", **kwargs
  ):
      """Execute a batch of quantum circuits on the device, and return both the
      results and the gradients.
  
      The circuits are represented by tapes, and they are executed
      one-by-one using the device's ``execute`` method. The results and the
      corresponding Jacobians are collected in a list.
  
      For plugin developers: This method should be overwritten if the device
      can efficiently run multiple circuits on a backend, for example using
      parallel and/or asynchronous executions, and return both the results and the
      Jacobians.
  
      Args:
          circuits (list[.tape.QuantumTape]): circuits to execute on the device
          method (str): the device method to call to compute the Jacobian of a single circuit
          **kwargs: keyword argument to pass when calling ``method``
  
      Returns:
          tuple[list[array[float]], list[array[float]]]: Tuple containing list of measured value(s)
          and list of Jacobians. Returned Jacobians should be of shape ``(output_shape, num_params)``.
      """
      if self.tracker.active:
          self.tracker.update(
              execute_and_derivative_batches=1,
              derivatives=len(circuits),
          )
          self.tracker.record()
      gradient_method = getattr(self, method)
  
      res = []
      jacs = []
  
      for circuit in circuits:
          # Evaluations and gradients are paired, so that
          # devices can re-use the device state for the
          # gradient computation (if applicable).
          res.append(self.batch_execute([circuit])[0])
          jacs.append(gradient_method(circuit, **kwargs))
  
      return res, jacs
  
imports: import abc
  import copy
  import types
  import warnings
  from collections import OrderedDict
  from collections.abc import Iterable, Sequence
  from functools import lru_cache
  import numpy
  import pennylane
  from pennylane.measurements import MeasurementProcess, CountsMP, Expectation, ExpectationMP, MidMeasureMP, Probability, ProbabilityMP, Sample, SampleMP, ShadowExpvalMP, State, Variance
  from pennylane.operation import Observable, Operation, Tensor, Operator, StatePrepBase
  from pennylane.ops import Hamiltonian, Sum
  from pennylane.tape import QuantumScript, QuantumTape, expand_tape_state_prep
  from pennylane.wires import WireError, Wires
  from pennylane.queuing import QueuingManager
label_desc: Use image node_id 25 for calling the Device obj's underlying member method code with example usage: obj.execute_and_gradients(circuits, method) and returns: res,  jacs