class_docstr: Abstract base class for PennyLane qubit devices.
  
  The following abstract method **must** be defined:
  
  * :meth:`~.apply`: append circuit operations, compile the circuit (if applicable),
    and perform the quantum computation.
  
  Devices that generate their own samples (such as hardware) may optionally
  overwrite :meth:`~.probability`. This method otherwise automatically
  computes the probabilities from the generated samples, and **must**
  overwrite the following method:
  
  * :meth:`~.generate_samples`: Generate samples from the device from the
    exact or approximate probability distribution.
  
  Analytic devices **must** overwrite the following method:
  
  * :meth:`~.analytic_probability`: returns the probability or marginal probability from the
    device after circuit execution. :meth:`~.marginal_prob` may be used here.
  
  This device contains common utility methods for qubit-based devices. These
  do not need to be overwritten. Utility methods include:
  
  * :meth:`~.expval`, :meth:`~.var`, :meth:`~.sample`: return expectation values,
    variances, and samples of observables after the circuit has been rotated
    into the observable eigenbasis.
  
  Args:
      wires (int, 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 (None, int, list[int]): Number of circuit evaluations/random samples used to estimate
          expectation values of observables. If ``None``, the device calculates probability, expectation values,
          and variances analytically. If an integer, it specifies the number of samples to estimate these quantities.
          If a list of integers is passed, the circuit evaluations are batched over the list of shots.
      r_dtype: Real floating point precision type.
      c_dtype: Complex floating point precision type.
docstr: Generates basis states in binary representation according to the number
  of wires specified.
  
  The states_to_binary method creates basis states faster (for larger
  systems at times over x25 times faster) than the approach using
  ``itertools.product``, at the expense of using slightly more memory.
  
  Due to the large size of the integer arrays for more than 32 bits,
  memory allocation errors may arise in the states_to_binary method.
  Hence we constraint the dtype of the array to represent unsigned
  integers on 32 bits. Due to this constraint, an overflow occurs for 32
  or more wires, therefore this approach is used only for fewer wires.
  
  For smaller number of wires speed is comparable to the next approach
  (using ``itertools.product``), hence we resort to that one for testing
  purposes.
  
  Args:
      num_wires (int): the number wires
      dtype=np.uint32 (type): the data type of the arrays to use
  
  Returns:
      array[int]: the sampled basis states
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code: def generate_basis_states(num_wires, dtype=np.uint32):
      """
      Generates basis states in binary representation according to the number
      of wires specified.
  
      The states_to_binary method creates basis states faster (for larger
      systems at times over x25 times faster) than the approach using
      ``itertools.product``, at the expense of using slightly more memory.
  
      Due to the large size of the integer arrays for more than 32 bits,
      memory allocation errors may arise in the states_to_binary method.
      Hence we constraint the dtype of the array to represent unsigned
      integers on 32 bits. Due to this constraint, an overflow occurs for 32
      or more wires, therefore this approach is used only for fewer wires.
  
      For smaller number of wires speed is comparable to the next approach
      (using ``itertools.product``), hence we resort to that one for testing
      purposes.
  
      Args:
          num_wires (int): the number wires
          dtype=np.uint32 (type): the data type of the arrays to use
  
      Returns:
          array[int]: the sampled basis states
      """
      if 2 < num_wires < 32:
          states_base_ten = np.arange(2**num_wires, dtype=dtype)
          return QubitDevice.states_to_binary(
              states_base_ten, num_wires, dtype=dtype
          )
  
      # A slower, but less memory intensive method
      basis_states_generator = itertools.product(
          (0, 1), repeat=num_wires
      )
      return np.fromiter(
          itertools.chain(*basis_states_generator), dtype=int
      ).reshape(-1, num_wires)
  
imports: import abc
  import itertools
  import warnings
  from collections import defaultdict
  from typing import Union, List
  import inspect
  import logging
  import numpy
  import pennylane
  from pennylane import Device, DeviceError
  from pennylane.workflow import set_shots
  from pennylane.math import multiply
  from pennylane.math import sum
  from pennylane.measurements import ClassicalShadowMP, CountsMP, ExpectationMP, MeasurementProcess, MeasurementTransform, MeasurementValue, MutualInfoMP, ProbabilityMP, SampleMeasurement, SampleMP, ShadowExpvalMP, StateMeasurement, StateMP, VarianceMP, VnEntropyMP, Shots
  from pennylane.resource import Resources
  from pennylane.operation import operation_derivative, Operation
  from pennylane.tape import QuantumTape
  from pennylane.wires import Wires
label_desc: Use image node_id 17 for calling the QubitDevice obj's underlying member method code with example usage: obj.generate_basis_states(num_wires, dtype) and returns: np, QubitDevice