code: def state_diagonalizing_gates(
      measurementprocess: StateMeasurement,
      state: TensorLike,
      is_state_batched: bool = False,
  ) -> TensorLike:
      """Apply a measurement to state when the measurement process has an observable with diagonalizing gates.
  
      Args:
          measurementprocess (StateMeasurement): measurement to apply to the state
          state (TensorLike): state to apply the measurement to
          is_state_batched (bool): whether the state is batched or not
  
      Returns:
          TensorLike: the result of the measurement
      """
      for op in measurementprocess.diagonalizing_gates():
          state = apply_operation(
              op, state, is_state_batched=is_state_batched
          )
  
      total_indices = len(state.shape) - is_state_batched
      wires = Wires(range(total_indices))
      flattened_state = flatten_state(state, total_indices)
      return measurementprocess.process_state(flattened_state, wires)
  
file_path: pennylane/pennylane/devices/qubit/measure.py
imports: from typing import Callable
  from scipy.sparse import csr_matrix
  from pennylane import math
  from pennylane.ops import Sum, Hamiltonian
  from pennylane.measurements import StateMeasurement, MeasurementProcess, MeasurementValue, ExpectationMP
  from pennylane.pauli.conversion import is_pauli_sentence, pauli_sentence
  from pennylane.typing import TensorLike
  from pennylane.wires import Wires
  from .apply_operation import apply_operation
label_desc: Use image node_id 2 for calling a global function with example usage: state_diagonalizing_gates(measurementprocess, state, is_state_batched) and returns: measurementprocess