code: def _generator_prefactor(gen):
      r"""Return the generator as ```(obs, prefactor)`` representing
      :math:`G=p \hat{O}`, where
  
      - prefactor :math:`p` is a float
      - observable `\hat{O}` is one of :class:`~.Hermitian`,
        :class:`~.SparseHamiltonian`, or a tensor product
        of Pauli words.
      """
  
      prefactor = 1.0
  
      if isinstance(gen, Prod):
          gen = qml.simplify(gen)
  
      if isinstance(gen, Hamiltonian):
          gen = qml.dot(gen.coeffs, gen.ops)  # convert to Sum
  
      if isinstance(gen, Sum):
          ops = [o.base if isinstance(o, SProd) else o for o in gen]
          coeffs = [
              o.scalar if isinstance(o, SProd) else 1 for o in gen
          ]
          abs_coeffs = list(qml.math.abs(coeffs))
          if qml.math.allequal(coeffs[0], coeffs):
              # case where the Hamiltonian coefficients are all the same
              return qml.sum(*ops), coeffs[0]
          if qml.math.allequal(abs_coeffs[0], abs_coeffs):
              # absolute value of coefficients is the same
              prefactor = abs_coeffs[0]
              coeffs = [c / prefactor for c in coeffs]
              return qml.dot(coeffs, ops), prefactor
  
      elif isinstance(gen, SProd):
          return gen.base, gen.scalar
  
      return gen, prefactor
  
file_path: pennylane/pennylane/ops/functions/generator.py
imports: import inspect
  import warnings
  import numpy
  import pennylane
  from pennylane.ops import Hamiltonian, SProd, Prod, Sum
label_desc: Use image node_id 2 for calling a global function with example usage: _generator_prefactor(gen) and returns: gen,  prefactor, qml,  coeffs, qml,  prefactor, gen,  gen