code: def prod(*ops, id=None, lazy=True):
      """Construct an operator which represents the generalized product of the
      operators provided.
  
      The generalized product operation represents both the tensor product as
      well as matrix composition. This can be resolved naturally from the wires
      that the given operators act on.
  
      Args:
          *ops (Union[tuple[~.operation.Operator], Callable]): The operators we would like to multiply.
              Alternatively, a single qfunc that queues operators can be passed to this function.
  
      Keyword Args:
          id (str or None): id for the product operator. Default is None.
          lazy=True (bool): If ``lazy=False``, a simplification will be performed such that when any of the operators is already a product operator, its operands will be used instead.
  
      Returns:
          ~ops.op_math.Prod: the operator representing the product.
  
      .. note::
  
          This operator supports batched operands:
  
          >>> op = qml.prod(qml.RX(np.array([1, 2, 3]), wires=0), qml.PauliX(1))
          >>> op.matrix().shape
          (3, 4, 4)
  
          But it doesn't support batching of operators:
  
          >>> op = qml.prod(np.array([qml.RX(0.5, 0), qml.RZ(0.3, 0)]), qml.PauliZ(0))
          AttributeError: 'numpy.ndarray' object has no attribute 'wires'
  
      .. seealso:: :class:`~.ops.op_math.Prod`
  
      **Example**
  
      >>> prod_op = prod(qml.PauliX(0), qml.PauliZ(0))
      >>> prod_op
      PauliX(wires=[0]) @ PauliZ(wires=[0])
      >>> prod_op.matrix()
      array([[ 0, -1],
             [ 1,  0]])
  
      You can also create a prod operator by passing a qfunc to prod, like the following:
  
      >>> def qfunc(x):
      ...     qml.RX(x, 0)
      ...     qml.CNOT([0, 1])
      >>> prod_op = prod(qfunc)(1.1)
      >>> prod_op
      CNOT(wires=[0, 1]) @ RX(1.1, wires=[0])
      """
      if len(ops) == 1:
          if isinstance(ops[0], qml.operation.Operator):
              return ops[0]
  
          fn = ops[0]
  
          if not callable(fn):
              raise TypeError(
                  f"Unexpected argument of type {type(fn).__name__} passed to qml.prod"
              )
  
          @wraps(fn)
          def wrapper(*args, **kwargs):
              qs = qml.tape.make_qscript(fn)(*args, **kwargs)
              return prod(*qs.operations[::-1], id=id, lazy=lazy)
  
          return wrapper
  
      if lazy:
          return Prod(*ops, id=id)
  
      ops_simp = Prod(
          *itertools.chain.from_iterable(
              [op if isinstance(op, Prod) else [op] for op in ops]
          ),
          id=id,
      )
  
      for op in ops:
          QueuingManager.remove(op)
  
      return ops_simp
  
imports: import itertools
  from copy import copy
  from functools import reduce, wraps
  from itertools import combinations
  from typing import List, Tuple, Union
  from scipy.sparse import kron
  import pennylane
  from pennylane import math
  from pennylane.operation import Operator
  from pennylane.ops.op_math.pow import Pow
  from pennylane.ops.op_math.sprod import SProd
  from pennylane.ops.op_math.sum import Sum
  from pennylane.ops.qubit import Hamiltonian
  from pennylane.ops.qubit.non_parametric_ops import PauliX, PauliY, PauliZ
  from pennylane.queuing import QueuingManager
  from pennylane.typing import TensorLike
  from pennylane.wires import Wires
  from .composite import CompositeOp
label_desc: Use image node_id 1 for calling a global function with example usage: prod() and returns: ops_simp, wrapper, Prod, ops, prod