code: def _apply_grover_without_matrix(state, op_wires, is_state_batched):
      r"""Apply GroverOperator to state. This method uses that this operator
      is :math:`2*P-\mathbb{I}`, where :math:`P` is the projector onto the
      all-plus state. This allows us to compute the new state by replacing summing
      over all axes on which the operation acts, and "filling in" the all-plus state
      in the resulting lower-dimensional state via a Kronecker product.
      """
      num_wires = len(op_wires)
      # 2 * Squared normalization of the all-plus state on the op wires
      # (squared, because we skipped the normalization when summing, 2* because of the def of Grover)
      prefactor = 2 ** (1 - num_wires)
  
      # The axes to sum over in order to obtain <+|\psi>, where <+| only acts on the op wires.
      sum_axes = [w + is_state_batched for w in op_wires]
      collapsed = math.sum(state, axis=tuple(sum_axes))
  
      if num_wires == (len(qml.math.shape(state)) - is_state_batched):
          # If the operation acts on all wires, we can skip the tensor product with all-ones state
          new_shape = (
              (-1,) + (1,) * num_wires
              if is_state_batched
              else (1,) * num_wires
          )
          return prefactor * math.reshape(collapsed, new_shape) - state
          # [todo]: Once Tensorflow support expand_dims with multiple axes in the second argument,
          # use the following line instead of the two above.
          # return prefactor * math.expand_dims(collapsed, sum_axes) - state
  
      all_plus = math.cast_like(
          math.full([2] * num_wires, prefactor), state
      )
      # After the Kronecker product (realized with tensordot with axes=0), we need to move
      # the new axes to the summed-away axes' positions. Finally, subtract the original state.
      source = list(range(math.ndim(collapsed), math.ndim(state)))
      # Probably it will be better to use math.full or math.tile to create the outer product
      # here computed with math.tensordot. However, Tensorflow and Torch do not have full support
      return (
          math.moveaxis(
              math.tensordot(collapsed, all_plus, axes=0),
              source,
              sum_axes,
          )
          - state
      )
  
imports: from functools import singledispatch
  from string import ascii_letters
  import numpy
  import pennylane
  from pennylane import math
label_desc: Use image node_id 13 for calling a global function with example usage: _apply_grover_without_matrix(state, op_wires, is_state_batched) and returns: unknown, unknown