code: def apply_operation(
      op: qml.operation.Operator,
      state,
      is_state_batched: bool = False,
      debugger=None,
  ):
      """Apply and operator to a given state.
  
      Args:
          op (Operator): The operation to apply to ``state``
          state (TensorLike): The starting state.
          is_state_batched (bool): Boolean representing whether the state is batched or not
          debugger (_Debugger): The debugger to use
  
      Returns:
          ndarray: output state
  
      .. warning::
  
          ``apply_operation`` is an internal function, and thus subject to change without a deprecation cycle.
  
      .. warning::
          ``apply_operation`` applies no validation to its inputs.
  
          This function assumes that the wires of the operator correspond to indices
          of the state. See :func:`~.map_wires` to convert operations to integer wire labels.
  
          The shape of state should be ``[2]*num_wires``.
  
      This is a ``functools.singledispatch`` function, so additional specialized kernels
      for specific operations can be registered like:
  
      .. code-block:: python
  
          @apply_operation.register
          def _(op: type_op, state):
              # custom op application method here
  
      **Example:**
  
      >>> state = np.zeros((2,2))
      >>> state[0][0] = 1
      >>> state
      tensor([[1., 0.],
          [0., 0.]], requires_grad=True)
      >>> apply_operation(qml.PauliX(0), state)
      tensor([[0., 0.],
          [1., 0.]], requires_grad=True)
  
      """
      return _apply_operation_default(
          op, state, is_state_batched, debugger
      )
  
imports: from functools import singledispatch
  from string import ascii_letters
  import numpy
  import pennylane
  from pennylane import math
label_desc: Use image node_id 4 for calling a global function with example usage: apply_operation(op, state, is_state_batched, debugger) and returns: _apply_operation_default