code: def hybrid_clip(
      F: ModuleType,
      x: nd_sym_type,
      clip_lower: nd_sym_type,
      clip_upper: nd_sym_type,
  ) -> nd_sym_type:
      """
      Apply clipping to input x between clip_lower and clip_upper.
      Added because F.clip doesn't support clipping bounds that are mx.nd.NDArray or mx.sym.Symbol.
  
      :param F: backend api, either `mxnet.nd` or `mxnet.sym` (if block has been hybridized).
      :param x: input data
      :param clip_lower: lower bound used for clipping, should be of shape (1,)
      :param clip_upper: upper bound used for clipping, should be of shape (1,)
      :return: clipped data
      """
      x_clip_lower = broadcast_like(F, clip_lower, x)
      x_clip_upper = broadcast_like(F, clip_upper, x)
      x_clipped = F.minimum(F.maximum(x, x_clip_lower), x_clip_upper)
      return x_clipped
  
imports: import inspect
  from typing import Any, Dict, Generator, Iterable, List, Tuple, Union
  from types import ModuleType
  import mxnet
  from mxnet import gluon, nd
  from mxnet.ndarray import NDArray
  import numpy
  from rl_coach.core_types import GradientClippingMethod
label_desc: Use image node_id 12 for calling a global function with example usage: hybrid_clip(F, x, clip_lower, clip_upper) and returns: x_clipped