code: def clip_gradient(self, gradient, threshold):
      # We do not use CuPy's linalg.norm, since it uses scalar reductions
      # using one CUDA block. This is a lot slower than the cuBLAS
      # implementation.
      def frobenius_norm(X):
          X_vec = X.reshape(-1)
          return cupy.cublas.nrm2(X_vec)
  
      grad_norm = cupy.maximum(frobenius_norm(gradient), 1e-12)
      gradient *= cupy.minimum(threshold, grad_norm) / grad_norm
      return gradient
  
imports: import numpy
  from ..None import registry
  from ..compat import cupy, cupyx
  from ..types import DeviceTypes
  from ..util import is_cupy_array, is_mxnet_gpu_array, is_tensorflow_gpu_array, is_torch_cuda_array, mxnet2xp, tensorflow2xp, torch2xp
  from .None import _custom_kernels
  from .numpy_ops import NumpyOps
  from .ops import Ops
label_desc: Use image node_id 23 for calling the CupyOps obj's underlying member method code with example usage: obj.clip_gradient(gradient, threshold) and returns: gradient, cupy