code: def get_activation_deriv(act: nn.HybridBlock) -> Callable:
      """
  
      Parameters
      ----------
      act
          Activation object
      Returns
      -------
      Callable
          Derivative function of associated activation
  
      """
      if isinstance(act, nn.Activation):
          activation = act._act_type
          if activation == "relu":
              raise NotImplementedError(activation)
          if activation == "sigmoid":
              raise NotImplementedError(activation)
          if activation == "tanh":
              return deriv_tanh
          if activation == "softrelu":
              return deriv_softrelu
          if activation == "softsign":
              raise NotImplementedError(activation)
      if isinstance(act, nn.ELU):
          return partial(deriv_elu, alpha=act._alpha)
      if isinstance(act, nn.Swish):
          return partial(deriv_swish, beta=act._beta)
      if isinstance(act, LipSwish):
          return partial(
              deriv_lipswish, beta=act.params.get("beta").data()
          )
      raise NotImplementedError(
          f'No derivative function for activation "{act.__class__.__name__}"'
      )
  
file_path: gluonts/src/gluonts/mx/activation.py
imports: from functools import partial
  from typing import Callable
  import mxnet
  import mxnet.gluon.nn
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
label_desc: Use image node_id 2 for calling a global function with example usage: get_activation_deriv(act) and returns: partial, partial, partial, deriv_tanh, deriv_softrelu