code: def _darknet_activations(inputs, params, attrs):
      """Process the activation function."""
      act = attrs.get("activation")
      data = (
          inputs[0]
          if isinstance(inputs, _expr.TupleWrapper)
          else inputs
      )
  
      def _const(val):
          return _expr.const(val)
  
      def _relu(data):
          return get_relay_op("relu")(data)
  
      def _exp(data):
          return get_relay_op("exp")(data)
  
      def _tanh(data):
          return get_relay_op("tanh")(data)
  
      def _sigmoid(data):
          return get_relay_op("sigmoid")(data)
  
      def _elu(data):
          alpha = _const(-1.0)
          return alpha * _relu(_const(1.0) - _exp(data)) + _relu(data)
  
      def _leaky_relu(data, slope):
          new_attrs = {}
          new_attrs["alpha"] = slope
          return get_relay_op("leaky_relu")(data, **new_attrs)
  
      if ACTIVATION.LOGISTIC == act:
          data = _sigmoid(data)
      elif ACTIVATION.RELU == act:
          data = _relu(data)
      elif ACTIVATION.TANH == act:
          data = _tanh(data)
      elif ACTIVATION.LINEAR == act:
          return data
      elif ACTIVATION.LEAKY == act:
          data = _leaky_relu(data, attrs.get("slope", 0.1))
      elif ACTIVATION.ELU == act:
          data = _elu(data)
      else:
          _darknet_not_support("act: " + attrs)
      return data
  
imports: from enum import Enum
  import numpy
  import tvm
  from tvm.ir import IRModule
  from ..None import analysis
  from ..None import expr
  from ..None import function
  from .common import get_relay_op, new_var
label_desc: Use image node_id 19 for calling a global function with example usage: _darknet_activations(inputs, params, attrs) and returns: data, _expr, get_relay_op, get_relay_op, get_relay_op, get_relay_op, unknown, get_relay_op, data