code: def convert_activation(net, node, module, builder):
      
      input_name, output_name = _get_input_output_name(net, node)
      name = node['name']
      mx_non_linearity = _get_attrs(node)['act_type']
      #TODO add SCALED_TANH, SOFTPLUS, SOFTSIGN, SIGMOID_HARD, LEAKYRELU, PRELU, ELU, PARAMETRICSOFTPLUS, THRESHOLDEDRELU, LINEAR
      if mx_non_linearity == 'relu':
          non_linearity = 'RELU'
      elif mx_non_linearity == 'tanh':
          non_linearity = 'TANH'
      elif mx_non_linearity == 'sigmoid':
          non_linearity = 'SIGMOID'
      else:
          raise TypeError('Unknown activation type %s' % mx_non_linearity)
      builder.add_activation(name = name,
                             non_linearity = non_linearity,
                             input_name = input_name,
                             output_name = output_name)
docs: Convert an activation layer from mxnet to coreml.
  
      Parameters
      ----------
      network: net
          A mxnet network object.
  
      layer: node
          Node to convert.
  
      module: module
          An module for MXNet
  
      builder: NeuralNetworkBuilder
          A neural network builder object.