code: def convert_instancenorm(net, node, model, builder):
      
      import numpy as _np
      input_name, output_name = _get_input_output_name(net, node)
  
      name = node['name']
      inputs = node['inputs']
      outputs = node['outputs']
  
  
      data_blob_name = _get_node_name(net, inputs[0][0])
      gamma_blob_name = _get_node_name(net, inputs[1][0])
      beta_blob_name = _get_node_name(net, inputs[2][0])
      channels = _get_node_channels(net, inputs[0][0])
  
      bn_output_name = output_name + '_bn_'
  
      builder.add_batchnorm(
          name = name + '_normalize',
          channels = channels,
          gamma = _np.ones((channels, )),
          beta = _np.zeros((channels, )),
          mean = None,
          variance = None,
          input_name = input_name,
          output_name = bn_output_name,
          compute_mean_var = True,
          instance_normalization = True)
  
      gamma_input_names = [bn_output_name, gamma_blob_name]
      gamma_output_name = output_name + '_mult_gamma'
      builder.add_elementwise(name=name+'_mult_gamma', input_names=gamma_input_names,
          output_name = gamma_output_name, mode='MULTIPLY', alpha = None)
      beta_input_names = [gamma_output_name, beta_blob_name]
      builder.add_elementwise(name=name+'_add_beta', input_names=beta_input_names,
          output_name = output_name, mode='ADD', alpha=None)
docs: Convert an instance norm layer from mxnet to coreml.
  
      Parameters
      ----------
      net: network
          A mxnet network object.
  
      node: layer
          Node to convert.
  
      model: model
          An model for MXNet
  
      builder: NeuralNetworkBuilder
          A neural network builder object.