code: def convert_convolution(
      builder, layer, input_names, output_names, keras_layer
  ):
      """Convert convolution layer from keras to coreml.
  
      Parameters
      ----------
      keras_layer: layer
          A keras layer object.
  
      builder: NeuralNetworkBuilder
          A neural network builder object.
      """
      # Get input and output names
      input_name, output_name = (input_names[0], output_names[0])
  
      has_bias = keras_layer.bias
      is_deconv = isinstance(
          keras_layer, keras.layers.convolutional.Deconvolution2D
      )
  
      # Get the weights from keras.
      # Keras stores convolution weights as list of numpy arrays
      weightList = keras_layer.get_weights()
      output_shape = list(filter(None, keras_layer.output_shape))[:-1]
  
      # Parameter
      height, width, channels, n_filters = weightList[0].shape
      stride_height, stride_width = keras_layer.subsample
  
      # Weights and bias terms
      W = weightList[0]
      b = weightList[1] if has_bias else None
  
      # dilation factors
      dilation_factors = [1, 1]
      if isinstance(
          keras_layer, keras.layers.convolutional.AtrousConvolution2D
      ):
          dilation_factors = list(keras_layer.atrous_rate)
  
      builder.add_convolution(
          name=layer,
          kernel_channels=channels,
          output_channels=n_filters,
          height=height,
          width=width,
          stride_height=stride_height,
          stride_width=stride_width,
          border_mode=keras_layer.border_mode,
          groups=1,
          W=W,
          b=b,
          has_bias=has_bias,
          is_deconv=is_deconv,
          output_shape=output_shape,
          input_name=input_name,
          output_name=output_name,
          dilation_factors=dilation_factors,
      )
  
imports: from .None import _utils
  import keras
  import numpy
label_desc: Use image node_id 13 for calling a global function with example usage: convert_convolution(builder, layer, input_names, output_names, keras_layer) without return types