code: def convert_dense(
      builder, layer, input_names, output_names, keras_layer
  ):
      """Convert a dense 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
      # Get the weights from keras
      W = keras_layer.get_weights()[0].T
      Wb = keras_layer.get_weights()[1].T if has_bias else None
  
      builder.add_inner_product(
          name=layer,
          W=W,
          b=Wb,
          input_channels=keras_layer.input_dim,
          output_channels=keras_layer.output_dim,
          has_bias=has_bias,
          input_name=input_name,
          output_name=output_name,
      )
  
imports: from .None import _utils
  import keras
  import numpy
label_desc: Use image node_id 5 for calling a global function with example usage: convert_dense(builder, layer, input_names, output_names, keras_layer) without return types