parameters: (net, node, module, builder)
docstring: Convert a dense 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.
docstring_summary: Convert a dense layer from mxnet to coreml.
docstring_tokens: Convert a dense layer from mxnet to coreml .
function: def convert_dense(net, node, module, builder):
      """Convert a dense 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.
      """
      input_name, output_name = _get_input_output_name(net, node)
      name = node['name']
  
      inputs = node['inputs']
      param = node['attrs']
      if 'no_bias' in param.keys():
          has_bias = not literal_eval(param['no_bias'])
      else:
          has_bias = True
  
      args, _ = module.get_params()
      W = args[_get_node_name(net, inputs[1][0])].asnumpy()
      if has_bias:
          Wb = args[_get_node_name(net, inputs[2][0])].asnumpy()
      else:
          Wb = None
      nC, nB = W.shape
  
      builder.add_inner_product(
          name=name,
          W=W,
          b=Wb,
          input_channels=nB,
          output_channels=nC,
          has_bias=has_bias,
          input_name=input_name,
          output_name=output_name
      )
function_tokens: def convert_dense ( net , node , module , builder ) : input_name , output_name = _get_input_output_name ( net , node ) name = node [ 'name' ] inputs = node [ 'inputs' ] param = node [ 'attrs' ] if 'no_bias' in param . keys ( ) : has_bias = not literal_eval ( param [ 'no_bias' ] ) else : has_bias = True args , _ = module . get_params ( ) W = args [ _get_node_name ( net , inputs [ 1 ] [ 0 ] ) ] . asnumpy ( ) if has_bias : Wb = args [ _get_node_name ( net , inputs [ 2 ] [ 0 ] ) ] . asnumpy ( ) else : Wb = None nC , nB = W . shape builder . add_inner_product ( name = name , W = W , b = Wb , input_channels = nB , output_channels = nC , has_bias = has_bias , input_name = input_name , output_name = output_name )