(builder, node, graph, err)
convert to CoreML ConcatND Layer:
    https://github.com/apple/coremltools/blob/655b3be5cc0d42c3c4fa49f0f0e4a93a26b3e492/mlmodel/format/NeuralNetwork.proto#L3521
convert to CoreML ConcatND Layer:
    https://github.com/apple/coremltools/blob/655b3be5cc0d42c3c4fa49f0f0e4a93a26b3e492/mlmodel/format/NeuralNetwork.proto#L3521
convert to CoreML ConcatND Layer : https : // github . com / apple / coremltools / blob / 655b3be5cc0d42c3c4fa49f0f0e4a93a26b3e492 / mlmodel / format / NeuralNetwork . proto#L3521
def _convert_concat(builder, node, graph, err):
    '''
    convert to CoreML ConcatND Layer:
    https://github.com/apple/coremltools/blob/655b3be5cc0d42c3c4fa49f0f0e4a93a26b3e492/mlmodel/format/NeuralNetwork.proto#L3521
    '''
    axis = node.attrs.get('axis')
    load_input_constants(builder, node, graph, err)

    # TODO: Adding Linear layer will change to
    #       either: Skip the op right away
    #       or:     Insert Linear and perform copy-propogation followed by dead code elimination        
    if len(node.inputs) == 1:
        builder.add_activation(
            name=node.name,
            non_linearity = 'LINEAR',
            input_name=node.inputs[0],
            output_name=node.outputs[0],
            params=[1.0, 0.0]
        )
    else:
        builder.add_concat_nd(
            name=node.name,
            input_names=node.inputs,
            output_name=node.outputs[0],
            axis=axis
        )
def _convert_concat ( builder , node , graph , err ) : axis = node . attrs . get ( 'axis' ) load_input_constants ( builder , node , graph , err ) # TODO: Adding Linear layer will change to #       either: Skip the op right away #       or:     Insert Linear and perform copy-propogation followed by dead code elimination         if len ( node . inputs ) == 1 : builder . add_activation ( name = node . name , non_linearity = 'LINEAR' , input_name = node . inputs [ 0 ] , output_name = node . outputs [ 0 ] , params = [ 1.0 , 0.0 ] ) else : builder . add_concat_nd ( name = node . name , input_names = node . inputs , output_name = node . outputs [ 0 ] , axis = axis )