Map MXNet's random_normal operator attributes to onnx's RandomNormal
    operator and return the created node.

def convert_random_normal(node, **kwargs):
    """Map MXNet's random_normal operator attributes to onnx's RandomNormal
    operator and return the created node.
    """
    name, input_nodes, attrs = get_inputs(node, kwargs)

    # Converting to float32
    mean = float(attrs.get("loc", 0))
    scale = float(attrs.get("scale", 1.0))
    shape = convert_string_to_list(attrs.get('shape', '[]'))
    dtype = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(attrs.get('dtype', 'float32'))]

    node = onnx.helper.make_node(
        'RandomNormal',
        input_nodes,
        [name],
        mean=mean,
        scale=scale,
        dtype=dtype,
        shape=shape,
        name=name
    )
    return [node]