def inf_norm(input, porder=None, axis=axis, keepdim=False, asvector=False, name=None):
    helper = LayerHelper('frobenius_norm', **locals())
    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
    helper.append_op(type='abs', inputs={'X': input}, outputs={'Out': out})
    reduce_out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
    reduce_all = (True if ((axis == None) or (axis == []) or (asvector == True)) else False)
    axis = (axis if ((axis != None) and (axis != [])) else [0])
    reduce_type = ('reduce_max' if (porder == np.float('inf')) else 'reduce_min')
    helper.append_op(type=reduce_type, inputs={'X': out}, outputs={'Out': reduce_out}, attrs={'dim': axis, 'keep_dim': keepdim, 'reduce_all': reduce_all})
    return reduce_out
def inf_norm(input, porder=None, axis=axis, keepdim=False, asvector=False, name=None):
    helper = LayerHelper('frobenius_norm', **locals())
    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
    helper.append_op(type='abs', inputs={'X': input}, outputs={'Out': out})
    reduce_out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
    reduce_all = (True if ((axis == None) or (axis == []) or (asvector == True)) else False)
    axis = (axis if ((axis != None) and (axis != [])) else [0])
    reduce_type = ('reduce_max' if (porder == np.float('inf')) else 'reduce_min')
    helper.append_op(type=reduce_type, inputs={'X': out}, outputs={'Out': reduce_out}, attrs={'dim': axis, 'keep_dim': keepdim, 'reduce_all': reduce_all})
    return reduce_out