human
Solve the following python programming problem given the following code:
# Advanced function
# Demonstrates imports, helper functions and multiple outputs
from typing import NamedTuple

def my_divmod(dividend: float, 
              divisor: float,
             ) -> NamedTuple('MyDivmodOutput', [('quotient', float), ('remainder', float), 
                                                ('mlpipeline_ui_metadata', 'UI_metadata'), 
                                                ('mlpipeline_metrics', 'Metrics')]):
    
    '''Divides two numbers and calculate  the quotient and remainder'''
    
    #Imports inside a component function:
    import numpy as np

    #This function demonstrates how to use nested functions inside a component function:
    def divmod_helper(dividend, divisor):
        return np.divmod(dividend, divisor)

    (quotient, remainder) = divmod_helper(dividend, divisor)

    import json
    
    # Exports a sample tensorboard:
    metadata = {
      'outputs' : [{
        'type': 'tensorboard',
        'source': 'gs://ml-pipeline-dataset/tensorboard-train',
      }]
    }

    # Exports two sample metrics:
    metrics = {
      'metrics': [{
          'name': 'quotient',
          'numberValue':  float(quotient),
        },{
          'name': 'remainder',
          'numberValue':  float(remainder),
        }]}

    from collections import namedtuple
    divmod_output = namedtuple('MyDivmodOutput', 
                               ['quotient', 'remainder', 'mlpipeline_ui_metadata', 'mlpipeline_metrics'])
    return divmod_output(quotient, remainder, json.dumps(metadata), json.dumps(metrics))

my_divmod(100, 7)

divmod_op = comp.func_to_container_op(func=my_divmod, 
                                      base_image="tensorflow/tensorflow:1.15.0-py3")
Given the above code, generate some markdown instructions for it.
assistant
#Here is Python code to calculate  the quotient and remainder'''
.

A more complex example, with multiple outputs