Apache Beam 中的 DoFn 是用于数据处理的函数对象。在创建自定义 DoFn 时，常常需要指定 process 方法及其关键字参数。具体实现方法如下：

import apache_beam as beam

class MyDoFn(beam.DoFn):
    def process(self, element, first_arg, second_arg='default_value'):
        # process the input element using first_arg and second_arg
        # return an iterable of output elements
        return [result]

其中，process 方法需要至少一个参数 element，代表输入的数据。需要处理的其他参数可以通过在类中定义相应的变量并在 process 方法中使用来实现。

示例代码：

import apache_beam as beam

class FilterWordsFn(beam.DoFn):
    def __init__(self, words_to_filter):
        self.words_to_filter = words_to_filter
        
    def process(self, element, min_length=5, max_length=10):
        results = []
        if isinstance(element, str) and min_length <= len(element) <= max_length:
            for word in element.split():
                if word not in self.words_to_filter:
                    results.append(word)
        return results

input_data = ['Hello world', 'this is a test sentence', 'some more words to filter out']
words_to_filter = ['is', 'a', 'to']

with beam.Pipeline() as p:
    output_data = (
        p
        | 'Create input data' >> beam.Create(input_data)
        | 'Filter words' >> beam.ParDo(FilterWordsFn(words_to_filter))
        | 'Join words' >> beam.CombineGlobally(lambda x: ' '.join(x))
        | 'Print output' >> beam.Map(print)
    )

这里的 FilterWordsFn 类是一个自定义 DoFn，它用于过滤掉输入数据中包含特定单词的单词或短语，并且只返回长度在 5 至 10 之间的单词或短语。min_length 和 max_length 都为可选关键字参数，如果未指定，则默认为 5 和 10。示例代码用 Create、Par