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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def s(name, parallel, inputs, outputs, image, programs=None, disk=None, cores=None, unlist=None, no_files=False):
 """Represent a step in a workflow. name -- The run function name, which must match a definition in distributed/multitasks inputs -- List of input keys required for the function. Each key is of the type: ["toplevel", "sublevel"] -- an argument you could pass to toolz.get_in. outputs -- List of outputs with information about file type. Use cwlout functions programs -- Required programs for this step, used to define resource usage. disk -- Information about disk usage requirements, specified as multipliers of input files. Ensures enough disk present when that is a limiting factor when selecting cloud node resources. cores -- Maximum cores necessary for this step, for non-multicore processes. unlist -- Variables being unlisted by this process. Useful for parallelization splitting and batching from multiple variables, like variant calling. no_files -- This step does not require file access. parallel -- Parallelization approach. There are three different levels of parallelization, each with subcomponents: 1. multi -- Multiple samples, parallelizing at the sample level. Used in top-level workflow. - multi-parallel -- Run individual samples in parallel. - multi-combined -- Run all samples together. - multi-batch -- Run all samples together, converting into batches of grouped samples. 2. single -- A single sample, used in sub-workflows. - single-split -- Split a sample into sub-components (by read sections). - single-parallel -- Run sub-components of a sample in parallel. - single-merge -- Merge multiple sub-components into a single sample. - single-single -- Single sample, single item, nothing fancy. 3. batch -- Several related samples (tumor/normal, or populations). Used in sub-workflows. - batch-split -- Split a batch of samples into sub-components (by genomic region). - batch-parallel -- Run sub-components of a batch in parallel. - batch-merge -- Merge sub-components back into a single batch. - batch-single -- Run on a single batch. """

    Step = collections.namedtuple("Step", "name parallel inputs outputs image programs disk cores unlist no_files")
    if programs is None: programs = []
    if unlist is None: unlist = []
    return Step(name, parallel, inputs, outputs, image, programs, disk, cores, unlist, no_files)