human
Solve the following python programming problem given the following code:
# Define a function to filter a single value
def ten(value):
    """Return whether value is below ten.

    Args:
        value (int): A number.

    Returns:
        bool: Whether `value` is less than ten.
    """
    if (value < 10):
        return True
    else:
        return False
# The ten function could also be written concisely as: def ten(value): return value < 10

# Pass the function ten to the filter transformation
# Filter is a transformation so no tasks are run
filteredRDD = subRDD.filter(ten)

# View the results using collect()
# Collect is an action and triggers the filter transformation to run
print filteredRDD.collect()
Given the above code, generate some markdown instructions for it.
assistant
#Here is Python code to print filteredRDD.

(3e) Apply transformation filter and view results with collect 
Next, we'll create a new RDD that only contains the values less than ten by using the filter(f) data-parallel operation. The filter(f) method is a transformation operation that creates a new RDD from the input RDD by applying filter function f to each item in the parent RDD and only passing those elements where the filter function returns True. Elements that do not return True will be dropped. Like map(), filter can be applied individually to each entry in the dataset, so is easily parallelized using Spark.
The figure below shows how this would work on the small four-partition dataset.

To filter this dataset, we'll define a function called ten(), which returns True if the input is less than 10 and False otherwise.  This function will be passed to the filter() transformation as the filter function f.
To view the filtered list of elements less than ten, we need to create a new list on the driver from the distributed data on the executor nodes.  We use the collect() method to return a list that contains all of the elements in this filtered RDD to the driver program.