Need a sample for saving and loading CustomMapping Transform

[artidoro]

We currently only have a simple sample to use the custom mapping transform, but saving and loading is a bit more involved and a sample about that would be very useful.

Here is the current sample that we have:
https://github.com/dotnet/machinelearning/blob/master/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/CustomMappingSample.cs

Here is a test that shows how to save and load the custom mapping transform:

machinelearning/test/Microsoft.ML.Tests/Transformers/CustomMappingTests.cs

Lines 33 to 85 in 3663320

	[CustomMappingFactoryAttribute("MyLambda")]
	public class MyLambda : CustomMappingFactory<MyInput, MyOutput>
	{
	public static void MyAction(MyInput input, MyOutput output)
	{
	output.Together = $"{input.Float1} + {string.Join(", ", input.Float4)}";
	}
	public override Action<MyInput, MyOutput> GetMapping()
	{
	return MyAction;
	}
	}
	[Fact]
	public void TestCustomTransformer()
	{
	string dataPath = GetDataPath("adult.tiny.with-schema.txt");
	var source = new MultiFileSource(dataPath);
	var loader = ML.Data.CreateTextLoader(new[] {
	new TextLoader.Column("Float1", DataKind.Single, 9),
	new TextLoader.Column("Float4", DataKind.Single, new[]{new TextLoader.Range(9), new TextLoader.Range(10), new TextLoader.Range(11), new TextLoader.Range(12) })
	}, hasHeader: true);
	var data = loader.Load(source);
	IDataView transformedData;
	// We create a temporary environment to instantiate the custom transformer. This is to ensure that we don't need the same
	// environment for saving and loading.
	var tempoEnv = new MLContext();
	var customEst = new CustomMappingEstimator<MyInput, MyOutput>(tempoEnv, MyLambda.MyAction, "MyLambda");
	try
	{
	TestEstimatorCore(customEst, data);
	Assert.True(false, "Cannot work without RegisterAssembly");
	}
	catch (InvalidOperationException ex)
	{
	if (!ex.IsMarked())
	throw;
	}
	ML.ComponentCatalog.RegisterAssembly(typeof(MyLambda).Assembly);
	TestEstimatorCore(customEst, data);
	transformedData = customEst.Fit(data).Transform(data);
	var inputs = ML.Data.CreateEnumerable<MyInput>(transformedData, true);
	var outputs = ML.Data.CreateEnumerable<MyOutput>(transformedData, true);
	Assert.True(inputs.Zip(outputs, (x, y) => y.Together == $"{x.Float1} + {string.Join(", ", x.Float4)}").All(x => x));
	Done();
	}