Initial Sequence support

- This adds support for ONNX sequences, along with basic unit tests for
   the sequence types.

 - This also introduces a new test network generated via sklearn in an
   included script.

 - The test using sklearn_randomforest.onnx has not been written yet,
   but I wanted to commit after doing all the work so far. The existing
   tests all pass.

test_data/generate_sklearn_network.py

@@ -0,0 +1,45 @@
+# This script is a modified version of the example from
+# https://pypi.org/project/skl2onnx/, which we use to produce
+# sklearn_randomforest.onnx. sklearn makes heavy use of onnxruntime maps and
+# sequences in its networks, so this is used for testing those data types.
+
+import numpy as np
+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+
+iris = load_iris()
+inputs, outputs = iris.data, iris.target
+inputs = inputs.astype(np.float32)
+inputs_train, inputs_test, outputs_train, outputs_test = train_test_split(inputs, outputs)
+classifier = RandomForestClassifier()
+classifier.fit(inputs_train, outputs_train)
+
+# Convert into ONNX format.
+from skl2onnx import to_onnx
+output_filename = "sklearn_randomforest.onnx"
+onnx_content = to_onnx(classifier, inputs[:1])
+with open(output_filename, "wb") as f:
+    f.write(onnx_content.SerializeToString())
+
+# Compute the prediction with onnxruntime.
+import onnxruntime as ort
+
+def float_formatter(f):
+    return f"{float(f):.06f}"
+
+np.set_printoptions(formatter = {'float_kind': float_formatter})
+session = ort.InferenceSession(output_filename)
+print(f"Input names: {[n.name for n in session.get_inputs()]!s}")
+print(f"Output names: {[o.name for o in session.get_outputs()]!s}")
+example_inputs = inputs_test.astype(np.float32)[:6]
+print(f"Inputs shape = {example_inputs.shape!s}")
+onnx_predictions = session.run(["output_label", "output_probability"],
+    {"X": example_inputs})
+labels = onnx_predictions[0]
+probabilities = onnx_predictions[1]
+
+print(f"Inputs to network: {example_inputs.astype(np.float32)}")
+print(f"ONNX predicted labels: {labels!s}")
+print(f"ONNX predicted probabilities: {probabilities!s}")
+