add bfloat16 support for ConcatTraining and SplitTraining ops (micros…

…oft#18280)

### Description
<!-- Describe your changes. -->

Updates input/output type constraints on training operators
ConcatTraining and SplitTraining to include bfloat16 which was
introduced in IR version 4.

### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->

Enabling `meta-llama/Llama-2-70b` to be finetuned with ONNX Runtime
training.

Co-authored-by: Prathik Rao <[email protected]@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>

orttraining/orttraining/core/graph/training_op_defs.cc

@@ -2193,7 +2193,7 @@ Example 4:
               OpSchema::Variadic)
       .TypeConstraint(
           "T",
-          OpSchema::all_tensor_types(),
+          OpSchema::all_tensor_types_ir4(),
           "Constrain input and output types to all tensor types.")
       .TypeAndShapeInferenceFunction([](ONNX_NAMESPACE::InferenceContext& ctx) {
         for (int i = 0; i < static_cast<int>(ctx.getNumOutputs()); ++i) {
@@ -2270,7 +2270,7 @@ Example 4:
               OpSchema::Optional)
       .TypeConstraint(
           "T",
-          OpSchema::all_tensor_types(),
+          OpSchema::all_tensor_types_ir4(),
           "Constrain output types to any tensor type.")
       .TypeConstraint(
           "Tint",