[Torch] Fix PrimNumToTensorScalarOp::fold (#3339)

In constant folding progress, a new constant op will be created
according to the origin op's result type.

See the code in TorchDialect.cpp.

```cpp
Operation *TorchDialect::materializeConstant(OpBuilder &builder,
                                             Attribute value, Type type,
                                             Location loc) {
  if (auto integerType = dyn_cast<Torch::IntType>(type))
    return builder.create<Torch::ConstantIntOp>(loc, cast<IntegerAttr>(value));

  if (auto floatType = dyn_cast<Torch::FloatType>(type))
    return builder.create<Torch::ConstantFloatOp>(loc, cast<FloatAttr>(value));

  if (auto numberType = dyn_cast<Torch::NumberType>(type)) {
    if (auto floatValue = dyn_cast<mlir::FloatAttr>(value)) {
      return builder.create<Torch::ConstantNumberOp>(loc, floatValue);
    } else if (auto intValue = dyn_cast<mlir::IntegerAttr>(value)) {
      return builder.create<Torch::ConstantNumberOp>(loc, intValue);
    }
  }

  if (isa<Torch::BoolType>(type)) {
    return builder.create<Torch::ConstantBoolOp>(loc, cast<IntegerAttr>(value));
  }

  if (isa<Torch::NoneType>(type))
    return builder.create<ConstantNoneOp>(loc);

  if (auto stringAttr = dyn_cast<StringAttr>(value))
    return builder.create<ConstantStrOp>(loc, stringAttr);

  if (auto elementsAttr = dyn_cast<ElementsAttr>(value)) {
    // Only !torch.vtensor can be constant folded. !torch.tensor has
    // non-trivial aliasing semantics which prevent deduplicating it.
    assert(isa<ValueTensorType>(type) && "should be a vtensor type!");
    return builder.create<ValueTensorLiteralOp>(loc, elementsAttr);
  }

  return nullptr;
}
```
So when the op has a tensor result type, it must be "ValueTensorType"
due to the **assert** statement. However, many fold methods in
TorchOps.cpp only have a judgment of "BaseTensorType".

Author: Xinyu Yang

lib/Dialect/Torch/IR/TorchOps.cpp

@@ -4471,10 +4471,10 @@ OpFoldResult PrimMaxIntOp::fold(FoldAdaptor adaptor) {
 
 OpFoldResult PrimNumToTensorScalarOp::fold(FoldAdaptor adaptor) {
   Attribute a = adaptor.getA();
-  auto resultTy = cast<BaseTensorType>(getType());
+  auto resultTy = dyn_cast<ValueTensorType>(getType());
   if (!a)
     return {};
-  if (!resultTy.hasDtype() || !resultTy.hasSizes())
+  if (!resultTy || !resultTy.hasDtype() || !resultTy.hasSizes())
     return {};
 
   auto dty = resultTy.getDtype();