variable->set_node_name("myvar_readonly");
  variable->mutable_shape()->add_dim()->set_size(1);
  variable->set_type(DT_FLOAT);
  variable->set_readonly(true);
  tf2xla::Variable* variable2 = config.add_variable();
  variable2->set_node_name("myvar");
  variable2->mutable_shape()->add_dim()->set_size(1);
  variable2->set_type(DT_FLOAT);
  tf2xla::Variable* variable3 = config.add_variable();
  variable3->set_node_name("my/var");

      AttrValue attr_val;
      // For type attributes, we only propagate the element type.
      mlir::Type elt_type = attr.getValue();
      if (auto shaped_type = mlir::dyn_cast<mlir::ShapedType>(elt_type)) {
        elt_type = shaped_type.getElementType();
      }
      TF_RETURN_IF_ERROR(
          ConvertAttribute(elt_type, remove_ref_type, &attr_val));
      list->add_type(attr_val.type());

  node_def->set_op(FunctionLibraryDefinition::kRetOp);
  DataType dtype;
  TF_RETURN_IF_ERROR(ConvertToDataType(
      mlir::cast<mlir::TensorType>(operand.getType()).getElementType(),
      &dtype));
  AttrValue type_attr;
  type_attr.set_type(dtype);
  (*node_def->mutable_attr())["T"] = type_attr;
  AttrValue index_attr;
  index_attr.set_i(index);
  (*node_def->mutable_attr())["index"] = index_attr;

  node_def->set_op(FunctionLibraryDefinition::kRetOp);
  DataType dtype;
  TF_RETURN_IF_ERROR(ConvertToDataType(
      mlir::cast<mlir::TensorType>(operand.getType()).getElementType(),
      &dtype));
  AttrValue type_attr;
  type_attr.set_type(dtype);
  (*node_def->mutable_attr())["T"] = type_attr;
  AttrValue index_attr;
  index_attr.set_i(index);
  (*node_def->mutable_attr())["index"] = index_attr;

		n.SetType(nil)
		return n
	}
	if !n.Y.Type().IsInteger() {
		n.SetType(nil)
		return n
	}
	n.SetType(n.X.Type())
	return n
}

// tcUnsafeSlice typechecks an OUNSAFESLICE node.
func tcUnsafeSlice(n *ir.BinaryExpr) *ir.BinaryExpr {
	n.X = Expr(n.X)
	n.Y = Expr(n.Y)
	if n.X.Type() == nil || n.Y.Type() == nil {
		n.SetType(nil)
		return n
	}

  const auto& input_dtype = input_tensor.dtype();
  const auto& input_shape = input_tensor.shape();
  Type elt_type;
  TF_RETURN_IF_ERROR(ConvertDataType(input_dtype, *builder, &elt_type));
  SmallVector<int64_t, 4> shape;
  ConvertToMlirShape(input_shape, &shape);
  auto type = RankedTensorType::get(shape, elt_type);

#define CONVERT_FLAT(DTYPE, CTYPE) \
  case DTYPE:                      \

	if t == nil || r.Type() == nil {
		n.SetType(nil)
		return n
	}
	switch t.Kind() {
	default:
		base.Errorf("invalid operation: %v (type %v does not support indexing)", n, t)
		n.SetType(nil)
		return n

	case types.TSTRING, types.TARRAY, types.TSLICE:
		n.Index = indexlit(n.Index)
		if t.IsString() {
			n.SetType(types.ByteType)
		} else {
			n.SetType(t.Elem())
		}
		why := "string"

		case elf.SHF_ALLOC:
			sb.SetType(sym.SRODATA)

		case elf.SHF_ALLOC + elf.SHF_WRITE:
			if sect.type_ == elf.SHT_NOBITS {
				sb.SetType(sym.SNOPTRBSS)
			} else {
				sb.SetType(sym.SNOPTRDATA)
			}

		case elf.SHF_ALLOC + elf.SHF_EXECINSTR:
			sb.SetType(sym.STEXT)
		}

		if sect.name == ".got" || sect.name == ".toc" {
			sb.SetType(sym.SELFGOT)
		}

        toolchain.setType("TYPE");
        toolchain.setProvides(props);
        PersistedToolchains userResult = new PersistedToolchains();
        userResult.setToolchains(Collections.singletonList(toolchain));

        props = new Properties();
        props.put("key", "global_value");
        toolchain = new ToolchainModel();
        toolchain.setType("TYPE");
        toolchain.setProvides(props);

  return absl::OkStatus();
}

Status MlirAbstractOp::AddRef(Type type, Type* output_type) {
  Type elt_type = getElementTypeOrSelf(type);
  if (mlir::isa<mlir::TF::TensorFlowRefType>(elt_type)) {
    return InvalidArgument("Requested reference to a reference type");
  }
  elt_type = TensorFlowRefType::get(elt_type);
  if (RankedTensorType tensor_type = mlir::dyn_cast<RankedTensorType>(type)) {