loop body inine):

    ```mlir
      %var0 = ...
      %var1 = ...
      tf.while (..., %var0, %var1) {
        tf_device.replicate ([%var0, %var1] as %rvar) {
          %compile:2 = "tf._TPUCompileMlir"()
          tf.TPUExecuteAndUpdateVariablesOp(%rvar, compile#1)
        }
      }
    ```

    This pass will transform it into

    ```mlir
      %var0 = ...
      %var1 = ...
      %state_var0 = ...

func.func @init() attributes {tf_saved_model.exported_names = ["__tf_saved_model_session_initializer_init"]} {
  %var0 = "tf.VarHandleOp"() {container = "", shared_name = "var0"} : () -> tensor<!tf_type.resource<tensor<i1>>>
  %cond = "tf.ReadVariableOp"(%var0) {device = "/CPU:0"} : (tensor<!tf_type.resource<tensor<i1>>>) -> tensor<i1>

         "      input_arg {"
         "        name: 'var0'"
         "        type: DT_RESOURCE"
         "      }"
         "      output_arg {"
         "        name: 'var0_value'"
         "        type: DT_FLOAT"
         "      }"
         "    }"
         "    node_def {"
         "      name: 'read0'"
         "      op: 'ReadVariableOp'"
         "      input: 'var0'"
         "      attr {"

        compile {
            attributes.attribute(buildType, 'debug')
            outgoing {
                variants {
                    var1 {
                        artifact oneJar
                        attributes.attribute(flavor, 'one')
                    }
                    var2 {
                        artifact twoJar
                        attributes.attribute(flavor, 'two')
                    }
                }

  // Create 2 resources and initialize them with dummy values.
  TF_CHECK_OK(device->resource_manager()->Create(
      container, "var1", new tensorflow::Var(tensorflow::DataType::DT_FLOAT)));
  TF_CHECK_OK(device->resource_manager()->Create(
      container, "var2", new tensorflow::Var(tensorflow::DataType::DT_FLOAT)));
}

Status FakeSession::Create(const tensorflow::GraphDef& graph) {

        compile {
            outgoing {
                variants {
                    var1 {
                        artifact file('a1.jar')
                        attributes.attribute(buildType, 'profile')
                        attributes.attribute(flavor, 'bland')
                    }
                    var2 {
                        artifact file('a2.jar')

                if (!"value1".equals(System.getProperty("var1"))) {
                    throw new RuntimeException("Expected system property not specified (var1)");
                }
                if (!"value2".equals(System.getProperty("var2"))) {
                    throw new RuntimeException("Expected system property not specified (var2)");
                }
            }
        }
        '''

  template <typename T>
  Var* CreateVariable(const string& name, const TensorShape& shape,
                      const gtl::ArraySlice<T> data) {
    Tensor* init_var_value = CreateDeviceTensor<T>(shape, data);
    Var* var = new Var(DataTypeToEnum<T>::v());
    *var->tensor() = *init_var_value;
    var->is_initialized = true;

    return var;
  }

		}
	}
	return t
}

func (subst *subster) varList(in []*Var) (out []*Var, copied bool) {
	out = in
	for i, v := range in {
		if w := subst.var_(v); w != v {
			if !copied {
				// first variable that got substituted => allocate new out slice
				// and copy all variables
				new := make([]*Var, len(in))
				copy(new, out)
				out = new
				copied = true
			}
			out[i] = w

		}
	}
	return t
}

func (subst *subster) varList(in []*Var) (out []*Var, copied bool) {
	out = in
	for i, v := range in {
		if w := subst.var_(v); w != v {
			if !copied {
				// first variable that got substituted => allocate new out slice
				// and copy all variables
				new := make([]*Var, len(in))
				copy(new, out)
				out = new
				copied = true
			}
			out[i] = w