const {{TYPE}}& var_{{NAME}}({{DIM_VARS}}) const {
    return (*static_cast<const {{TYPE}}(*){{DIM_SIZES}}>(
        arg_data({{I}}))){{INDICES}};
  }
  int var_{{NAME}}_size() const {
    return {{COUNT}} * sizeof({{TYPE}});
  }
  int var_{{NAME}}_count() const {
    return {{COUNT}};
  }
)";
    const tf2xla::Variable& var = config.variable(i - config.feed_size());

	}
	n.Rhs = []ir.Node{call}
	n.SetOp(ir.OAS2FUNC)

	// don't generate a = *var if a is _
	if ir.IsBlank(a) {
		return walkExpr(typecheck.Stmt(n), init)
	}

	var_ := typecheck.TempAt(base.Pos, ir.CurFunc, types.NewPtr(t.Elem()))
	var_.SetTypecheck(1)
	var_.MarkNonNil() // mapaccess always returns a non-nil pointer

	n.Lhs[0] = var_
	init.Append(walkExpr(n, init))

		}
	}
	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

  call->set_requested_device(kXlaDeviceName);
  call->AddAttr(kXlaHasReferenceVarsAttr, false);

  auto var =
      ops::VarHandleOp(root.WithOpName("var"), DT_INT32, TensorShape({}));
  auto int32_on_device =
      ops::ReadVariableOp(root.WithOpName("int32_on_device"), var, DT_INT32);

  root.graph()->AddEdge(int32_on_device.node(), 0, call, 0);

  std::unique_ptr<Graph> graph;

		init.Append(typecheck.Stmt(nif))

		t = types.NewArray(t.Elem(), i) // [r]T
		var_ := typecheck.TempAt(base.Pos, ir.CurFunc, t)
		appendWalkStmt(init, ir.NewAssignStmt(base.Pos, var_, nil))  // zero temp
		r := ir.NewSliceExpr(base.Pos, ir.OSLICE, var_, nil, l, nil) // arr[:l]
		// The conv is necessary in case n.Type is named.
		return walkExpr(typecheck.Expr(typecheck.Conv(r, n.Type())), init)

  // 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')

    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 = ...

                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)");
                }
            }
        }
        '''