VariableInfo::VariableInfo(
    int index, absl::string_view name, Var* var,
    const std::optional<ManagedStackTrace>& definition_stack_trace)
    : index_(index),
      name_(name),
      var_(var),
      definition_stack_trace_(definition_stack_trace) {}

VariableInfo::VariableInfo(VariableInfo&& other)
    : index_(other.index_),
      var_(other.var_),
      definition_stack_trace_(other.definition_stack_trace_),

      grads = gt.gradient(y, vars_)  # uses registered gradient function.

    # compute with composition
    with backprop.GradientTape() as gt:
      for var_ in vars_:
        gt.watch(var_)
      re_y = compute_composite(**kwargs)  # uses composite function.
      re_grads = gt.gradient(re_y, vars_)  # uses gradients compposite function.

    for v, re_v in zip(y, re_y):
      self.assertAllClose(v, re_v)

					v.AddArg(w)
					continue
				}
			}

			if _, ok := s.varnum[var_]; ok {
				continue
			}
			s.varnum[var_] = int32(len(vartypes))
			if debugPhi {
				fmt.Printf("var%d = %v\n", len(vartypes), var_)
			}
			vars = append(vars, var_)
			vartypes = append(vartypes, v.Type)
		}
	}

	if len(vartypes) == 0 {
		return
	}

	// Find all definitions of the variables we need to process.

		}
		appendWalkStmt(init, ir.NewAssignStmt(base.Pos, var_, r))

		var_ = ir.NewStarExpr(base.Pos, var_)
		var_ = typecheck.AssignExpr(var_)
		anylit(n.X, var_, init)

	case ir.OSTRUCTLIT, ir.OARRAYLIT:
		n := n.(*ir.CompLitExpr)
		if !t.IsStruct() && !t.IsArray() {
			base.Fatalf("anylit: not struct/array")
		}

		if isSimpleName(var_) && len(n.List) > 4 {
			// lay out static data

  int index() const { return index_; }

  // A pointer to the resource variable.  May be null if this VariableInfo is
  // "empty", i.e. it does not track a resource variable.
  Var* var() const { return var_; }

  // Returns the variable name.
  absl::string_view name() const { return name_; }

  // Returns true if the resource variable lock was successfully acquired by
  // this thread.

  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;