"RecordingArrays cannot be used across tapes" when backpropagating dlode45

Hi! The program works well but throws error "RecordingArrays cannot be used across tapes" from dlgradient when I added 'GradientMode="adjoint"' to dlode45,

Error using deep.internal.recording.RecordingArray/recordBinary

RecordingArrays cannot be used across tapes.

Error in deep.internal.recording.RecordingArray/permute (line 13)

x = recordBinary(x,order,op);

Error in dlarray/interp1>iGetDataV (line 131)

dataV = permute(dataV, viewToData);

Error in dlarray/interp1 (line 62)

[dataV, dimsV] = iGetDataV(v, isvector(xq));

Error in NeuralODE_Lstm_WindEppFiber_red_v2_adjGrad>odeModel (line 1344)

F_t = interp1(t_ref, F, t)'; % Current excitation

Error in NeuralODE_Lstm_WindEppFiber_red_v2_adjGrad>@(t,y,theta)odeModel(t,y,theta,F,tspan) (line 1322)

X_i = dlode45(@(t, y, theta) odeModel(t, y, theta, F, tspan), tspan, X0, neuralOdeParameters, DataFormat="CB", GradientMode="adjoint");

Error in internal_dlodeBackward>iComputeBatchedAugRightHandSide (line 141)

f = dlodefun(t,z,theta);

Error in deep.AcceleratedFunction/parenReference (line 255)

[varargout{1:nargout}] = obj.Function(inputs{:});

Error in deep.internal.fevalWithNewTape (line 21)

[varargout{1:nargout}] = fun(varargin{:});

Error in internal_dlodeBackward>augDynamicsRightHandSide (line 113)

[f, adf_dz, adf_dtheta] = deep.internal.fevalWithNewTape(gradientFcn,...

Error in internal_dlodeBackward>@(t,s)augDynamicsRightHandSide(t,s,originalTheta,dlodefun,yStateSize,numObs,originalSize,originalFormat,numLearnables,gradientBatchFcn) (line 44)

augDynamics = @(t,s) augDynamicsRightHandSide(t,s,originalTheta,dlodefun,yStateSize,...

Error in internal_dlodeBackward (line 63)

f0 = augDynamics(initialTime,augState);

Error in deep.internal.recording.operations.DlodeOp/backward (line 81)

[outputs{:}] = internal_dlodeBackward(dy,y,op.Y0Format,op.Odefun,tspan,op.Options,...

Error in deep.internal.recording.RecordingArray/backwardPass (line 89)

grad = backwardTape(tm,{y},{initialAdjoint},x,retainData,false,0);

Error in dlarray/dlgradient (line 132)

[grad,isTracedGrad] = backwardPass(y,xc,pvpairs{:});

Error in NeuralODE_Lstm_WindEppFiber_red_v2_adjGrad>modelGradients (line 1334)

gradients = dlgradient(loss, neuralOdeParameters);

Error in deep.internal.dlfeval (line 17)

[varargout{1:nargout}] = fun(x{:});

Error in dlfeval (line 40)

[varargout{1:nargout}] = deep.internal.dlfeval(fun,varargin{:});

Error in NeuralODE_Lstm_WindEppFiber_red_v2_adjGrad (line 486)

[gradients, loss, X_i] = dlfeval(@modelGradients, t, XTrain_Tr, index_batch, neuralOdeParameters, YTrain_Tr, U_Peak_mean);

Here is the odeModel:

function y = odeModel(t, y, theta, F, t_ref)
% Inputs:
%   t = time
%   y = state vector
%   theta = parameters 
%   F = excitation
%   t_ref = time points of the excitation

    F_t = interp1(t_ref, F, t)'; % Current input signal, interpolate to find the signal value at t

    y = tanh(theta.fc1.Weights*y + theta.fc1.Bias);
    y = tanh(theta.fc2.Weights*y + theta.fc2.Bias) + theta.fc3.Weights*F_t;
end

And looks like the error is within "interp1". However, even I tried to replace the "interp1" by the following lines of code the error presists.

    t_left = t_ref(t_ref<=t); F_left = F(t_ref<=t,:);
    t_right = t_ref(t_ref>t); F_right = F(t_ref>t,:);
    t1 = t_left(end); 
    if isempty(t_right)
        t2=-1;
        F2=0;
    else
        t2=t_right(1);
        F2 = F_right(1,:)';
    end
    F1 = F_left(end,:)';
    F_t = (F1*(t2-t) + F2*(t-t1))/(t2-t1);

Thanks in advance!