Gekko and Intermediate variables

I am using GEKKO to fit a function.

The form of a function is known.
It looks like a sum of similar subfunctions with various parameters,
each subfunction has its own set of parameters to find (optimize)…

I don’t think I understand the use of intermediate variables fully and would love some help with my code.
I am using intermediate variables to minimize the code and make it more readable.

I got an error:

apm 212.160.70.179_gk_model1 <br><pre> ----------------------------------------------------------------
 APMonitor, Version 1.0.1
 APMonitor Optimization Suite
 ----------------------------------------------------------------
 
 @error: Intermediate Definition
 Error: Intermediate variable with no equality (=) expression
   30.124819430.1605366830.1963387730.2322259530.2681985430.30425683
 STOPPING...

As I understood I am getting some array as output?

How can I understand which Intermediate variable causes a problem?
Or what am I doing wrong?

Here is the code:

# using GEKKO for preliminary fitting
xData = np.array(df['E'])
yData = np.array(df['exp_trans'])

m = GEKKO()

# parameters
x = m.Param(value = xData) # x-coordinates for fitting
z = m.Param(value = yData) # experimental results to fit to

# constants
A1 =  m.Const(A1_c)
gj = m.Const(gj_c)

# variables
El_0 = m.FV(lb = borders_left[0], ub=borders_right[0])
El_1 = m.FV(lb = borders_left[1], ub=borders_right[1])
El_2 = m.FV(lb = borders_left[2], ub=borders_right[2])

G1_0 = m.FV(lb = 0.000001, ub=1)
G1_1 = m.FV(lb = 0.000001, ub=1)
G1_2 = m.FV(lb = 0.000001, ub=1)

# G2 = 0
G2_0 = m.FV(lb = 0.000000, ub=0)
G2_1 = m.FV(lb = 0.000000, ub=0)
G2_2 = m.FV(lb = 0.000000, ub=0)

Gg_0 = m.FV(lb = 0.000001, ub=1)
Gg_1 = m.FV(lb = 0.000001, ub=1)
Gg_2 = m.FV(lb = 0.000001, ub=1)

#Intermediates
k_alfa = m.Intermediate(A1*np.sqrt(x))
ro = m.Intermediate(k_alfa*ac)

G_0 = m.Intermediate(G1_0+G2_0+Gg_0)
G_1 = m.Intermediate(G1_1+G2_1+Gg_1)
G_2 = m.Intermediate(G1_2+G2_2+Gg_2)

d0 = m.Intermediate((El_0-x)**2+(G_0/2)**2)
d1 = m.Intermediate((El_1-x)**2+(G_1/2)**2)
d2 = m.Intermediate((El_2-x)**2+(G_2/2)**2)

phi = m.Intermediate(ro)

f0=m.Intermediate((1-(1-(G_0*G1_0/(2*d0)))*m.cos(2*phi)-((El_0-x)*G1_0/d0)*m.sin(2*phi)))

f1=m.Intermediate((1-(1-(G_1*G1_1/(2*d1)))*m.cos(2*phi)-((El_1-x)*G1_1/d1)*m.sin(2*phi)))

f2=m.Intermediate((1-(1-(G_2*G1_2/(2*d2)))*m.cos(2*phi)-((El_2-x)*G1_2/d2)*m.sin(2*phi)))

sigma_sum = m.Intermediate(2*math.pi*gj/k_alfa * (f0+f1+f2))

# designing an equation for the model
y = m.Var()

m.Equation(y == m.exp(-n*sigma_sum))

m.Minimize(((y-z))**2)
  
# Options
El_0.STATUS = 1
El_1.STATUS = 1
El_2.STATUS = 1

G1_0.STATUS = 1
G1_1.STATUS = 1
G1_2.STATUS = 1

G2_0.STATUS = 1
G2_1.STATUS = 1
G2_2.STATUS = 1

Gg_0.STATUS = 1
Gg_1.STATUS = 1
Gg_2.STATUS = 1

m.options.IMODE = 2
m.options.SOLVER = 3

m.options.MAX_ITER = 1000

m.solve(disp=1)