Using gurobi optimize variable

Please help, In the below code, i want to  optimize the C such that maximum bw_initial[i][k] is selected. This code does not produce the correct value for all i..when i run i got this

for i=1, C[1,1] 0  C[1,2] 1  C[1,3] 0  C[1,4] 0  when  bw[1,1] = 17.845765   bw[1,2] = 47.036043  bw[1,3] = 17.62816  bw[1,4] = 45.454545

it's correct as maximum value is for index(col) 2

for i=2, C[2,1] 1  C[2,2] 0  C[2,3] 0  C[2,4] 0  when bw[2,1] = 39.528471 bw[2,2] = 60.633906
 bw[2,3] = 29.961076  bw[2,4] = 70.710678

it's not correct because maximum value is for index 2 (col),

for i=3, C[3,1] 1  C[3,2] 0  C[3,3] 0 C[3,4] 0 when  bw[3,1] = 19.07272 bw[3,2] = 30.700278
bw[3,3] = 89.442719  bw[3,4] = 22.134948

it's not correct because maximum value is for index(col) 3

C[4,1] 0 C[4,2] 0 C[4,3] 1  C[4,4] 0  bw[4,1] = 19.869293  bw[4,2] = 24.189616  bw[4,3] = 164.39898  bw[4,4] = 19.400111

it's correct as maximum value is for index (col) 3

C[5,1] 0  C[5,2] 0 C[5,3] 0 C[5,4] 1    correct 
C[6,1] 0 C[6,2] 0 C[6,3] 1 C[6,4] 0  correct 
C[7,1] 0 C[7,2] 0 C[7,3] 1 C[7,4] 0   correct
C[8,1] 0 C[8,2] 0 C[8,3] 1 C[8,4] 0  correct 
C[9,1] 0 C[9,2] 1 C[9,3] 0 C[9,4] 0       correct
C[10,1] 0 C[10,2] 0 C[10,3] 0 C[10,4] 1  correct

C[11,1] 1  C[11,2] 0 C[11,3] 0 C[11,4] 0  it's not correct as index 2 has maximum  bw[11,1] 23.2119  bw[11,2] 250 bw[11,3] 30.6282 bw[11,4] 52.7046  
C[12,1] 0 C[12,2] 0 C[12,3] 1 C[12,4] 0 correct 

import gurobipy as gp
from gurobipy import GRB
import numpy.random as nmp
import numpy as nmp1


M=4
R=2
P=12


r_d=nmp1.array([0,2784.1197313700336,1732.8079780363323,1838.9621555991528,2658.1153759429553,2614.766454499971,2851.0224151217917,    1926.7146058957019,2782.155110131377,2593.0183386247504,2337.4051758710193,1813.66195993706,2195.782753056987])




#action_data 
x = nmp.uniform(0,0,size=(P+1))
a_d=nmp1.array(x)
for i in range(P+1):
    a_d[i]=r_d[i]/5
    
d=nmp1.array([0,0.9402464272132742,0.7448813804681473,0.6565893304701793,0.6008024612896197,0.5146076482342433,0.582672879367322,0.8669282654741378,0.8590528679956628,0.946149432999205,0.604588896353693,0.9652070673409696,0.9263662096190788])

CU=nmp1.array([0,633.0078669901113,883.1634714339139,805.3492477945315,550.7580377592622,874.7768423200384,774.8169342185408,758.161700861176,615.0165436142821,574.9698857827192,534.8853850841147,    512.4025723207523,898.920298549464 ])
    




x = nmp.uniform(0,0,size=(P+1,M+1))
bw_initial=nmp1.array([[0,0,0,0,0],[0,17.845765, 47.036043, 17.62816, 45.454545],[0, 39.528471,60.633906, 29.961076, 70.710678],[0,19.07272, 30.700278, 89.442719, 22.134948],[ 0, 19.869293, 24.189616, 164.398987, 19.400111],[0,50.188561, 36.155076, 21.276596, 64.415663],[ 0, 14.382907, 28.194194, 29.210312, 19.725746
],[0, 19.748813, 20.129242,70.359754, 17.200523],[0, 19.964097, 26.602896, 200, 20.61527],[0, 26.416599,200, 26.602896, 95.782629],[0, 24.992191,76.92307700000001, 21.295885, 164.398987],[0, 23.211917, 250, 30.628195, 52.704628],[0, 21.551524,36.060922,101.534617,25.342864]])


#create model m
m = gp.Model()

#ceate optimize and auxiliary variables
TL=m.addVars(range(1,P+1),vtype=GRB.CONTINUOUS,name="TL")
C=m.addVars(range(1,P+1),range(1,M+1),lb=0,ub=1,vtype=GRB.BINARY, name="C")
X = m.addVar(vtype=GRB.CONTINUOUS, name="X")
bw=m.addVars(range(1,P+1),range(1,M+1),vtype=GRB.CONTINUOUS,name="bw")

m.setObjective(X, GRB.MINIMIZE) # objective to minimize the GFT
m.addConstr(X== gp.max_(TL[i] for i in range(1,P+1)), name="GFT")


for i in range(1,P+1):
    for j in range(1,M+1):
        m.addConstr(bw[i,j]==bw_initial[i][j])

for i in range(1,12+1):
    m.addConstr(TL[i]==(r_d[i]*d[i]/CU[i] + a_d[i]*sum(1/bw_initial[i][k]*C[i,k] for k in range(1,M+1))),"TLc") #optimize
    
for i in range(1,P+1):
    m.addConstr(sum(C[i,k] for k in range(1,M+1))==1) #optimize model 2

m.optimize()
for v in m.getVars():
    print('%s %g' % (v.VarName, v.X))    
print('Obj: %g' % m.ObjVal)
optimal_value=m.ObjVal
m.write("mymodel_gft_optimize.lp")