"p=NaN" at symple slopes analysis of GLMM

Question

I analyzed a simple slope of the GLMM cross-level interaction terms for which significant associations were found. I used "reghelper" package in R.

if (require(lme4, quietly=TRUE)) {
    model <- glmer(Y ~ X * W + (1|Com_ID), data=dat, family='binomial')
    print(summary(model))
    print(simple_slopes(model))
    graph_model(model, y=Y, x=X, lines=W)
}

The outputs of summary and simple_slopes were as follows:

Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
 Family: binomial  ( logit )
Formula: Y ~ X * W + (1 | Com_ID)
   Data: dat

     AIC      BIC   logLik deviance df.resid 
  1441.5   1468.0   -715.7   1431.5     1473 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-0.6387 -0.6055 -0.3888 -0.3147  3.1778 

Random effects:
 Groups Name        Variance Std.Dev.
 Com_ID (Intercept) 0.007915 0.08897 
Number of obs: 1478, groups:  Com_ID, 11

Fixed effects:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)  -1.3638     0.1117 -12.204  < 2e-16 ***
X            -0.4381     0.1095  -3.999 6.37e-05 ***
Wurban       -0.1526     0.1562  -0.977    0.329    
X:Wurban     -0.1977     0.1489  -1.328    0.184    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
         (Intr) X      Wurban
X         0.258              
Wurban   -0.722 -0.185       
X:Wurban -0.187 -0.736  0.289
       X                  W Test Estimate Std. Error t value
1     -1 0.0451892102738553        0.1820     0.2483  0.8039
2      0 -0.152553682044491        0.1562    -0.9766  0.3288
3      1 -0.350285976436759        0.2450    -1.4298  0.1528
4 sstest -0.438054016500106        0.1095    -3.9987  0.0001
5 sstest -0.635779394026394        0.1009    -6.3039  0.0000

The p-value of the simple slope cannot be identified in that package, so I found some articles introducing to a website(http://www.quantpsy.org/interact/hlm2.htm) and used the calculator. However, SE, z and p were "NaN" at simple intercepts and slopes at conditional values of w at w1(2). I don't know what caused this. Can I ask for help?

Y and X are binary data and X is scaled. W is a two-level factor type.

    CASE 3 TWO-WAY INTERACTION SIMPLE SLOPES OUTPUT

Your Input
========================================================
  w1(1)       = 0
  w1(2)       = 1
  x1(1)       = -1
  x1(2)       = 1
  Intercept   = -1.07691
  x1 Slope    = -0.71724
  w1 Slope    = -0.03432
  w1x1 Slope  = -0.56498
  alpha       = 0.05

Asymptotic (Co)variances
========================================================
  var(g00) 0.01709818
  var(g10) 0.05253264
  var(g01) 0.03291322
  var(g11) 0.10903864
  cov(g00,g01) -0.726
  cov(g10,g11) -0.694
  cov(g00,g10) -0.458
  cov(g01,g11) -0.378

Region of Significance on w (level-2 predictor)
========================================================
  w1 at lower bound of region = -0.0508
  w1 at upper bound of region = 61.634
  (simple slopes are significant *inside* this region.)

Simple Intercepts and Slopes at Conditional Values of w
========================================================
  At w1(1)...
    simple intercept = -1.0769(0.1308), z=-8.2358, p=0
    simple slope     = -0.7172(0.2292), z=-3.1293, p=0.0018
  At w1(2)...
    simple intercept = -1.1112(NaN), z=NaN, p=NaN            # <--- Here
    simple slope     = -1.2822(NaN), z=NaN, p=NaN            # <--- Here

Simple Intercepts and Slopes at Region Boundaries for w
========================================================
  Lower Bound...    
    simple intercept = -1.0752(0.3016), z=-3.5652, p=0.0004
    simple slope     = -0.6885(0.3512), z=-1.9607, p=0.0499
  Upper Bound...    
    simple intercept = -3.1922(5.9627), z=-0.5354, p=0.5924
    simple slope     = -35.5392(18.1305), z=-1.9602, p=0.05

Region of Significance on x (level-1 predictor)
========================================================
  x1 at lower bound of region = 0.0426
  x1 at upper bound of region = 29.4637
  (simple slopes are significant *inside* this region.)

Simple Intercepts and Slopes at Conditional Values of x
========================================================
  At x1(1)...
    simple intercept = -0.3597(0.9928), z=-0.3623, p=0.7171
    simple slope     = 0.5307(0.9476), z=0.56, p=0.5755
  At x1(2)...
    simple intercept = -1.7941(NaN), z=NaN, p=NaN
    simple slope     = -0.5993(NaN), z=NaN, p=NaN

Simple Intercepts and Slopes at Region Boundaries for x
========================================================
  Lower Bound...    
    simple intercept = -1.1075(NaN), z=NaN, p=NaN
    simple slope     = -0.0584(0.0301), z=-1.9404, p=0.0524
  Upper Bound...    
    simple intercept = -22.2095(4.3165), z=-5.1452, p=0
    simple slope     = -16.6807(8.5098), z=-1.9602, p=0.05

Points to Plot
=======================================================
  Line for w1(1):  From {x1=-1, Y=-0.3597} to {x1=1, Y=-1.7941}
  Line for w1(2):  From {x1=-1, Y=0.171} to {x1=1, Y=-2.3934}

I found this website(https://groups.google.com/g/lavaan/c/lyjF4JZAgBk) and set 100000 at df_int and df_slp. But the result of "simple intercepts and slopes at conditional values of w at w1(2)" didn't change.