A strategy to build iteratively tables for fitted models statistics with sjPlot

I'm facing with this fitted models list:

Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: response ~ CATEGORY + (1 | subj)
   Data: .

REML criterion at convergence: 402.7

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-1.92138 -0.48219  0.01134  0.39725  2.08276 

Random effects:
 Groups   Name        Variance Std.Dev.
 subj     (Intercept) 12.931   3.596   
 Residual              6.112   2.472   
Number of obs: 75, groups:  subj, 25

Fixed effects:
               Estimate Std. Error      df t value Pr(>|t|)  
(Intercept)     -1.6123     0.8794 34.0211  -1.833   0.0754 .
CATEGORYtim-B  0.2275     0.6792 48.0000   0.335   0.7393  
CATEGORYtim-C  0.1437     0.6792 48.0000   0.212   0.8330  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
                 (Intr) cndtnB
CATEGORYtim-B -0.381        
CATEGORYtim-C -0.381  0.511

and other 12 elements, all embedded into an object called model_list

If I would like to present them as into an elegant tables (as it is shown singularly for each model here in these slides) with sjPlot() package or others (alternatively):

Does anyone know what I should do?

It seems you are using summary of the models rather than the models themselves. Do:

models_list_3 <- out_long %>%   
       group_by(signals) %>%   
       do(fit = lmerTest::lmer(value ~ COND + (1|ID), data = .)) %>%    
       pull(fit) 

tab_model(model_list_3, show.ci =  FALSE, show.se =  TRUE)

for each model separately, you could do:

 lapply(model_list_3, tab_model, show.ci = FALSE, show.se = TRUE)