How to get the significance of coeficients in logistic regression using `ml_logistic_regression`

I want to know the significance of each coefficient of a logistic regression model using spark function ml_logistic_regression. The code is as follows:

# data in R
library(MASS)
data(birthwt) 
str(birthwt)
detach("package:MASS", unload=TRUE)

# Connection to Spark
library(sparklyr)
library(dplyr)
sc = spark_connect(master = "local")

# copy the data to Spark
birth_sc = copy_to(sc, birthwt, "birth_sc", overwrite = TRUE)

# Model
# create dummy variables for race (race_1, race_2, race_3)
birth_sc = ml_create_dummy_variables(birth_sc, "race")
model = ml_logistic_regression(birth_sc, low ~ lwt + race_2 + race_3)

The model I get is the following:

> model
Call: low ~ lwt + race_2 + race_3

Coefficients:
(Intercept)         lwt      race_2      race_3 
 0.80575496 -0.01522311  1.08106617  0.48060322 

In an R model you use summary and it gives you the significance of the coefficients, but if I use it with this model I get the same result:

> summary(model)
Call: ml_logistic_regression(birth_sc, low ~ lwt + race_2 + race_3)

Coefficients:
  (Intercept)         lwt      race_2      race_3 
0.80575496 -0.01522311  1.08106617  0.48060322 

How could get the significance of each variable in the model?