How to visualize Deep learning framework using Keras package with R?

Question

I have constructed an example of a DNN-based model using the following code in R.

library(keras)
library(caret)

data(iris)
iris_df <- iris
rm(iris)

iris_df$Species <- as.factor(iris_df$Species)


set.seed(42)
splitIndex <- createDataPartition(iris_df$Species, p = 0.8, list = FALSE)
train_data <- iris_df[splitIndex, ]
test_data <- iris_df[-splitIndex, ]


train_labels <- keras::to_categorical(as.integer(train_data$Species) - 1, num_classes = 3)
test_labels <- to_categorical(as.integer(test_data$Species) - 1, num_classes = 3)


model <- keras_model_sequential()

# Input Layer
model %>% 
  layer_dense(units = 3, activation = 'sigmoid', input_shape = ncol(train_data) - 1)

# Hidden Layers
for (i in 1:8) {
  units <- sample(20:60, 1)  # Select one random number from 20 to 60
  model %>%
    layer_dense(units = units, activation = 'tanh') %>%
    layer_dropout(rate = 0.5)  # Dropout layer to prevent overfitting
}

# Output Layer
model %>%
  layer_dense(units = 3, activation = 'softmax')

# Model Compile
model %>% compile(
  optimizer = optimizer_adam(),  # Adam optimizer
  loss = 'categorical_crossentropy',  # Categorical cross-entropy loss
  metrics = c('accuracy')
)

However, I keep encountering errors when attempting to plot the framework of the model. What could be the issue?

I would like to draw the diagram as follows: