#Define a scale function
scale_data = function(train, test, feature_range = c(-1, 1)) {
  x = train
  fr_min = feature_range[1]
  fr_max = feature_range[2]
  std_train = ((x - min(x) ) / (max(x) - min(x)  ))
  std_test  = ((test - min(x) ) / (max(x) - min(x)  ))
  scaled_train = std_train *(fr_max -fr_min) + fr_min
  scaled_test = std_test *(fr_max -fr_min) + fr_min
  return(list(scaled_train = as.vector(scaled_train), scaled_test = as.vector(scaled_test), scaler= c(min = min(x), max = max(x))))
Scaled = scale_data(kt_train_male, kt_test_male, c(0, 1))
kt_train_male_scaled = Scaled$scaled_train
kt_test_male_scaled = Scaled$scaled_test
#Define a invert scaling function
invert_scaling = function(scaled, scaler, feature_range = c(-1, 1)){
  min = scaler[1]
  max = scaler[2]
  t = length(scaled)
  mins = feature_range[1]
  maxs = feature_range[2]
  inverted_dfs = numeric(t)
   for(i in 1:t){
    X = (scaled[i]- mins)/(maxs - mins)
    rawValues = X *(max - min) + min
    inverted_dfs[i] <- rawValues
  return(inverted_dfs)
#Split training data in sliding window format
create_dataset = function(data, n_input, n_out){
        X <- matrix(nrow = 0, ncol = n_input)
        Y <- matrix(nrow = 0, ncol = n_out)
        in_start <- 0
        for (i in seq_along(data)) {
                #define the end of the input sequence
                in_end <- in_start + n_input
                out_end <- in_end + n_out
                        if(out_end <= length(data)){
                                X <- rbind(X, data[(in_start+1):in_end])
                                Y <- rbind(Y, data[(in_end+1):out_end])
                #move along one time step
                in_start = in_start + 1
   list(X, Y)
#24 months to predict the next 12 months
n_input = 24 
n_out = 12
train_sliding = create_dataset(data = kt_train_male_scaled, n_input = n_input, n_out = n_out)
X_train = train_sliding[[1]]
dim(X_train) #107 24
y_train = train_sliding[[2]]
dim(y_train) #107 12
#Array transformation to Keras LSTM
dim(X_train) = c(dim(X_train), 1)
dim(X_train) #107 24 1
#Model in Keras
batch_size = 1 
model <- keras_model_sequential() 
model%>%
  layer_lstm(units = 16, activation = "relu", batch_size = batch_size, input_shape = c(n_input, n_out), stateful= TRUE)%>%
  layer_dense(units = 1) %>%
summary(model)
model %>% compile(
  loss = 'mse',
  optimizer = optimizer_adam(lr= 0.02, decay = 1e-6 ),  
  metrics = c('mae')
When I try to run the chunck of code bellow I get the following error
Epochs = 100   
for(i in 1:Epochs ){
  model %>% fit(X_train, y_train, epochs=1, batch_size=batch_size, verbose=1, shuffle=FALSE)
  model %>% reset_states()
The code is not executed and this error is thrown
2021-04-18 18:57:30.767632: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)
Error in py_call_impl(callable, dots$args, dots$keywords) : 
  ValueError: in user code:
    /Users/my_user/Library/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py:805 train_function  *
        return step_function(self, iterator)
    /Users/my_user/Library/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py:795 step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    /Users/my_user/Library/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:1259 run
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    /Users/my_user/Library/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:2730 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    /Users/my_user/Library/r-mini
I uploaded my data to make it reproducible. Although the error happens before the test data is used I uploaded it for the sake of completeness.

kt_train_male.csv

kt_test_male.csv
        changed the title
Error in py_call_impl(callable, dots$args, dots$keywords) :    ValueError: in user code:
 I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)
      Apr 18, 2021
2021-04-18 18:57:30.767632: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)
This isn't an error but more like a warning. For this one I recommend reading this post from Stack Overflow.
The true error is this one:
Error in py_call_impl(callable, dots$args, dots$keywords) :

ValueError: in user code:

/Users/my_user/Library/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py:805 train_function  *

return step_function(self, iterator)

/Users/my_user/Library/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py:795 step_function  **

outputs = model.distribute_strategy.run(run_step, args=(data,))

/Users/my_user/Library/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:1259 run

return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)

/Users/my_user/Library/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:2730 call_for_each_replica

return self._call_for_each_replica(fn, args, kwargs)

/Users/my_user/Library/r-mini
I don't really know if the problem is how the model is built.