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F1 = read($1, data_type="frame", format="csv", sep=",");

# Example Preprocessing:
F1[,4] = map(F1[,4], "x -> x.toLowerCase()");

# Example to estimate max tokens based on string length
# max_token = as.integer(max(as.matrix(map(F1[,4], "x -> x.length()"))));
# print(max_length);

max_token = 2000;

# Example spec:
# jspec = "{\"algo\": \"whitespace\",\"out\": \"count\",\"id_col\": [2],\"tokenize_col\": 3}";

jspec = read($2, data_type="scalar", value_type="string");

# If slicing is performed, then only the remaining columns are considered for tokenizer spec:
F2 = tokenize(target=F1[,2:4], spec=jspec, max_tokens=max_token);
write(F2, $3, format="csv");

# Followup spec to transform tokens
jspec2 = "{\"ids\": true, \"recode\": [1,2]}";

# Afterward, you can transform it into a matrix:
[X, M] = transformencode(target=F2, spec=jspec2);
# If format is long, then table could be applied afterwards
X = table(X[1,], X[2,], X[3,]);