// Code generated by stanc v2.24.1
#include <stan/model/model_header.hpp>
namespace cmdstanr_test_model_namespace {


inline void validate_positive_index(const char* var_name, const char* expr,
                                    int val) {
  if (val < 1) {
    std::stringstream msg;
    msg << "Found dimension size less than one in simplex declaration"
        << "; variable=" << var_name << "; dimension size expression=" << expr
        << "; expression value=" << val;
    std::string msg_str(msg.str());
    throw std::invalid_argument(msg_str.c_str());
  }
}

inline void validate_unit_vector_index(const char* var_name, const char* expr,
                                       int val) {
  if (val <= 1) {
    std::stringstream msg;
    if (val == 1) {
      msg << "Found dimension size one in unit vector declaration."
          << " One-dimensional unit vector is discrete"
          << " but the target distribution must be continuous."
          << " variable=" << var_name << "; dimension size expression=" << expr;
    } else {
      msg << "Found dimension size less than one in unit vector declaration"
          << "; variable=" << var_name << "; dimension size expression=" << expr
          << "; expression value=" << val;
    }
    std::string msg_str(msg.str());
    throw std::invalid_argument(msg_str.c_str());
  }
}


using std::istream;
using std::string;
using std::stringstream;
using std::vector;
using std::pow;
using stan::io::dump;
using stan::math::lgamma;
using stan::model::model_base_crtp;
using stan::model::rvalue;
using stan::model::cons_list;
using stan::model::index_uni;
using stan::model::index_max;
using stan::model::index_min;
using stan::model::index_min_max;
using stan::model::index_multi;
using stan::model::index_omni;
using stan::model::nil_index_list;
using namespace stan::math;
using stan::math::pow; 

static int current_statement__ = 0;
static const std::vector<string> locations_array__ = {" (found before start of program)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 120, column 2 to column 26)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 121, column 2 to column 28)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 122, column 2 to column 30)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 123, column 2 to column 26)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 124, column 2 to column 26)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 125, column 2 to column 22)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 126, column 2 to column 28)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 127, column 2 to column 23)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 128, column 2 to column 32)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 131, column 2 to column 23)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 133, column 2 to column 26)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 134, column 2 to column 27)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 135, column 2 to column 28)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 136, column 2 to column 26)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 137, column 2 to column 26)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 139, column 2 to column 46)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 140, column 2 to column 25)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 141, column 2 to column 26)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 142, column 2 to column 27)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 143, column 2 to column 25)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 144, column 2 to column 25)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 202, column 2 to column 66)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 203, column 2 to column 39)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 207, column 6 to column 48)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 206, column 25 to line 208, column 5)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 206, column 4 to line 208, column 5)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 205, column 19 to line 209, column 3)",
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                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 148, column 9 to column 10)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 148, column 2 to column 42)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 150, column 9 to column 10)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 150, column 2 to column 45)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 152, column 9 to column 10)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 152, column 2 to column 48)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 154, column 9 to column 10)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 154, column 2 to column 42)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 156, column 9 to column 10)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 156, column 2 to column 42)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 158, column 9 to column 10)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 158, column 2 to column 15)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 161, column 4 to column 57)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 159, column 17 to line 162, column 3)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 159, column 2 to line 162, column 3)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 165, column 4 to column 60)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 163, column 17 to line 166, column 3)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 163, column 2 to line 166, column 3)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 169, column 4 to column 63)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 167, column 17 to line 170, column 3)",
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                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 171, column 2 to line 174, column 3)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 177, column 4 to column 57)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 175, column 17 to line 178, column 3)",
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                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 181, column 4 to column 276)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 179, column 17 to line 182, column 3)",
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                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 184, column 2 to column 43)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 185, column 2 to column 46)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 186, column 2 to column 44)",
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                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 191, column 2 to line 192, column 35)",
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                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 196, column 19 to line 198, column 3)",
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                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 73, column 2 to column 17)",
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                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 74, column 2 to column 14)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 75, column 2 to column 23)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 76, column 9 to column 10)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 76, column 12 to column 19)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 76, column 2 to column 29)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 77, column 2 to column 24)",
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                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 78, column 12 to column 20)",
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                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 89, column 2 to column 16)",
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                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 102, column 9 to column 10)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 102, column 2 to column 17)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 103, column 11 to column 12)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 103, column 2 to column 14)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 105, column 2 to column 19)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 106, column 2 to column 19)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 107, column 19 to column 20)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 107, column 2 to column 22)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 109, column 9 to column 10)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 109, column 2 to column 24)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 110, column 9 to column 10)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 110, column 2 to column 25)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 111, column 9 to column 10)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 111, column 2 to column 26)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 112, column 9 to column 10)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 112, column 2 to column 24)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 113, column 9 to column 10)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 113, column 2 to column 24)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 114, column 2 to column 20)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 115, column 2 to column 17)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 120, column 9 to column 16)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 121, column 9 to column 17)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 122, column 9 to column 18)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 123, column 9 to column 16)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 124, column 9 to column 16)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 126, column 18 to column 21)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 127, column 9 to column 12)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 127, column 14 to column 17)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 128, column 23 to column 26)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 131, column 9 to column 12)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 131, column 14 to column 17)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 133, column 9 to column 12)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 134, column 9 to column 12)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 135, column 9 to column 12)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 136, column 9 to column 12)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 137, column 9 to column 12)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 202, column 14 to column 17)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 203, column 27 to column 31)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 16, column 4 to column 12)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 17, column 4 to column 12)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 18, column 4 to column 12)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 19, column 4 to column 12)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 20, column 4 to column 12)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 21, column 4 to column 13)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 22, column 4 to column 14)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 24, column 4 to column 12)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 25, column 4 to column 12)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 26, column 4 to column 12)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 27, column 4 to column 12)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 28, column 4 to column 14)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 30, column 4 to column 20)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 31, column 4 to column 22)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 32, column 4 to column 24)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 33, column 4 to column 20)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 34, column 4 to column 20)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 36, column 4 to column 18)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 37, column 4 to column 19)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 39, column 4 to column 69)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 40, column 4 to column 69)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 42, column 4 to column 41)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 43, column 4 to column 41)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 45, column 4 to column 19)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 47, column 4 to line 68, column 74)",
                                                      " (in '/home/granville/Repositories/Multilevel_TCM/R/Sim_data_model/Rok_Testing/cmdstan_test/cmdstanr_test.stan', line 13, column 36 to line 69, column 1)"};


template <typename T0__, typename T1__, typename T2__, typename T3__,
typename T4__, typename T5__, typename T6__, typename T7__, typename T8__,
typename T9__, typename T10__, typename T11__, typename T12__,
typename T13__, typename T14__, typename T15__, typename T16__,
typename T17__, typename T18__, typename T19__, typename T20__,
typename T21__>
stan::promote_args_t<T0__, T1__, T2__, T3__, T4__, stan::promote_args_t<T5__,
T6__, T7__, T8__, T9__, stan::promote_args_t<T10__, T11__, T12__, T13__,
T14__, stan::promote_args_t<T15__, T16__, T17__, T18__,
T19__, stan::promote_args_t<T20__,
T21__>>>>>
twotcm_log_stan(const T0__& logk1, const T1__& logvnd, const T2__& logbpnd,
                const T3__& logk4, const T4__& logvb, const T5__& MidTime,
                const T6__& b_pfc, const T7__& lambda1_pfc,
                const T8__& lambda2_pfc, const T9__& lambda3_pfc,
                const T10__& A1_pfc, const T11__& A2_pfc,
                const T12__& A3_pfc, const T13__& tstar_pfc,
                const T14__& b_tot, const T15__& lambda1_tot,
                const T16__& lambda2_tot, const T17__& lambda3_tot,
                const T18__& A1_tot, const T19__& A2_tot,
                const T20__& A3_tot, const T21__& tstar_tot,
                const int& indicator, std::ostream* pstream__) {
  using local_scalar_t__ = stan::promote_args_t<T0__,
          T1__,
          T2__,
          T3__,
          T4__, stan::promote_args_t<T5__,
          T6__,
          T7__,
          T8__,
          T9__, stan::promote_args_t<T10__,
          T11__,
          T12__,
          T13__,
          T14__, stan::promote_args_t<T15__,
          T16__,
          T17__,
          T18__,
          T19__, stan::promote_args_t<T20__,
          T21__>>>>>;
  const static bool propto__ = true;
  (void) propto__;
  local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
  (void) DUMMY_VAR__;  // suppress unused var warning
  
  try {
    local_scalar_t__ k1;
    k1 = DUMMY_VAR__;
    
    local_scalar_t__ k2;
    k2 = DUMMY_VAR__;
    
    local_scalar_t__ k3;
    k3 = DUMMY_VAR__;
    
    local_scalar_t__ k4;
    k4 = DUMMY_VAR__;
    
    local_scalar_t__ vb;
    vb = DUMMY_VAR__;
    
    local_scalar_t__ vnd;
    vnd = DUMMY_VAR__;
    
    local_scalar_t__ bpnd;
    bpnd = DUMMY_VAR__;
    
    local_scalar_t__ R1;
    R1 = DUMMY_VAR__;
    
    local_scalar_t__ R2;
    R2 = DUMMY_VAR__;
    
    local_scalar_t__ L1;
    L1 = DUMMY_VAR__;
    
    local_scalar_t__ L2;
    L2 = DUMMY_VAR__;
    
    local_scalar_t__ time;
    time = DUMMY_VAR__;
    
    current_statement__ = 176;
    k1 = stan::math::exp(logk1);
    current_statement__ = 177;
    vnd = stan::math::exp(logvnd);
    current_statement__ = 178;
    bpnd = stan::math::exp(logbpnd);
    current_statement__ = 179;
    k4 = stan::math::exp(logk4);
    current_statement__ = 180;
    vb = stan::math::exp(logvb);
    current_statement__ = 181;
    k2 = (k1 / vnd);
    current_statement__ = 182;
    k3 = (bpnd * k4);
    current_statement__ = 183;
    R1 = (0.5 *
           (((k2 + k3) + k4) +
             stan::math::sqrt((pow(((k2 + k3) + k4), 2) - ((4 * k2) * k4)))));
    current_statement__ = 184;
    R2 = (0.5 *
           (((k2 + k3) + k4) -
             stan::math::sqrt((pow(((k2 + k3) + k4), 2) - ((4 * k2) * k4)))));
    current_statement__ = 185;
    L1 = ((k1 * ((R1 - k3) - k4)) / (R1 - R2));
    current_statement__ = 186;
    L2 = ((k1 * ((k3 + k4) - R2)) / (R1 - R2));
    current_statement__ = 187;
    time = MidTime;
    current_statement__ = 188;
    return (((1 - vb) *
              ((indicator *
                 (((b_pfc * L1) *
                    (((stan::math::exp((-R1 * time)) / pow(R1, 2)) +
                       (time / R1)) - (1 / pow(R1, 2)))) +
                   ((b_pfc * L2) *
                     (((stan::math::exp((-R2 * time)) / pow(R2, 2)) +
                        (time / R2)) - (1 / pow(R2, 2)))))) +
                ((-1 * (indicator - 1)) *
                  ((((((((((b_pfc * L1) * stan::math::exp((-R1 * time))) *
                           ((((tstar_pfc / R1) *
                               stan::math::exp((R1 * tstar_pfc))) -
                              ((1 / pow(R1, 2)) *
                                stan::math::exp((R1 * tstar_pfc)))) +
                             (1 / pow(R1, 2)))) +
                          ((L1 * A1_pfc) *
                            ((stan::math::exp((-lambda1_pfc * time)) /
                               (R1 - lambda1_pfc)) -
                              (stan::math::exp(
                                 (((R1 * tstar_pfc) - (R1 * time)) -
                                   (lambda1_pfc * tstar_pfc))) /
                                (R1 - lambda1_pfc))))) +
                         ((L1 * A2_pfc) *
                           ((stan::math::exp((-lambda2_pfc * time)) /
                              (R1 - lambda2_pfc)) -
                             (stan::math::exp(
                                (((R1 * tstar_pfc) - (R1 * time)) -
                                  (lambda2_pfc * tstar_pfc))) /
                               (R1 - lambda2_pfc))))) +
                        ((L1 * A3_pfc) *
                          ((stan::math::exp((-lambda3_pfc * time)) /
                             (R1 - lambda3_pfc)) -
                            (stan::math::exp(
                               (((R1 * tstar_pfc) - (R1 * time)) -
                                 (lambda3_pfc * tstar_pfc))) /
                              (R1 - lambda3_pfc))))) +
                       (((b_pfc * L2) * stan::math::exp((-R2 * time))) *
                         ((((tstar_pfc / R2) *
                             stan::math::exp((R2 * tstar_pfc))) -
                            ((1 / pow(R2, 2)) *
                              stan::math::exp((R2 * tstar_pfc)))) +
                           (1 / pow(R2, 2))))) +
                      ((L2 * A1_pfc) *
                        ((stan::math::exp((-lambda1_pfc * time)) /
                           (R2 - lambda1_pfc)) -
                          (stan::math::exp(
                             (((R2 * tstar_pfc) - (R2 * time)) -
                               (lambda1_pfc * tstar_pfc))) /
                            (R2 - lambda1_pfc))))) +
                     ((L2 * A2_pfc) *
                       ((stan::math::exp((-lambda2_pfc * time)) /
                          (R2 - lambda2_pfc)) -
                         (stan::math::exp(
                            (((R2 * tstar_pfc) - (R2 * time)) -
                              (lambda2_pfc * tstar_pfc))) /
                           (R2 - lambda2_pfc))))) +
                    ((L2 * A3_pfc) *
                      ((stan::math::exp((-lambda3_pfc * time)) /
                         (R2 - lambda3_pfc)) -
                        (stan::math::exp(
                           (((R2 * tstar_pfc) - (R2 * time)) -
                             (lambda3_pfc * tstar_pfc))) /
                          (R2 - lambda3_pfc)))))))) +
             (vb *
               (((indicator * b_tot) * time) +
                 ((-1 * (indicator - 1)) *
                   (((A1_tot * stan::math::exp((-lambda1_tot * time))) +
                      (A2_tot * stan::math::exp((-lambda2_tot * time)))) +
                     (A3_tot * stan::math::exp((-lambda3_tot * time))))))));
  } catch (const std::exception& e) {
    stan::lang::rethrow_located(e, locations_array__[current_statement__]);
      // Next line prevents compiler griping about no return
      throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***"); 
  }
  
}

struct twotcm_log_stan_functor__ {
template <typename T0__, typename T1__, typename T2__, typename T3__,
typename T4__, typename T5__, typename T6__, typename T7__, typename T8__,
typename T9__, typename T10__, typename T11__, typename T12__,
typename T13__, typename T14__, typename T15__, typename T16__,
typename T17__, typename T18__, typename T19__, typename T20__,
typename T21__>
stan::promote_args_t<T0__, T1__, T2__, T3__, T4__, stan::promote_args_t<T5__,
T6__, T7__, T8__, T9__, stan::promote_args_t<T10__, T11__, T12__, T13__,
T14__, stan::promote_args_t<T15__, T16__, T17__, T18__,
T19__, stan::promote_args_t<T20__,
T21__>>>>>
operator()(const T0__& logk1, const T1__& logvnd, const T2__& logbpnd,
           const T3__& logk4, const T4__& logvb, const T5__& MidTime,
           const T6__& b_pfc, const T7__& lambda1_pfc,
           const T8__& lambda2_pfc, const T9__& lambda3_pfc,
           const T10__& A1_pfc, const T11__& A2_pfc, const T12__& A3_pfc,
           const T13__& tstar_pfc, const T14__& b_tot,
           const T15__& lambda1_tot, const T16__& lambda2_tot,
           const T17__& lambda3_tot, const T18__& A1_tot,
           const T19__& A2_tot, const T20__& A3_tot, const T21__& tstar_tot,
           const int& indicator, std::ostream* pstream__)  const 
{
return twotcm_log_stan(logk1, logvnd, logbpnd, logk4, logvb, MidTime, b_pfc,
         lambda1_pfc, lambda2_pfc, lambda3_pfc, A1_pfc, A2_pfc, A3_pfc,
         tstar_pfc, b_tot, lambda1_tot, lambda2_tot, lambda3_tot, A1_tot,
         A2_tot, A3_tot, tstar_tot, indicator, pstream__);
}
};

class cmdstanr_test_model final : public model_base_crtp<cmdstanr_test_model> {

 private:
  int N;
  Eigen::Matrix<double, -1, 1> Y;
  int K_logk1;
  Eigen::Matrix<double, -1, -1> X_logk1;
  int K_logvnd;
  Eigen::Matrix<double, -1, -1> X_logvnd;
  int K_logbpnd;
  Eigen::Matrix<double, -1, -1> X_logbpnd;
  int K_logk4;
  Eigen::Matrix<double, -1, -1> X_logk4;
  int K_logvb;
  Eigen::Matrix<double, -1, -1> X_logvb;
  Eigen::Matrix<double, -1, 1> C_1;
  Eigen::Matrix<double, -1, 1> C_2;
  Eigen::Matrix<double, -1, 1> C_3;
  Eigen::Matrix<double, -1, 1> C_4;
  Eigen::Matrix<double, -1, 1> C_5;
  Eigen::Matrix<double, -1, 1> C_6;
  Eigen::Matrix<double, -1, 1> C_7;
  Eigen::Matrix<double, -1, 1> C_8;
  Eigen::Matrix<double, -1, 1> C_9;
  Eigen::Matrix<double, -1, 1> C_10;
  Eigen::Matrix<double, -1, 1> C_11;
  Eigen::Matrix<double, -1, 1> C_12;
  Eigen::Matrix<double, -1, 1> C_13;
  Eigen::Matrix<double, -1, 1> C_14;
  Eigen::Matrix<double, -1, 1> C_15;
  Eigen::Matrix<double, -1, 1> C_16;
  Eigen::Matrix<double, -1, 1> C_17;
  std::vector<int> C_18;
  int N_1;
  int M_1;
  std::vector<int> J_1;
  Eigen::Matrix<double, -1, 1> Z_1_logk1_1;
  Eigen::Matrix<double, -1, 1> Z_1_logvnd_2;
  Eigen::Matrix<double, -1, 1> Z_1_logbpnd_3;
  Eigen::Matrix<double, -1, 1> Z_1_logk4_4;
  Eigen::Matrix<double, -1, 1> Z_1_logvb_5;
  int NC_1;
  int prior_only;
 
 public:
  ~cmdstanr_test_model() final { }
  
  std::string model_name() const final { return "cmdstanr_test_model"; }

  std::vector<std::string> model_compile_info() const {
    std::vector<std::string> stanc_info;
    stanc_info.push_back("stanc_version = stanc3 v2.24.1");
    stanc_info.push_back("stancflags = ");
    return stanc_info;
  }
  
  
  cmdstanr_test_model(stan::io::var_context& context__,
                      unsigned int random_seed__ = 0,
                      std::ostream* pstream__ = nullptr) : model_base_crtp(0) {
    using local_scalar_t__ = double ;
    boost::ecuyer1988 base_rng__ = 
        stan::services::util::create_rng(random_seed__, 0);
    (void) base_rng__;  // suppress unused var warning
    static const char* function__ = "cmdstanr_test_model_namespace::cmdstanr_test_model";
    (void) function__;  // suppress unused var warning
    local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
    (void) DUMMY_VAR__;  // suppress unused var warning
    
    try {
      int pos__;
      pos__ = std::numeric_limits<int>::min();
      
      pos__ = 1;
      current_statement__ = 71;
      context__.validate_dims("data initialization","N","int",
          context__.to_vec());
      N = std::numeric_limits<int>::min();
      
      current_statement__ = 71;
      N = context__.vals_i("N")[(1 - 1)];
      current_statement__ = 71;
      current_statement__ = 71;
      check_greater_or_equal(function__, "N", N, 1);
      current_statement__ = 72;
      validate_non_negative_index("Y", "N", N);
      current_statement__ = 73;
      context__.validate_dims("data initialization","Y","double",
          context__.to_vec(N));
      Y = Eigen::Matrix<double, -1, 1>(N);
      stan::math::fill(Y, std::numeric_limits<double>::quiet_NaN());
      
      {
        std::vector<local_scalar_t__> Y_flat__;
        current_statement__ = 73;
        assign(Y_flat__, nil_index_list(), context__.vals_r("Y"),
          "assigning variable Y_flat__");
        current_statement__ = 73;
        pos__ = 1;
        current_statement__ = 73;
        for (int sym1__ = 1; sym1__ <= N; ++sym1__) {
          current_statement__ = 73;
          assign(Y, cons_list(index_uni(sym1__), nil_index_list()),
            Y_flat__[(pos__ - 1)], "assigning variable Y");
          current_statement__ = 73;
          pos__ = (pos__ + 1);}
      }
      current_statement__ = 74;
      context__.validate_dims("data initialization","K_logk1","int",
          context__.to_vec());
      K_logk1 = std::numeric_limits<int>::min();
      
      current_statement__ = 74;
      K_logk1 = context__.vals_i("K_logk1")[(1 - 1)];
      current_statement__ = 74;
      current_statement__ = 74;
      check_greater_or_equal(function__, "K_logk1", K_logk1, 1);
      current_statement__ = 75;
      validate_non_negative_index("X_logk1", "N", N);
      current_statement__ = 76;
      validate_non_negative_index("X_logk1", "K_logk1", K_logk1);
      current_statement__ = 77;
      context__.validate_dims("data initialization","X_logk1","double",
          context__.to_vec(N, K_logk1));
      X_logk1 = Eigen::Matrix<double, -1, -1>(N, K_logk1);
      stan::math::fill(X_logk1, std::numeric_limits<double>::quiet_NaN());
      
      {
        std::vector<local_scalar_t__> X_logk1_flat__;
        current_statement__ = 77;
        assign(X_logk1_flat__, nil_index_list(), context__.vals_r("X_logk1"),
          "assigning variable X_logk1_flat__");
        current_statement__ = 77;
        pos__ = 1;
        current_statement__ = 77;
        for (int sym1__ = 1; sym1__ <= K_logk1; ++sym1__) {
          current_statement__ = 77;
          for (int sym2__ = 1; sym2__ <= N; ++sym2__) {
            current_statement__ = 77;
            assign(X_logk1,
              cons_list(index_uni(sym2__),
                cons_list(index_uni(sym1__), nil_index_list())),
              X_logk1_flat__[(pos__ - 1)], "assigning variable X_logk1");
            current_statement__ = 77;
            pos__ = (pos__ + 1);}}
      }
      current_statement__ = 78;
      context__.validate_dims("data initialization","K_logvnd","int",
          context__.to_vec());
      K_logvnd = std::numeric_limits<int>::min();
      
      current_statement__ = 78;
      K_logvnd = context__.vals_i("K_logvnd")[(1 - 1)];
      current_statement__ = 78;
      current_statement__ = 78;
      check_greater_or_equal(function__, "K_logvnd", K_logvnd, 1);
      current_statement__ = 79;
      validate_non_negative_index("X_logvnd", "N", N);
      current_statement__ = 80;
      validate_non_negative_index("X_logvnd", "K_logvnd", K_logvnd);
      current_statement__ = 81;
      context__.validate_dims("data initialization","X_logvnd","double",
          context__.to_vec(N, K_logvnd));
      X_logvnd = Eigen::Matrix<double, -1, -1>(N, K_logvnd);
      stan::math::fill(X_logvnd, std::numeric_limits<double>::quiet_NaN());
      
      {
        std::vector<local_scalar_t__> X_logvnd_flat__;
        current_statement__ = 81;
        assign(X_logvnd_flat__, nil_index_list(),
          context__.vals_r("X_logvnd"), "assigning variable X_logvnd_flat__");
        current_statement__ = 81;
        pos__ = 1;
        current_statement__ = 81;
        for (int sym1__ = 1; sym1__ <= K_logvnd; ++sym1__) {
          current_statement__ = 81;
          for (int sym2__ = 1; sym2__ <= N; ++sym2__) {
            current_statement__ = 81;
            assign(X_logvnd,
              cons_list(index_uni(sym2__),
                cons_list(index_uni(sym1__), nil_index_list())),
              X_logvnd_flat__[(pos__ - 1)], "assigning variable X_logvnd");
            current_statement__ = 81;
            pos__ = (pos__ + 1);}}
      }
      current_statement__ = 82;
      context__.validate_dims("data initialization","K_logbpnd","int",
          context__.to_vec());
      K_logbpnd = std::numeric_limits<int>::min();
      
      current_statement__ = 82;
      K_logbpnd = context__.vals_i("K_logbpnd")[(1 - 1)];
      current_statement__ = 82;
      current_statement__ = 82;
      check_greater_or_equal(function__, "K_logbpnd", K_logbpnd, 1);
      current_statement__ = 83;
      validate_non_negative_index("X_logbpnd", "N", N);
      current_statement__ = 84;
      validate_non_negative_index("X_logbpnd", "K_logbpnd", K_logbpnd);
      current_statement__ = 85;
      context__.validate_dims("data initialization","X_logbpnd","double",
          context__.to_vec(N, K_logbpnd));
      X_logbpnd = Eigen::Matrix<double, -1, -1>(N, K_logbpnd);
      stan::math::fill(X_logbpnd, std::numeric_limits<double>::quiet_NaN());
      
      {
        std::vector<local_scalar_t__> X_logbpnd_flat__;
        current_statement__ = 85;
        assign(X_logbpnd_flat__, nil_index_list(),
          context__.vals_r("X_logbpnd"),
          "assigning variable X_logbpnd_flat__");
        current_statement__ = 85;
        pos__ = 1;
        current_statement__ = 85;
        for (int sym1__ = 1; sym1__ <= K_logbpnd; ++sym1__) {
          current_statement__ = 85;
          for (int sym2__ = 1; sym2__ <= N; ++sym2__) {
            current_statement__ = 85;
            assign(X_logbpnd,
              cons_list(index_uni(sym2__),
                cons_list(index_uni(sym1__), nil_index_list())),
              X_logbpnd_flat__[(pos__ - 1)], "assigning variable X_logbpnd");
            current_statement__ = 85;
            pos__ = (pos__ + 1);}}
      }
      current_statement__ = 86;
      context__.validate_dims("data initialization","K_logk4","int",
          context__.to_vec());
      K_logk4 = std::numeric_limits<int>::min();
      
      current_statement__ = 86;
      K_logk4 = context__.vals_i("K_logk4")[(1 - 1)];
      current_statement__ = 86;
      current_statement__ = 86;
      check_greater_or_equal(function__, "K_logk4", K_logk4, 1);
      current_statement__ = 87;
      validate_non_negative_index("X_logk4", "N", N);
      current_statement__ = 88;
      validate_non_negative_index("X_logk4", "K_logk4", K_logk4);
      current_statement__ = 89;
      context__.validate_dims("data initialization","X_logk4","double",
          context__.to_vec(N, K_logk4));
      X_logk4 = Eigen::Matrix<double, -1, -1>(N, K_logk4);
      stan::math::fill(X_logk4, std::numeric_limits<double>::quiet_NaN());
      
      {
        std::vector<local_scalar_t__> X_logk4_flat__;
        current_statement__ = 89;
        assign(X_logk4_flat__, nil_index_list(), context__.vals_r("X_logk4"),
          "assigning variable X_logk4_flat__");
        current_statement__ = 89;
        pos__ = 1;
        current_statement__ = 89;
        for (int sym1__ = 1; sym1__ <= K_logk4; ++sym1__) {
          current_statement__ = 89;
          for (int sym2__ = 1; sym2__ <= N; ++sym2__) {
            current_statement__ = 89;
            assign(X_logk4,
              cons_list(index_uni(sym2__),
                cons_list(index_uni(sym1__), nil_index_list())),
              X_logk4_flat__[(pos__ - 1)], "assigning variable X_logk4");
            current_statement__ = 89;
            pos__ = (pos__ + 1);}}
      }
      current_statement__ = 90;
      context__.validate_dims("data initialization","K_logvb","int",
          context__.to_vec());
      K_logvb = std::numeric_limits<int>::min();
      
      current_statement__ = 90;
      K_logvb = context__.vals_i("K_logvb")[(1 - 1)];
      current_statement__ = 90;
      current_statement__ = 90;
      check_greater_or_equal(function__, "K_logvb", K_logvb, 1);
      current_statement__ = 91;
      validate_non_negative_index("X_logvb", "N", N);
      current_statement__ = 92;
      validate_non_negative_index("X_logvb", "K_logvb", K_logvb);
      current_statement__ = 93;
      context__.validate_dims("data initialization","X_logvb","double",
          context__.to_vec(N, K_logvb));
      X_logvb = Eigen::Matrix<double, -1, -1>(N, K_logvb);
      stan::math::fill(X_logvb, std::numeric_limits<double>::quiet_NaN());
      
      {
        std::vector<local_scalar_t__> X_logvb_flat__;
        current_statement__ = 93;
        assign(X_logvb_flat__, nil_index_list(), context__.vals_r("X_logvb"),
          "assigning variable X_logvb_flat__");
        current_statement__ = 93;
        pos__ = 1;
        current_statement__ = 93;
        for (int sym1__ = 1; sym1__ <= K_logvb; ++sym1__) {
          current_statement__ = 93;
          for (int sym2__ = 1; sym2__ <= N; ++sym2__) {
            current_statement__ = 93;
            assign(X_logvb,
              cons_list(index_uni(sym2__),
                cons_list(index_uni(sym1__), nil_index_list())),
              X_logvb_flat__[(pos__ - 1)], "assigning variable X_logvb");
            current_statement__ = 93;
            pos__ = (pos__ + 1);}}
      }
      current_statement__ = 94;
      validate_non_negative_index("C_1", "N", N);
      current_statement__ = 95;
      context__.validate_dims("data initialization","C_1","double",
          context__.to_vec(N));
      C_1 = Eigen::Matrix<double, -1, 1>(N);
      stan::math::fill(C_1, std::numeric_limits<double>::quiet_NaN());
      
      {
        std::vector<local_scalar_t__> C_1_flat__;
        current_statement__ = 95;
        assign(C_1_flat__, nil_index_list(), context__.vals_r("C_1"),
          "assigning variable C_1_flat__");
        current_statement__ = 95;
        pos__ = 1;
        current_statement__ = 95;
        for (int sym1__ = 1; sym1__ <= N; ++sym1__) {
          current_statement__ = 95;
          assign(C_1, cons_list(index_uni(sym1__), nil_index_list()),
            C_1_flat__[(pos__ - 1)], "assigning variable C_1");
          current_statement__ = 95;
          pos__ = (pos__ + 1);}
      }
      current_statement__ = 96;
      validate_non_negative_index("C_2", "N", N);
      current_statement__ = 97;
      context__.validate_dims("data initialization","C_2","double",
          context__.to_vec(N));
      C_2 = Eigen::Matrix<double, -1, 1>(N);
      stan::math::fill(C_2, std::numeric_limits<double>::quiet_NaN());
      
      {
        std::vector<local_scalar_t__> C_2_flat__;
        current_statement__ = 97;
        assign(C_2_flat__, nil_index_list(), context__.vals_r("C_2"),
          "assigning variable C_2_flat__");
        current_statement__ = 97;
        pos__ = 1;
        current_statement__ = 97;
        for (int sym1__ = 1; sym1__ <= N; ++sym1__) {
          current_statement__ = 97;
          assign(C_2, cons_list(index_uni(sym1__), nil_index_list()),
            C_2_flat__[(pos__ - 1)], "assigning variable C_2");
          current_statement__ = 97;
          pos__ = (pos__ + 1);}
      }
      current_statement__ = 98;
      validate_non_negative_index("C_3", "N", N);
      current_statement__ = 99;
      context__.validate_dims("data initialization","C_3","double",
          context__.to_vec(N));
      C_3 = Eigen::Matrix<double, -1, 1>(N);
      stan::math::fill(C_3, std::numeric_limits<double>::quiet_NaN());
      
      {
        std::vector<local_scalar_t__> C_3_flat__;
        current_statement__ = 99;
        assign(C_3_flat__, nil_index_list(), context__.vals_r("C_3"),
          "assigning variable C_3_flat__");
        current_statement__ = 99;
        pos__ = 1;
        current_statement__ = 99;
        for (int sym1__ = 1; sym1__ <= N; ++sym1__) {
          current_statement__ = 99;
          assign(C_3, cons_list(index_uni(sym1__), nil_index_list()),
            C_3_flat__[(pos__ - 1)], "assigning variable C_3");
          current_statement__ = 99;
          pos__ = (pos__ + 1);}
      }
      current_statement__ = 100;
      validate_non_negative_index("C_4", "N", N);
      current_statement__ = 101;
      context__.validate_dims("data initialization","C_4","double",
          context__.to_vec(N));
      C_4 = Eigen::Matrix<double, -1, 1>(N);
      stan::math::fill(C_4, std::numeric_limits<double>::quiet_NaN());
      
      {
        std::vector<local_scalar_t__> C_4_flat__;
        current_statement__ = 101;
        assign(C_4_flat__, nil_index_list(), context__.vals_r("C_4"),
          "assigning variable C_4_flat__");
        current_statement__ = 101;
        pos__ = 1;
        current_statement__ = 101;
        for (int sym1__ = 1; sym1__ <= N; ++sym1__) {
          current_statement__ = 101;
          assign(C_4, cons_list(index_uni(sym1__), nil_index_list()),
            C_4_flat__[(pos__ - 1)], "assigning variable C_4");
          current_statement__ = 101;
          pos__ = (pos__ + 1);}
      }
      current_statement__ = 102;
      validate_non_negative_index("C_5", "N", N);
      current_statement__ = 103;
      context__.validate_dims("data initialization","C_5","double",
          context__.to_vec(N));
      C_5 = Eigen::Matrix<double, -1, 1>(N);
      stan::math::fill(C_5, std::numeric_limits<double>::quiet_NaN());
      
      {
        std::vector<local_scalar_t__> C_5_flat__;
        current_statement__ = 103;
        assign(C_5_flat__, nil_index_list(), context__.vals_r("C_5"),
          "assigning variable C_5_flat__");
        current_statement__ = 103;
        pos__ = 1;
        current_statement__ = 103;
        for (int sym1__ = 1; sym1__ <= N; ++sym1__) {
          current_statement__ = 103;
          assign(C_5, cons_list(index_uni(sym1__), nil_index_list()),
            C_5_flat__[(pos__ - 1)], "assigning variable C_5");
          current_statement__ = 103;
          pos__ = (pos__ + 1);}
      }
      current_statement__ = 104;
      validate_non_negative_index("C_6", "N", N);
      current_statement__ = 105;
      context__.validate_dims("data initialization","C_6","double",
          context__.to_vec(N));
      C_6 = Eigen::Matrix<double, -1, 1>(N);
      stan::math::fill(C_6, std::numeric_limits<double>::quiet_NaN());
      
      {
        std::vector<local_scalar_t__> C_6_flat__;
        current_statement__ = 105;
        assign(C_6_flat__, nil_index_list(), context__.vals_r("C_6"),
          "assigning variable C_6_flat__");
        current_statement__ = 105;
        pos__ = 1;
        current_statement__ = 105;
        for (int sym1__ = 1; sym1__ <= N; ++sym1__) {
          current_statement__ = 105;
          assign(C_6, cons_list(index_uni(sym1__), nil_index_list()),
            C_6_flat__[(pos__ - 1)], "assigning variable C_6");
          current_statement__ = 105;
          pos__ = (pos__ + 1);}
      }
      current_statement__ = 106;
      validate_non_negative_index("C_7", "N", N);
      current_statement__ = 107;
      context__.validate_dims("data initialization","C_7","double",
          context__.to_vec(N));
      C_7 = Eigen::Matrix<double, -1, 1>(N);
      stan::math::fill(C_7, std::numeric_limits<double>::quiet_NaN());
      
      {
        std::vector<local_scalar_t__> C_7_flat__;
        current_statement__ = 107;
        assign(C_7_flat__, nil_index_list(), context__.vals_r("C_7"),
          "assigning variable C_7_flat__");
        current_statement__ = 107;
        pos__ = 1;
        current_statement__ = 107;
        for (int sym1__ = 1; sym1__ <= N; ++sym1__) {
          current_statement__ = 107;
          assign(C_7, cons_list(index_uni(sym1__), nil_index_list()),
            C_7_flat__[(pos__ - 1)], "assigning variable C_7");
          current_statement__ = 107;
          pos__ = (pos__ + 1);}
      }
      current_statement__ = 108;
      validate_non_negative_index("C_8", "N", N);
      current_statement__ = 109;
      context__.validate_dims("data initialization","C_8","double",
          context__.to_vec(N));
      C_8 = Eigen::Matrix<double, -1, 1>(N);
      stan::math::fill(C_8, std::numeric_limits<double>::quiet_NaN());
      
      {
        std::vector<local_scalar_t__> C_8_flat__;
        current_statement__ = 109;
        assign(C_8_flat__, nil_index_list(), context__.vals_r("C_8"),
          "assigning variable C_8_flat__");
        current_statement__ = 109;
        pos__ = 1;
        current_statement__ = 109;
        for (int sym1__ = 1; sym1__ <= N; ++sym1__) {
          current_statement__ = 109;
          assign(C_8, cons_list(index_uni(sym1__), nil_index_list()),
            C_8_flat__[(pos__ - 1)], "assigning variable C_8");
          current_statement__ = 109;
          pos__ = (pos__ + 1);}
      }
      current_statement__ = 110;
      validate_non_negative_index("C_9", "N", N);
      current_statement__ = 111;
      context__.validate_dims("data initialization","C_9","double",
          context__.to_vec(N));
      C_9 = Eigen::Matrix<double, -1, 1>(N);
      stan::math::fill(C_9, std::numeric_limits<double>::quiet_NaN());
      
      {
        std::vector<local_scalar_t__> C_9_flat__;
        current_statement__ = 111;
        assign(C_9_flat__, nil_index_list(), context__.vals_r("C_9"),
          "assigning variable C_9_flat__");
        current_statement__ = 111;
        pos__ = 1;
        current_statement__ = 111;
        for (int sym1__ = 1; sym1__ <= N; ++sym1__) {
          current_statement__ = 111;
          assign(C_9, cons_list(index_uni(sym1__), nil_index_list()),
            C_9_flat__[(pos__ - 1)], "assigning variable C_9");
          current_statement__ = 111;
          pos__ = (pos__ + 1);}
      }
      current_statement__ = 112;
      validate_non_negative_index("C_10", "N", N);
      current_statement__ = 113;
      context__.validate_dims("data initialization","C_10","double",
          context__.to_vec(N));
      C_10 = Eigen::Matrix<double, -1, 1>(N);
      stan::math::fill(C_10, std::numeric_limits<double>::quiet_NaN());
      
      {
        std::vector<local_scalar_t__> C_10_flat__;
        current_statement__ = 113;
        assign(C_10_flat__, nil_index_list(), context__.vals_r("C_10"),
          "assigning variable C_10_flat__");
        current_statement__ = 113;
        pos__ = 1;
        current_statement__ = 113;
        for (int sym1__ = 1; sym1__ <= N; ++sym1__) {
          current_statement__ = 113;
          assign(C_10, cons_list(index_uni(sym1__), nil_index_list()),
            C_10_flat__[(pos__ - 1)], "assigning variable C_10");
          current_statement__ = 113;
          pos__ = (pos__ + 1);}
      }
      current_statement__ = 114;
      validate_non_negative_index("C_11", "N", N);
      current_statement__ = 115;
      context__.validate_dims("data initialization","C_11","double",
          context__.to_vec(N));
      C_11 = Eigen::Matrix<double, -1, 1>(N);
      stan::math::fill(C_11, std::numeric_limits<double>::quiet_NaN());
      
      {
        std::vector<local_scalar_t__> C_11_flat__;
        current_statement__ = 115;
        assign(C_11_flat__, nil_index_list(), context__.vals_r("C_11"),
          "assigning variable C_11_flat__");
        current_statement__ = 115;
        pos__ = 1;
        current_statement__ = 115;
        for (int sym1__ = 1; sym1__ <= N; ++sym1__) {
          current_statement__ = 115;
          assign(C_11, cons_list(index_uni(sym1__), nil_index_list()),
            C_11_flat__[(pos__ - 1)], "assigning variable C_11");
          current_statement__ = 115;
          pos__ = (pos__ + 1);}
      }
      current_statement__ = 116;
      validate_non_negative_index("C_12", "N", N);
      current_statement__ = 117;
      context__.validate_dims("data initialization","C_12","double",
          context__.to_vec(N));
      C_12 = Eigen::Matrix<double, -1, 1>(N);
      stan::math::fill(C_12, std::numeric_limits<double>::quiet_NaN());
      
      {
        std::vector<local_scalar_t__> C_12_flat__;
        current_statement__ = 117;
        assign(C_12_flat__, nil_index_list(), context__.vals_r("C_12"),
          "assigning variable C_12_flat__");
        current_statement__ = 117;
        pos__ = 1;
        current_statement__ = 117;
        for (int sym1__ = 1; sym1__ <= N; ++sym1__) {
          current_statement__ = 117;
          assign(C_12, cons_list(index_uni(sym1__), nil_index_list()),
            C_12_flat__[(pos__ - 1)], "assigning variable C_12");
          current_statement__ = 117;
          pos__ = (pos__ + 1);}
      }
      current_statement__ = 118;
      validate_non_negative_index("C_13", "N", N);
      current_statement__ = 119;
      context__.validate_dims("data initialization","C_13","double",
          context__.to_vec(N));
      C_13 = Eigen::Matrix<double, -1, 1>(N);
      stan::math::fill(C_13, std::numeric_limits<double>::quiet_NaN());
      
      {
        std::vector<local_scalar_t__> C_13_flat__;
        current_statement__ = 119;
        assign(C_13_flat__, nil_index_list(), context__.vals_r("C_13"),
          "assigning variable C_13_flat__");
        current_statement__ = 119;
        pos__ = 1;
        current_statement__ = 119;
        for (int sym1__ = 1; sym1__ <= N; ++sym1__) {
          current_statement__ = 119;
          assign(C_13, cons_list(index_uni(sym1__), nil_index_list()),
            C_13_flat__[(pos__ - 1)], "assigning variable C_13");
          current_statement__ = 119;
          pos__ = (pos__ + 1);}
      }
      current_statement__ = 120;
      validate_non_negative_index("C_14", "N", N);
      current_statement__ = 121;
      context__.validate_dims("data initialization","C_14","double",
          context__.to_vec(N));
      C_14 = Eigen::Matrix<double, -1, 1>(N);
      stan::math::fill(C_14, std::numeric_limits<double>::quiet_NaN());
      
      {
        std::vector<local_scalar_t__> C_14_flat__;
        current_statement__ = 121;
        assign(C_14_flat__, nil_index_list(), context__.vals_r("C_14"),
          "assigning variable C_14_flat__");
        current_statement__ = 121;
        pos__ = 1;
        current_statement__ = 121;
        for (int sym1__ = 1; sym1__ <= N; ++sym1__) {
          current_statement__ = 121;
          assign(C_14, cons_list(index_uni(sym1__), nil_index_list()),
            C_14_flat__[(pos__ - 1)], "assigning variable C_14");
          current_statement__ = 121;
          pos__ = (pos__ + 1);}
      }
      current_statement__ = 122;
      validate_non_negative_index("C_15", "N", N);
      current_statement__ = 123;
      context__.validate_dims("data initialization","C_15","double",
          context__.to_vec(N));
      C_15 = Eigen::Matrix<double, -1, 1>(N);
      stan::math::fill(C_15, std::numeric_limits<double>::quiet_NaN());
      
      {
        std::vector<local_scalar_t__> C_15_flat__;
        current_statement__ = 123;
        assign(C_15_flat__, nil_index_list(), context__.vals_r("C_15"),
          "assigning variable C_15_flat__");
        current_statement__ = 123;
        pos__ = 1;
        current_statement__ = 123;
        for (int sym1__ = 1; sym1__ <= N; ++sym1__) {
          current_statement__ = 123;
          assign(C_15, cons_list(index_uni(sym1__), nil_index_list()),
            C_15_flat__[(pos__ - 1)], "assigning variable C_15");
          current_statement__ = 123;
          pos__ = (pos__ + 1);}
      }
      current_statement__ = 124;
      validate_non_negative_index("C_16", "N", N);
      current_statement__ = 125;
      context__.validate_dims("data initialization","C_16","double",
          context__.to_vec(N));
      C_16 = Eigen::Matrix<double, -1, 1>(N);
      stan::math::fill(C_16, std::numeric_limits<double>::quiet_NaN());
      
      {
        std::vector<local_scalar_t__> C_16_flat__;
        current_statement__ = 125;
        assign(C_16_flat__, nil_index_list(), context__.vals_r("C_16"),
          "assigning variable C_16_flat__");
        current_statement__ = 125;
        pos__ = 1;
        current_statement__ = 125;
        for (int sym1__ = 1; sym1__ <= N; ++sym1__) {
          current_statement__ = 125;
          assign(C_16, cons_list(index_uni(sym1__), nil_index_list()),
            C_16_flat__[(pos__ - 1)], "assigning variable C_16");
          current_statement__ = 125;
          pos__ = (pos__ + 1);}
      }
      current_statement__ = 126;
      validate_non_negative_index("C_17", "N", N);
      current_statement__ = 127;
      context__.validate_dims("data initialization","C_17","double",
          context__.to_vec(N));
      C_17 = Eigen::Matrix<double, -1, 1>(N);
      stan::math::fill(C_17, std::numeric_limits<double>::quiet_NaN());
      
      {
        std::vector<local_scalar_t__> C_17_flat__;
        current_statement__ = 127;
        assign(C_17_flat__, nil_index_list(), context__.vals_r("C_17"),
          "assigning variable C_17_flat__");
        current_statement__ = 127;
        pos__ = 1;
        current_statement__ = 127;
        for (int sym1__ = 1; sym1__ <= N; ++sym1__) {
          current_statement__ = 127;
          assign(C_17, cons_list(index_uni(sym1__), nil_index_list()),
            C_17_flat__[(pos__ - 1)], "assigning variable C_17");
          current_statement__ = 127;
          pos__ = (pos__ + 1);}
      }
      current_statement__ = 128;
      validate_non_negative_index("C_18", "N", N);
      current_statement__ = 129;
      context__.validate_dims("data initialization","C_18","int",
          context__.to_vec(N));
      C_18 = std::vector<int>(N, std::numeric_limits<int>::min());
      
      current_statement__ = 129;
      assign(C_18, nil_index_list(), context__.vals_i("C_18"),
        "assigning variable C_18");
      current_statement__ = 130;
      context__.validate_dims("data initialization","N_1","int",
          context__.to_vec());
      N_1 = std::numeric_limits<int>::min();
      
      current_statement__ = 130;
      N_1 = context__.vals_i("N_1")[(1 - 1)];
      current_statement__ = 130;
      current_statement__ = 130;
      check_greater_or_equal(function__, "N_1", N_1, 1);
      current_statement__ = 131;
      context__.validate_dims("data initialization","M_1","int",
          context__.to_vec());
      M_1 = std::numeric_limits<int>::min();
      
      current_statement__ = 131;
      M_1 = context__.vals_i("M_1")[(1 - 1)];
      current_statement__ = 131;
      current_statement__ = 131;
      check_greater_or_equal(function__, "M_1", M_1, 1);
      current_statement__ = 132;
      validate_non_negative_index("J_1", "N", N);
      current_statement__ = 133;
      context__.validate_dims("data initialization","J_1","int",
          context__.to_vec(N));
      J_1 = std::vector<int>(N, std::numeric_limits<int>::min());
      
      current_statement__ = 133;
      assign(J_1, nil_index_list(), context__.vals_i("J_1"),
        "assigning variable J_1");
      current_statement__ = 133;
      for (int sym1__ = 1; sym1__ <= N; ++sym1__) {
        current_statement__ = 133;
        current_statement__ = 133;
        check_greater_or_equal(function__, "J_1[sym1__]", J_1[(sym1__ - 1)],
                               1);}
      current_statement__ = 134;
      validate_non_negative_index("Z_1_logk1_1", "N", N);
      current_statement__ = 135;
      context__.validate_dims("data initialization","Z_1_logk1_1","double",
          context__.to_vec(N));
      Z_1_logk1_1 = Eigen::Matrix<double, -1, 1>(N);
      stan::math::fill(Z_1_logk1_1, std::numeric_limits<double>::quiet_NaN());
      
      {
        std::vector<local_scalar_t__> Z_1_logk1_1_flat__;
        current_statement__ = 135;
        assign(Z_1_logk1_1_flat__, nil_index_list(),
          context__.vals_r("Z_1_logk1_1"),
          "assigning variable Z_1_logk1_1_flat__");
        current_statement__ = 135;
        pos__ = 1;
        current_statement__ = 135;
        for (int sym1__ = 1; sym1__ <= N; ++sym1__) {
          current_statement__ = 135;
          assign(Z_1_logk1_1, cons_list(index_uni(sym1__), nil_index_list()),
            Z_1_logk1_1_flat__[(pos__ - 1)], "assigning variable Z_1_logk1_1");
          current_statement__ = 135;
          pos__ = (pos__ + 1);}
      }
      current_statement__ = 136;
      validate_non_negative_index("Z_1_logvnd_2", "N", N);
      current_statement__ = 137;
      context__.validate_dims("data initialization","Z_1_logvnd_2","double",
          context__.to_vec(N));
      Z_1_logvnd_2 = Eigen::Matrix<double, -1, 1>(N);
      stan::math::fill(Z_1_logvnd_2, std::numeric_limits<double>::quiet_NaN());
      
      {
        std::vector<local_scalar_t__> Z_1_logvnd_2_flat__;
        current_statement__ = 137;
        assign(Z_1_logvnd_2_flat__, nil_index_list(),
          context__.vals_r("Z_1_logvnd_2"),
          "assigning variable Z_1_logvnd_2_flat__");
        current_statement__ = 137;
        pos__ = 1;
        current_statement__ = 137;
        for (int sym1__ = 1; sym1__ <= N; ++sym1__) {
          current_statement__ = 137;
          assign(Z_1_logvnd_2,
            cons_list(index_uni(sym1__), nil_index_list()),
            Z_1_logvnd_2_flat__[(pos__ - 1)],
            "assigning variable Z_1_logvnd_2");
          current_statement__ = 137;
          pos__ = (pos__ + 1);}
      }
      current_statement__ = 138;
      validate_non_negative_index("Z_1_logbpnd_3", "N", N);
      current_statement__ = 139;
      context__.validate_dims("data initialization","Z_1_logbpnd_3","double",
          context__.to_vec(N));
      Z_1_logbpnd_3 = Eigen::Matrix<double, -1, 1>(N);
      stan::math::fill(Z_1_logbpnd_3, std::numeric_limits<double>::quiet_NaN());
      
      {
        std::vector<local_scalar_t__> Z_1_logbpnd_3_flat__;
        current_statement__ = 139;
        assign(Z_1_logbpnd_3_flat__, nil_index_list(),
          context__.vals_r("Z_1_logbpnd_3"),
          "assigning variable Z_1_logbpnd_3_flat__");
        current_statement__ = 139;
        pos__ = 1;
        current_statement__ = 139;
        for (int sym1__ = 1; sym1__ <= N; ++sym1__) {
          current_statement__ = 139;
          assign(Z_1_logbpnd_3,
            cons_list(index_uni(sym1__), nil_index_list()),
            Z_1_logbpnd_3_flat__[(pos__ - 1)],
            "assigning variable Z_1_logbpnd_3");
          current_statement__ = 139;
          pos__ = (pos__ + 1);}
      }
      current_statement__ = 140;
      validate_non_negative_index("Z_1_logk4_4", "N", N);
      current_statement__ = 141;
      context__.validate_dims("data initialization","Z_1_logk4_4","double",
          context__.to_vec(N));
      Z_1_logk4_4 = Eigen::Matrix<double, -1, 1>(N);
      stan::math::fill(Z_1_logk4_4, std::numeric_limits<double>::quiet_NaN());
      
      {
        std::vector<local_scalar_t__> Z_1_logk4_4_flat__;
        current_statement__ = 141;
        assign(Z_1_logk4_4_flat__, nil_index_list(),
          context__.vals_r("Z_1_logk4_4"),
          "assigning variable Z_1_logk4_4_flat__");
        current_statement__ = 141;
        pos__ = 1;
        current_statement__ = 141;
        for (int sym1__ = 1; sym1__ <= N; ++sym1__) {
          current_statement__ = 141;
          assign(Z_1_logk4_4, cons_list(index_uni(sym1__), nil_index_list()),
            Z_1_logk4_4_flat__[(pos__ - 1)], "assigning variable Z_1_logk4_4");
          current_statement__ = 141;
          pos__ = (pos__ + 1);}
      }
      current_statement__ = 142;
      validate_non_negative_index("Z_1_logvb_5", "N", N);
      current_statement__ = 143;
      context__.validate_dims("data initialization","Z_1_logvb_5","double",
          context__.to_vec(N));
      Z_1_logvb_5 = Eigen::Matrix<double, -1, 1>(N);
      stan::math::fill(Z_1_logvb_5, std::numeric_limits<double>::quiet_NaN());
      
      {
        std::vector<local_scalar_t__> Z_1_logvb_5_flat__;
        current_statement__ = 143;
        assign(Z_1_logvb_5_flat__, nil_index_list(),
          context__.vals_r("Z_1_logvb_5"),
          "assigning variable Z_1_logvb_5_flat__");
        current_statement__ = 143;
        pos__ = 1;
        current_statement__ = 143;
        for (int sym1__ = 1; sym1__ <= N; ++sym1__) {
          current_statement__ = 143;
          assign(Z_1_logvb_5, cons_list(index_uni(sym1__), nil_index_list()),
            Z_1_logvb_5_flat__[(pos__ - 1)], "assigning variable Z_1_logvb_5");
          current_statement__ = 143;
          pos__ = (pos__ + 1);}
      }
      current_statement__ = 144;
      context__.validate_dims("data initialization","NC_1","int",
          context__.to_vec());
      NC_1 = std::numeric_limits<int>::min();
      
      current_statement__ = 144;
      NC_1 = context__.vals_i("NC_1")[(1 - 1)];
      current_statement__ = 144;
      current_statement__ = 144;
      check_greater_or_equal(function__, "NC_1", NC_1, 1);
      current_statement__ = 145;
      context__.validate_dims("data initialization","prior_only","int",
          context__.to_vec());
      prior_only = std::numeric_limits<int>::min();
      
      current_statement__ = 145;
      prior_only = context__.vals_i("prior_only")[(1 - 1)];
      current_statement__ = 146;
      validate_non_negative_index("b_logk1", "K_logk1", K_logk1);
      current_statement__ = 147;
      validate_non_negative_index("b_logvnd", "K_logvnd", K_logvnd);
      current_statement__ = 148;
      validate_non_negative_index("b_logbpnd", "K_logbpnd", K_logbpnd);
      current_statement__ = 149;
      validate_non_negative_index("b_logk4", "K_logk4", K_logk4);
      current_statement__ = 150;
      validate_non_negative_index("b_logvb", "K_logvb", K_logvb);
      current_statement__ = 151;
      validate_non_negative_index("sd_1", "M_1", M_1);
      current_statement__ = 152;
      validate_non_negative_index("z_1", "M_1", M_1);
      current_statement__ = 153;
      validate_non_negative_index("z_1", "N_1", N_1);
      current_statement__ = 154;
      validate_non_negative_index("L_1", "M_1", M_1);
      current_statement__ = 154;
      validate_non_negative_index("L_1", "M_1", M_1);
      current_statement__ = 155;
      validate_non_negative_index("r_1", "N_1", N_1);
      current_statement__ = 156;
      validate_non_negative_index("r_1", "M_1", M_1);
      current_statement__ = 157;
      validate_non_negative_index("r_1_logk1_1", "N_1", N_1);
      current_statement__ = 158;
      validate_non_negative_index("r_1_logvnd_2", "N_1", N_1);
      current_statement__ = 159;
      validate_non_negative_index("r_1_logbpnd_3", "N_1", N_1);
      current_statement__ = 160;
      validate_non_negative_index("r_1_logk4_4", "N_1", N_1);
      current_statement__ = 161;
      validate_non_negative_index("r_1_logvb_5", "N_1", N_1);
      current_statement__ = 162;
      validate_non_negative_index("Cor_1", "M_1", M_1);
      current_statement__ = 162;
      validate_non_negative_index("Cor_1", "M_1", M_1);
      current_statement__ = 163;
      validate_non_negative_index("cor_1", "NC_1", NC_1);
    } catch (const std::exception& e) {
      stan::lang::rethrow_located(e, locations_array__[current_statement__]);
      // Next line prevents compiler griping about no return
      throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***"); 
    }
    num_params_r__ = 0U;
    
    try {
      num_params_r__ += K_logk1;
      num_params_r__ += K_logvnd;
      num_params_r__ += K_logbpnd;
      num_params_r__ += K_logk4;
      num_params_r__ += K_logvb;
      num_params_r__ += 1;
      num_params_r__ += M_1;
      num_params_r__ += M_1 * N_1;
      num_params_r__ += ((M_1 * (M_1 - 1)) / 2);
    } catch (const std::exception& e) {
      stan::lang::rethrow_located(e, locations_array__[current_statement__]);
      // Next line prevents compiler griping about no return
      throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***"); 
    }
  }
  template <bool propto__, bool jacobian__, typename T__>
  inline T__ log_prob(std::vector<T__>& params_r__,
                      std::vector<int>& params_i__,
                      std::ostream* pstream__ = nullptr) const {
    using local_scalar_t__ = T__;
    T__ lp__(0.0);
    stan::math::accumulator<T__> lp_accum__;
    static const char* function__ = "cmdstanr_test_model_namespace::log_prob";
(void) function__;  // suppress unused var warning

    stan::io::reader<local_scalar_t__> in__(params_r__, params_i__);
    local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
    (void) DUMMY_VAR__;  // suppress unused var warning

    
    try {
      Eigen::Matrix<local_scalar_t__, -1, 1> b_logk1;
      b_logk1 = Eigen::Matrix<local_scalar_t__, -1, 1>(K_logk1);
      stan::math::fill(b_logk1, DUMMY_VAR__);
      
      current_statement__ = 1;
      b_logk1 = in__.vector(K_logk1);
      Eigen::Matrix<local_scalar_t__, -1, 1> b_logvnd;
      b_logvnd = Eigen::Matrix<local_scalar_t__, -1, 1>(K_logvnd);
      stan::math::fill(b_logvnd, DUMMY_VAR__);
      
      current_statement__ = 2;
      b_logvnd = in__.vector(K_logvnd);
      Eigen::Matrix<local_scalar_t__, -1, 1> b_logbpnd;
      b_logbpnd = Eigen::Matrix<local_scalar_t__, -1, 1>(K_logbpnd);
      stan::math::fill(b_logbpnd, DUMMY_VAR__);
      
      current_statement__ = 3;
      b_logbpnd = in__.vector(K_logbpnd);
      Eigen::Matrix<local_scalar_t__, -1, 1> b_logk4;
      b_logk4 = Eigen::Matrix<local_scalar_t__, -1, 1>(K_logk4);
      stan::math::fill(b_logk4, DUMMY_VAR__);
      
      current_statement__ = 4;
      b_logk4 = in__.vector(K_logk4);
      Eigen::Matrix<local_scalar_t__, -1, 1> b_logvb;
      b_logvb = Eigen::Matrix<local_scalar_t__, -1, 1>(K_logvb);
      stan::math::fill(b_logvb, DUMMY_VAR__);
      
      current_statement__ = 5;
      b_logvb = in__.vector(K_logvb);
      local_scalar_t__ sigma;
      sigma = DUMMY_VAR__;
      
      current_statement__ = 6;
      sigma = in__.scalar();
      current_statement__ = 6;
      if (jacobian__) {
        current_statement__ = 6;
        sigma = stan::math::lb_constrain(sigma, 0, lp__);
      } else {
        current_statement__ = 6;
        sigma = stan::math::lb_constrain(sigma, 0);
      }
      Eigen::Matrix<local_scalar_t__, -1, 1> sd_1;
      sd_1 = Eigen::Matrix<local_scalar_t__, -1, 1>(M_1);
      stan::math::fill(sd_1, DUMMY_VAR__);
      
      current_statement__ = 7;
      sd_1 = in__.vector(M_1);
      current_statement__ = 7;
      for (int sym1__ = 1; sym1__ <= M_1; ++sym1__) {
        current_statement__ = 7;
        if (jacobian__) {
          current_statement__ = 7;
          assign(sd_1, cons_list(index_uni(sym1__), nil_index_list()),
            stan::math::lb_constrain(sd_1[(sym1__ - 1)], 0, lp__),
            "assigning variable sd_1");
        } else {
          current_statement__ = 7;
          assign(sd_1, cons_list(index_uni(sym1__), nil_index_list()),
            stan::math::lb_constrain(sd_1[(sym1__ - 1)], 0),
            "assigning variable sd_1");
        }}
      Eigen::Matrix<local_scalar_t__, -1, -1> z_1;
      z_1 = Eigen::Matrix<local_scalar_t__, -1, -1>(M_1, N_1);
      stan::math::fill(z_1, DUMMY_VAR__);
      
      current_statement__ = 8;
      z_1 = in__.matrix(M_1, N_1);
      Eigen::Matrix<local_scalar_t__, -1, -1> L_1;
      L_1 = Eigen::Matrix<local_scalar_t__, -1, -1>(M_1, M_1);
      stan::math::fill(L_1, DUMMY_VAR__);
      
      Eigen::Matrix<local_scalar_t__, -1, 1> L_1_in__;
      L_1_in__ = Eigen::Matrix<local_scalar_t__, -1, 1>(((M_1 * (M_1 - 1)) /
                                                          2));
      stan::math::fill(L_1_in__, DUMMY_VAR__);
      
      current_statement__ = 9;
      L_1_in__ = in__.vector(((M_1 * (M_1 - 1)) / 2));
      current_statement__ = 9;
      if (jacobian__) {
        current_statement__ = 9;
        assign(L_1, nil_index_list(),
          stan::math::cholesky_corr_constrain(L_1_in__, M_1, lp__),
          "assigning variable L_1");
      } else {
        current_statement__ = 9;
        assign(L_1, nil_index_list(),
          stan::math::cholesky_corr_constrain(L_1_in__, M_1),
          "assigning variable L_1");
      }
      Eigen::Matrix<local_scalar_t__, -1, -1> r_1;
      r_1 = Eigen::Matrix<local_scalar_t__, -1, -1>(N_1, M_1);
      stan::math::fill(r_1, DUMMY_VAR__);
      
      Eigen::Matrix<local_scalar_t__, -1, 1> r_1_logk1_1;
      r_1_logk1_1 = Eigen::Matrix<local_scalar_t__, -1, 1>(N_1);
      stan::math::fill(r_1_logk1_1, DUMMY_VAR__);
      
      Eigen::Matrix<local_scalar_t__, -1, 1> r_1_logvnd_2;
      r_1_logvnd_2 = Eigen::Matrix<local_scalar_t__, -1, 1>(N_1);
      stan::math::fill(r_1_logvnd_2, DUMMY_VAR__);
      
      Eigen::Matrix<local_scalar_t__, -1, 1> r_1_logbpnd_3;
      r_1_logbpnd_3 = Eigen::Matrix<local_scalar_t__, -1, 1>(N_1);
      stan::math::fill(r_1_logbpnd_3, DUMMY_VAR__);
      
      Eigen::Matrix<local_scalar_t__, -1, 1> r_1_logk4_4;
      r_1_logk4_4 = Eigen::Matrix<local_scalar_t__, -1, 1>(N_1);
      stan::math::fill(r_1_logk4_4, DUMMY_VAR__);
      
      Eigen::Matrix<local_scalar_t__, -1, 1> r_1_logvb_5;
      r_1_logvb_5 = Eigen::Matrix<local_scalar_t__, -1, 1>(N_1);
      stan::math::fill(r_1_logvb_5, DUMMY_VAR__);
      
      current_statement__ = 16;
      assign(r_1, nil_index_list(),
        transpose(multiply(diag_pre_multiply(sd_1, L_1), z_1)),
        "assigning variable r_1");
      current_statement__ = 17;
      assign(r_1_logk1_1, nil_index_list(),
        rvalue(r_1,
          cons_list(index_omni(), cons_list(index_uni(1), nil_index_list())),
          "r_1"), "assigning variable r_1_logk1_1");
      current_statement__ = 18;
      assign(r_1_logvnd_2, nil_index_list(),
        rvalue(r_1,
          cons_list(index_omni(), cons_list(index_uni(2), nil_index_list())),
          "r_1"), "assigning variable r_1_logvnd_2");
      current_statement__ = 19;
      assign(r_1_logbpnd_3, nil_index_list(),
        rvalue(r_1,
          cons_list(index_omni(), cons_list(index_uni(3), nil_index_list())),
          "r_1"), "assigning variable r_1_logbpnd_3");
      current_statement__ = 20;
      assign(r_1_logk4_4, nil_index_list(),
        rvalue(r_1,
          cons_list(index_omni(), cons_list(index_uni(4), nil_index_list())),
          "r_1"), "assigning variable r_1_logk4_4");
      current_statement__ = 21;
      assign(r_1_logvb_5, nil_index_list(),
        rvalue(r_1,
          cons_list(index_omni(), cons_list(index_uni(5), nil_index_list())),
          "r_1"), "assigning variable r_1_logvb_5");
      {
        current_statement__ = 29;
        validate_non_negative_index("nlp_logk1", "N", N);
        Eigen::Matrix<local_scalar_t__, -1, 1> nlp_logk1;
        nlp_logk1 = Eigen::Matrix<local_scalar_t__, -1, 1>(N);
        stan::math::fill(nlp_logk1, DUMMY_VAR__);
        
        current_statement__ = 30;
        assign(nlp_logk1, nil_index_list(), multiply(X_logk1, b_logk1),
          "assigning variable nlp_logk1");
        current_statement__ = 31;
        validate_non_negative_index("nlp_logvnd", "N", N);
        Eigen::Matrix<local_scalar_t__, -1, 1> nlp_logvnd;
        nlp_logvnd = Eigen::Matrix<local_scalar_t__, -1, 1>(N);
        stan::math::fill(nlp_logvnd, DUMMY_VAR__);
        
        current_statement__ = 32;
        assign(nlp_logvnd, nil_index_list(), multiply(X_logvnd, b_logvnd),
          "assigning variable nlp_logvnd");
        current_statement__ = 33;
        validate_non_negative_index("nlp_logbpnd", "N", N);
        Eigen::Matrix<local_scalar_t__, -1, 1> nlp_logbpnd;
        nlp_logbpnd = Eigen::Matrix<local_scalar_t__, -1, 1>(N);
        stan::math::fill(nlp_logbpnd, DUMMY_VAR__);
        
        current_statement__ = 34;
        assign(nlp_logbpnd, nil_index_list(), multiply(X_logbpnd, b_logbpnd),
          "assigning variable nlp_logbpnd");
        current_statement__ = 35;
        validate_non_negative_index("nlp_logk4", "N", N);
        Eigen::Matrix<local_scalar_t__, -1, 1> nlp_logk4;
        nlp_logk4 = Eigen::Matrix<local_scalar_t__, -1, 1>(N);
        stan::math::fill(nlp_logk4, DUMMY_VAR__);
        
        current_statement__ = 36;
        assign(nlp_logk4, nil_index_list(), multiply(X_logk4, b_logk4),
          "assigning variable nlp_logk4");
        current_statement__ = 37;
        validate_non_negative_index("nlp_logvb", "N", N);
        Eigen::Matrix<local_scalar_t__, -1, 1> nlp_logvb;
        nlp_logvb = Eigen::Matrix<local_scalar_t__, -1, 1>(N);
        stan::math::fill(nlp_logvb, DUMMY_VAR__);
        
        current_statement__ = 38;
        assign(nlp_logvb, nil_index_list(), multiply(X_logvb, b_logvb),
          "assigning variable nlp_logvb");
        current_statement__ = 39;
        validate_non_negative_index("mu", "N", N);
        Eigen::Matrix<local_scalar_t__, -1, 1> mu;
        mu = Eigen::Matrix<local_scalar_t__, -1, 1>(N);
        stan::math::fill(mu, DUMMY_VAR__);
        
        current_statement__ = 43;
        for (int n = 1; n <= N; ++n) {
          current_statement__ = 41;
          assign(nlp_logk1, cons_list(index_uni(n), nil_index_list()),
            (nlp_logk1[(n - 1)] +
              (r_1_logk1_1[(J_1[(n - 1)] - 1)] * Z_1_logk1_1[(n - 1)])),
            "assigning variable nlp_logk1");}
        current_statement__ = 46;
        for (int n = 1; n <= N; ++n) {
          current_statement__ = 44;
          assign(nlp_logvnd, cons_list(index_uni(n), nil_index_list()),
            (nlp_logvnd[(n - 1)] +
              (r_1_logvnd_2[(J_1[(n - 1)] - 1)] * Z_1_logvnd_2[(n - 1)])),
            "assigning variable nlp_logvnd");}
        current_statement__ = 49;
        for (int n = 1; n <= N; ++n) {
          current_statement__ = 47;
          assign(nlp_logbpnd, cons_list(index_uni(n), nil_index_list()),
            (nlp_logbpnd[(n - 1)] +
              (r_1_logbpnd_3[(J_1[(n - 1)] - 1)] * Z_1_logbpnd_3[(n - 1)])),
            "assigning variable nlp_logbpnd");}
        current_statement__ = 52;
        for (int n = 1; n <= N; ++n) {
          current_statement__ = 50;
          assign(nlp_logk4, cons_list(index_uni(n), nil_index_list()),
            (nlp_logk4[(n - 1)] +
              (r_1_logk4_4[(J_1[(n - 1)] - 1)] * Z_1_logk4_4[(n - 1)])),
            "assigning variable nlp_logk4");}
        current_statement__ = 55;
        for (int n = 1; n <= N; ++n) {
          current_statement__ = 53;
          assign(nlp_logvb, cons_list(index_uni(n), nil_index_list()),
            (nlp_logvb[(n - 1)] +
              (r_1_logvb_5[(J_1[(n - 1)] - 1)] * Z_1_logvb_5[(n - 1)])),
            "assigning variable nlp_logvb");}
        current_statement__ = 58;
        for (int n = 1; n <= N; ++n) {
          current_statement__ = 56;
          assign(mu, cons_list(index_uni(n), nil_index_list()),
            twotcm_log_stan(nlp_logk1[(n - 1)], nlp_logvnd[(n - 1)],
              nlp_logbpnd[(n - 1)], nlp_logk4[(n - 1)], nlp_logvb[(n - 1)],
              C_1[(n - 1)], C_2[(n - 1)], C_3[(n - 1)], C_4[(n - 1)],
              C_5[(n - 1)], C_6[(n - 1)], C_7[(n - 1)], C_8[(n - 1)],
              C_9[(n - 1)], C_10[(n - 1)], C_11[(n - 1)], C_12[(n - 1)],
              C_13[(n - 1)], C_14[(n - 1)], C_15[(n - 1)], C_16[(n - 1)],
              C_17[(n - 1)], C_18[(n - 1)], pstream__),
            "assigning variable mu");}
        current_statement__ = 59;
        lp_accum__.add(normal_lpdf<false>(b_logk1, -3, 0.2));
        current_statement__ = 60;
        lp_accum__.add(normal_lpdf<false>(b_logvnd, -1.5, 0.2));
        current_statement__ = 61;
        lp_accum__.add(normal_lpdf<false>(b_logbpnd, 1, 0.2));
        current_statement__ = 62;
        lp_accum__.add(normal_lpdf<false>(b_logk4, -4, 0.2));
        current_statement__ = 63;
        lp_accum__.add(normal_lpdf<false>(b_logvb, -2, 1));
        current_statement__ = 64;
        lp_accum__.add(
          (normal_lpdf<false>(sigma, 0, 0.005) -
            (1 * normal_lccdf(0, 0, 0.005))));
        current_statement__ = 65;
        lp_accum__.add(
          (normal_lpdf<false>(sd_1, 0, 0.2) - (5 * normal_lccdf(0, 0, 0.2))));
        current_statement__ = 66;
        lp_accum__.add(std_normal_lpdf<false>(to_vector(z_1)));
        current_statement__ = 67;
        lp_accum__.add(lkj_corr_cholesky_lpdf<false>(L_1, 1));
        current_statement__ = 70;
        if (logical_negation(prior_only)) {
          current_statement__ = 68;
          lp_accum__.add(normal_lpdf<false>(Y, mu, sigma));
        } 
      }
    } catch (const std::exception& e) {
      stan::lang::rethrow_located(e, locations_array__[current_statement__]);
      // Next line prevents compiler griping about no return
      throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***"); 
    }
    lp_accum__.add(lp__);
    return lp_accum__.sum();
    } // log_prob() 
    
  template <typename RNG>
  inline void write_array(RNG& base_rng__, std::vector<double>& params_r__,
                          std::vector<int>& params_i__,
                          std::vector<double>& vars__,
                          bool emit_transformed_parameters__ = true,
                          bool emit_generated_quantities__ = true,
                          std::ostream* pstream__ = nullptr) const {
    using local_scalar_t__ = double;
    vars__.resize(0);
    stan::io::reader<local_scalar_t__> in__(params_r__, params_i__);
    static const char* function__ = "cmdstanr_test_model_namespace::write_array";
(void) function__;  // suppress unused var warning

    (void) function__;  // suppress unused var warning

    double lp__ = 0.0;
    (void) lp__;  // dummy to suppress unused var warning
    stan::math::accumulator<double> lp_accum__;
    local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
    (void) DUMMY_VAR__;  // suppress unused var warning

    
    try {
      Eigen::Matrix<double, -1, 1> b_logk1;
      b_logk1 = Eigen::Matrix<double, -1, 1>(K_logk1);
      stan::math::fill(b_logk1, std::numeric_limits<double>::quiet_NaN());
      
      current_statement__ = 1;
      b_logk1 = in__.vector(K_logk1);
      Eigen::Matrix<double, -1, 1> b_logvnd;
      b_logvnd = Eigen::Matrix<double, -1, 1>(K_logvnd);
      stan::math::fill(b_logvnd, std::numeric_limits<double>::quiet_NaN());
      
      current_statement__ = 2;
      b_logvnd = in__.vector(K_logvnd);
      Eigen::Matrix<double, -1, 1> b_logbpnd;
      b_logbpnd = Eigen::Matrix<double, -1, 1>(K_logbpnd);
      stan::math::fill(b_logbpnd, std::numeric_limits<double>::quiet_NaN());
      
      current_statement__ = 3;
      b_logbpnd = in__.vector(K_logbpnd);
      Eigen::Matrix<double, -1, 1> b_logk4;
      b_logk4 = Eigen::Matrix<double, -1, 1>(K_logk4);
      stan::math::fill(b_logk4, std::numeric_limits<double>::quiet_NaN());
      
      current_statement__ = 4;
      b_logk4 = in__.vector(K_logk4);
      Eigen::Matrix<double, -1, 1> b_logvb;
      b_logvb = Eigen::Matrix<double, -1, 1>(K_logvb);
      stan::math::fill(b_logvb, std::numeric_limits<double>::quiet_NaN());
      
      current_statement__ = 5;
      b_logvb = in__.vector(K_logvb);
      double sigma;
      sigma = std::numeric_limits<double>::quiet_NaN();
      
      current_statement__ = 6;
      sigma = in__.scalar();
      current_statement__ = 6;
      sigma = stan::math::lb_constrain(sigma, 0);
      Eigen::Matrix<double, -1, 1> sd_1;
      sd_1 = Eigen::Matrix<double, -1, 1>(M_1);
      stan::math::fill(sd_1, std::numeric_limits<double>::quiet_NaN());
      
      current_statement__ = 7;
      sd_1 = in__.vector(M_1);
      current_statement__ = 7;
      for (int sym1__ = 1; sym1__ <= M_1; ++sym1__) {
        current_statement__ = 7;
        assign(sd_1, cons_list(index_uni(sym1__), nil_index_list()),
          stan::math::lb_constrain(sd_1[(sym1__ - 1)], 0),
          "assigning variable sd_1");}
      Eigen::Matrix<double, -1, -1> z_1;
      z_1 = Eigen::Matrix<double, -1, -1>(M_1, N_1);
      stan::math::fill(z_1, std::numeric_limits<double>::quiet_NaN());
      
      current_statement__ = 8;
      z_1 = in__.matrix(M_1, N_1);
      Eigen::Matrix<double, -1, -1> L_1;
      L_1 = Eigen::Matrix<double, -1, -1>(M_1, M_1);
      stan::math::fill(L_1, std::numeric_limits<double>::quiet_NaN());
      
      Eigen::Matrix<local_scalar_t__, -1, 1> L_1_in__;
      L_1_in__ = Eigen::Matrix<local_scalar_t__, -1, 1>(((M_1 * (M_1 - 1)) /
                                                          2));
      stan::math::fill(L_1_in__, DUMMY_VAR__);
      
      current_statement__ = 9;
      L_1_in__ = in__.vector(((M_1 * (M_1 - 1)) / 2));
      current_statement__ = 9;
      assign(L_1, nil_index_list(),
        stan::math::cholesky_corr_constrain(L_1_in__, M_1),
        "assigning variable L_1");
      Eigen::Matrix<double, -1, -1> r_1;
      r_1 = Eigen::Matrix<double, -1, -1>(N_1, M_1);
      stan::math::fill(r_1, std::numeric_limits<double>::quiet_NaN());
      
      Eigen::Matrix<double, -1, 1> r_1_logk1_1;
      r_1_logk1_1 = Eigen::Matrix<double, -1, 1>(N_1);
      stan::math::fill(r_1_logk1_1, std::numeric_limits<double>::quiet_NaN());
      
      Eigen::Matrix<double, -1, 1> r_1_logvnd_2;
      r_1_logvnd_2 = Eigen::Matrix<double, -1, 1>(N_1);
      stan::math::fill(r_1_logvnd_2, std::numeric_limits<double>::quiet_NaN());
      
      Eigen::Matrix<double, -1, 1> r_1_logbpnd_3;
      r_1_logbpnd_3 = Eigen::Matrix<double, -1, 1>(N_1);
      stan::math::fill(r_1_logbpnd_3, std::numeric_limits<double>::quiet_NaN());
      
      Eigen::Matrix<double, -1, 1> r_1_logk4_4;
      r_1_logk4_4 = Eigen::Matrix<double, -1, 1>(N_1);
      stan::math::fill(r_1_logk4_4, std::numeric_limits<double>::quiet_NaN());
      
      Eigen::Matrix<double, -1, 1> r_1_logvb_5;
      r_1_logvb_5 = Eigen::Matrix<double, -1, 1>(N_1);
      stan::math::fill(r_1_logvb_5, std::numeric_limits<double>::quiet_NaN());
      
      for (int sym1__ = 1; sym1__ <= K_logk1; ++sym1__) {
        vars__.emplace_back(b_logk1[(sym1__ - 1)]);}
      for (int sym1__ = 1; sym1__ <= K_logvnd; ++sym1__) {
        vars__.emplace_back(b_logvnd[(sym1__ - 1)]);}
      for (int sym1__ = 1; sym1__ <= K_logbpnd; ++sym1__) {
        vars__.emplace_back(b_logbpnd[(sym1__ - 1)]);}
      for (int sym1__ = 1; sym1__ <= K_logk4; ++sym1__) {
        vars__.emplace_back(b_logk4[(sym1__ - 1)]);}
      for (int sym1__ = 1; sym1__ <= K_logvb; ++sym1__) {
        vars__.emplace_back(b_logvb[(sym1__ - 1)]);}
      vars__.emplace_back(sigma);
      for (int sym1__ = 1; sym1__ <= M_1; ++sym1__) {
        vars__.emplace_back(sd_1[(sym1__ - 1)]);}
      for (int sym1__ = 1; sym1__ <= N_1; ++sym1__) {
        for (int sym2__ = 1; sym2__ <= M_1; ++sym2__) {
          vars__.emplace_back(
            rvalue(z_1,
              cons_list(index_uni(sym2__),
                cons_list(index_uni(sym1__), nil_index_list())), "z_1"));}}
      for (int sym1__ = 1; sym1__ <= M_1; ++sym1__) {
        for (int sym2__ = 1; sym2__ <= M_1; ++sym2__) {
          vars__.emplace_back(
            rvalue(L_1,
              cons_list(index_uni(sym2__),
                cons_list(index_uni(sym1__), nil_index_list())), "L_1"));}}
      if (logical_negation((primitive_value(emit_transformed_parameters__) ||
            primitive_value(emit_generated_quantities__)))) {
        return ;
      } 
      current_statement__ = 16;
      assign(r_1, nil_index_list(),
        transpose(multiply(diag_pre_multiply(sd_1, L_1), z_1)),
        "assigning variable r_1");
      current_statement__ = 17;
      assign(r_1_logk1_1, nil_index_list(),
        rvalue(r_1,
          cons_list(index_omni(), cons_list(index_uni(1), nil_index_list())),
          "r_1"), "assigning variable r_1_logk1_1");
      current_statement__ = 18;
      assign(r_1_logvnd_2, nil_index_list(),
        rvalue(r_1,
          cons_list(index_omni(), cons_list(index_uni(2), nil_index_list())),
          "r_1"), "assigning variable r_1_logvnd_2");
      current_statement__ = 19;
      assign(r_1_logbpnd_3, nil_index_list(),
        rvalue(r_1,
          cons_list(index_omni(), cons_list(index_uni(3), nil_index_list())),
          "r_1"), "assigning variable r_1_logbpnd_3");
      current_statement__ = 20;
      assign(r_1_logk4_4, nil_index_list(),
        rvalue(r_1,
          cons_list(index_omni(), cons_list(index_uni(4), nil_index_list())),
          "r_1"), "assigning variable r_1_logk4_4");
      current_statement__ = 21;
      assign(r_1_logvb_5, nil_index_list(),
        rvalue(r_1,
          cons_list(index_omni(), cons_list(index_uni(5), nil_index_list())),
          "r_1"), "assigning variable r_1_logvb_5");
      if (emit_transformed_parameters__) {
        for (int sym1__ = 1; sym1__ <= M_1; ++sym1__) {
          for (int sym2__ = 1; sym2__ <= N_1; ++sym2__) {
            vars__.emplace_back(
              rvalue(r_1,
                cons_list(index_uni(sym2__),
                  cons_list(index_uni(sym1__), nil_index_list())), "r_1"));}}
        for (int sym1__ = 1; sym1__ <= N_1; ++sym1__) {
          vars__.emplace_back(r_1_logk1_1[(sym1__ - 1)]);}
        for (int sym1__ = 1; sym1__ <= N_1; ++sym1__) {
          vars__.emplace_back(r_1_logvnd_2[(sym1__ - 1)]);}
        for (int sym1__ = 1; sym1__ <= N_1; ++sym1__) {
          vars__.emplace_back(r_1_logbpnd_3[(sym1__ - 1)]);}
        for (int sym1__ = 1; sym1__ <= N_1; ++sym1__) {
          vars__.emplace_back(r_1_logk4_4[(sym1__ - 1)]);}
        for (int sym1__ = 1; sym1__ <= N_1; ++sym1__) {
          vars__.emplace_back(r_1_logvb_5[(sym1__ - 1)]);}
      } 
      if (logical_negation(emit_generated_quantities__)) {
        return ;
      } 
      Eigen::Matrix<double, -1, -1> Cor_1;
      Cor_1 = Eigen::Matrix<double, -1, -1>(M_1, M_1);
      stan::math::fill(Cor_1, std::numeric_limits<double>::quiet_NaN());
      
      current_statement__ = 22;
      assign(Cor_1, nil_index_list(), multiply_lower_tri_self_transpose(L_1),
        "assigning variable Cor_1");
      Eigen::Matrix<double, -1, 1> cor_1;
      cor_1 = Eigen::Matrix<double, -1, 1>(NC_1);
      stan::math::fill(cor_1, std::numeric_limits<double>::quiet_NaN());
      
      current_statement__ = 28;
      for (int k = 1; k <= M_1; ++k) {
        current_statement__ = 26;
        for (int j = 1; j <= (k - 1); ++j) {
          current_statement__ = 24;
          assign(cor_1,
            cons_list(index_uni((choose((k - 1), 2) + j)), nil_index_list()),
            rvalue(Cor_1,
              cons_list(index_uni(j),
                cons_list(index_uni(k), nil_index_list())), "Cor_1"),
            "assigning variable cor_1");}}
      current_statement__ = 22;
      current_statement__ = 22;
      check_corr_matrix(function__, "Cor_1", Cor_1);
      current_statement__ = 23;
      for (int sym1__ = 1; sym1__ <= NC_1; ++sym1__) {
        current_statement__ = 23;
        current_statement__ = 23;
        check_greater_or_equal(function__, "cor_1[sym1__]",
                               cor_1[(sym1__ - 1)], -1);}
      current_statement__ = 23;
      for (int sym1__ = 1; sym1__ <= NC_1; ++sym1__) {
        current_statement__ = 23;
        current_statement__ = 23;
        check_less_or_equal(function__, "cor_1[sym1__]", cor_1[(sym1__ - 1)],
                            1);}
      for (int sym1__ = 1; sym1__ <= M_1; ++sym1__) {
        for (int sym2__ = 1; sym2__ <= M_1; ++sym2__) {
          vars__.emplace_back(
            rvalue(Cor_1,
              cons_list(index_uni(sym2__),
                cons_list(index_uni(sym1__), nil_index_list())), "Cor_1"));}}
      for (int sym1__ = 1; sym1__ <= NC_1; ++sym1__) {
        vars__.emplace_back(cor_1[(sym1__ - 1)]);}
    } catch (const std::exception& e) {
      stan::lang::rethrow_located(e, locations_array__[current_statement__]);
      // Next line prevents compiler griping about no return
      throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***"); 
    }
    } // write_array() 
    
  inline void transform_inits(const stan::io::var_context& context__,
                              std::vector<int>& params_i__,
                              std::vector<double>& vars__,
                              std::ostream* pstream__) const
    final {
    using local_scalar_t__ = double;
    vars__.clear();
    vars__.reserve(num_params_r__);
    
    try {
      int pos__;
      pos__ = std::numeric_limits<int>::min();
      
      pos__ = 1;
      Eigen::Matrix<double, -1, 1> b_logk1;
      b_logk1 = Eigen::Matrix<double, -1, 1>(K_logk1);
      stan::math::fill(b_logk1, std::numeric_limits<double>::quiet_NaN());
      
      {
        std::vector<local_scalar_t__> b_logk1_flat__;
        current_statement__ = 1;
        assign(b_logk1_flat__, nil_index_list(), context__.vals_r("b_logk1"),
          "assigning variable b_logk1_flat__");
        current_statement__ = 1;
        pos__ = 1;
        current_statement__ = 1;
        for (int sym1__ = 1; sym1__ <= K_logk1; ++sym1__) {
          current_statement__ = 1;
          assign(b_logk1, cons_list(index_uni(sym1__), nil_index_list()),
            b_logk1_flat__[(pos__ - 1)], "assigning variable b_logk1");
          current_statement__ = 1;
          pos__ = (pos__ + 1);}
      }
      Eigen::Matrix<double, -1, 1> b_logvnd;
      b_logvnd = Eigen::Matrix<double, -1, 1>(K_logvnd);
      stan::math::fill(b_logvnd, std::numeric_limits<double>::quiet_NaN());
      
      {
        std::vector<local_scalar_t__> b_logvnd_flat__;
        current_statement__ = 2;
        assign(b_logvnd_flat__, nil_index_list(),
          context__.vals_r("b_logvnd"), "assigning variable b_logvnd_flat__");
        current_statement__ = 2;
        pos__ = 1;
        current_statement__ = 2;
        for (int sym1__ = 1; sym1__ <= K_logvnd; ++sym1__) {
          current_statement__ = 2;
          assign(b_logvnd, cons_list(index_uni(sym1__), nil_index_list()),
            b_logvnd_flat__[(pos__ - 1)], "assigning variable b_logvnd");
          current_statement__ = 2;
          pos__ = (pos__ + 1);}
      }
      Eigen::Matrix<double, -1, 1> b_logbpnd;
      b_logbpnd = Eigen::Matrix<double, -1, 1>(K_logbpnd);
      stan::math::fill(b_logbpnd, std::numeric_limits<double>::quiet_NaN());
      
      {
        std::vector<local_scalar_t__> b_logbpnd_flat__;
        current_statement__ = 3;
        assign(b_logbpnd_flat__, nil_index_list(),
          context__.vals_r("b_logbpnd"),
          "assigning variable b_logbpnd_flat__");
        current_statement__ = 3;
        pos__ = 1;
        current_statement__ = 3;
        for (int sym1__ = 1; sym1__ <= K_logbpnd; ++sym1__) {
          current_statement__ = 3;
          assign(b_logbpnd, cons_list(index_uni(sym1__), nil_index_list()),
            b_logbpnd_flat__[(pos__ - 1)], "assigning variable b_logbpnd");
          current_statement__ = 3;
          pos__ = (pos__ + 1);}
      }
      Eigen::Matrix<double, -1, 1> b_logk4;
      b_logk4 = Eigen::Matrix<double, -1, 1>(K_logk4);
      stan::math::fill(b_logk4, std::numeric_limits<double>::quiet_NaN());
      
      {
        std::vector<local_scalar_t__> b_logk4_flat__;
        current_statement__ = 4;
        assign(b_logk4_flat__, nil_index_list(), context__.vals_r("b_logk4"),
          "assigning variable b_logk4_flat__");
        current_statement__ = 4;
        pos__ = 1;
        current_statement__ = 4;
        for (int sym1__ = 1; sym1__ <= K_logk4; ++sym1__) {
          current_statement__ = 4;
          assign(b_logk4, cons_list(index_uni(sym1__), nil_index_list()),
            b_logk4_flat__[(pos__ - 1)], "assigning variable b_logk4");
          current_statement__ = 4;
          pos__ = (pos__ + 1);}
      }
      Eigen::Matrix<double, -1, 1> b_logvb;
      b_logvb = Eigen::Matrix<double, -1, 1>(K_logvb);
      stan::math::fill(b_logvb, std::numeric_limits<double>::quiet_NaN());
      
      {
        std::vector<local_scalar_t__> b_logvb_flat__;
        current_statement__ = 5;
        assign(b_logvb_flat__, nil_index_list(), context__.vals_r("b_logvb"),
          "assigning variable b_logvb_flat__");
        current_statement__ = 5;
        pos__ = 1;
        current_statement__ = 5;
        for (int sym1__ = 1; sym1__ <= K_logvb; ++sym1__) {
          current_statement__ = 5;
          assign(b_logvb, cons_list(index_uni(sym1__), nil_index_list()),
            b_logvb_flat__[(pos__ - 1)], "assigning variable b_logvb");
          current_statement__ = 5;
          pos__ = (pos__ + 1);}
      }
      double sigma;
      sigma = std::numeric_limits<double>::quiet_NaN();
      
      current_statement__ = 6;
      sigma = context__.vals_r("sigma")[(1 - 1)];
      current_statement__ = 6;
      sigma = stan::math::lb_free(sigma, 0);
      Eigen::Matrix<double, -1, 1> sd_1;
      sd_1 = Eigen::Matrix<double, -1, 1>(M_1);
      stan::math::fill(sd_1, std::numeric_limits<double>::quiet_NaN());
      
      {
        std::vector<local_scalar_t__> sd_1_flat__;
        current_statement__ = 7;
        assign(sd_1_flat__, nil_index_list(), context__.vals_r("sd_1"),
          "assigning variable sd_1_flat__");
        current_statement__ = 7;
        pos__ = 1;
        current_statement__ = 7;
        for (int sym1__ = 1; sym1__ <= M_1; ++sym1__) {
          current_statement__ = 7;
          assign(sd_1, cons_list(index_uni(sym1__), nil_index_list()),
            sd_1_flat__[(pos__ - 1)], "assigning variable sd_1");
          current_statement__ = 7;
          pos__ = (pos__ + 1);}
      }
      current_statement__ = 7;
      for (int sym1__ = 1; sym1__ <= M_1; ++sym1__) {
        current_statement__ = 7;
        assign(sd_1, cons_list(index_uni(sym1__), nil_index_list()),
          stan::math::lb_free(sd_1[(sym1__ - 1)], 0),
          "assigning variable sd_1");}
      Eigen::Matrix<double, -1, -1> z_1;
      z_1 = Eigen::Matrix<double, -1, -1>(M_1, N_1);
      stan::math::fill(z_1, std::numeric_limits<double>::quiet_NaN());
      
      {
        std::vector<local_scalar_t__> z_1_flat__;
        current_statement__ = 8;
        assign(z_1_flat__, nil_index_list(), context__.vals_r("z_1"),
          "assigning variable z_1_flat__");
        current_statement__ = 8;
        pos__ = 1;
        current_statement__ = 8;
        for (int sym1__ = 1; sym1__ <= N_1; ++sym1__) {
          current_statement__ = 8;
          for (int sym2__ = 1; sym2__ <= M_1; ++sym2__) {
            current_statement__ = 8;
            assign(z_1,
              cons_list(index_uni(sym2__),
                cons_list(index_uni(sym1__), nil_index_list())),
              z_1_flat__[(pos__ - 1)], "assigning variable z_1");
            current_statement__ = 8;
            pos__ = (pos__ + 1);}}
      }
      Eigen::Matrix<double, -1, -1> L_1;
      L_1 = Eigen::Matrix<double, -1, -1>(M_1, M_1);
      stan::math::fill(L_1, std::numeric_limits<double>::quiet_NaN());
      
      {
        std::vector<local_scalar_t__> L_1_flat__;
        current_statement__ = 9;
        assign(L_1_flat__, nil_index_list(), context__.vals_r("L_1"),
          "assigning variable L_1_flat__");
        current_statement__ = 9;
        pos__ = 1;
        current_statement__ = 9;
        for (int sym1__ = 1; sym1__ <= M_1; ++sym1__) {
          current_statement__ = 9;
          for (int sym2__ = 1; sym2__ <= M_1; ++sym2__) {
            current_statement__ = 9;
            assign(L_1,
              cons_list(index_uni(sym2__),
                cons_list(index_uni(sym1__), nil_index_list())),
              L_1_flat__[(pos__ - 1)], "assigning variable L_1");
            current_statement__ = 9;
            pos__ = (pos__ + 1);}}
      }
      Eigen::Matrix<double, -1, 1> L_1_free__;
      L_1_free__ = Eigen::Matrix<double, -1, 1>(((M_1 * (M_1 - 1)) / 2));
      stan::math::fill(L_1_free__, std::numeric_limits<double>::quiet_NaN());
      
      current_statement__ = 9;
      assign(L_1_free__, nil_index_list(),
        stan::math::cholesky_corr_free(L_1), "assigning variable L_1_free__");
      for (int sym1__ = 1; sym1__ <= K_logk1; ++sym1__) {
        vars__.emplace_back(b_logk1[(sym1__ - 1)]);}
      for (int sym1__ = 1; sym1__ <= K_logvnd; ++sym1__) {
        vars__.emplace_back(b_logvnd[(sym1__ - 1)]);}
      for (int sym1__ = 1; sym1__ <= K_logbpnd; ++sym1__) {
        vars__.emplace_back(b_logbpnd[(sym1__ - 1)]);}
      for (int sym1__ = 1; sym1__ <= K_logk4; ++sym1__) {
        vars__.emplace_back(b_logk4[(sym1__ - 1)]);}
      for (int sym1__ = 1; sym1__ <= K_logvb; ++sym1__) {
        vars__.emplace_back(b_logvb[(sym1__ - 1)]);}
      vars__.emplace_back(sigma);
      for (int sym1__ = 1; sym1__ <= M_1; ++sym1__) {
        vars__.emplace_back(sd_1[(sym1__ - 1)]);}
      for (int sym1__ = 1; sym1__ <= N_1; ++sym1__) {
        for (int sym2__ = 1; sym2__ <= M_1; ++sym2__) {
          vars__.emplace_back(
            rvalue(z_1,
              cons_list(index_uni(sym2__),
                cons_list(index_uni(sym1__), nil_index_list())), "z_1"));}}
      for (int sym1__ = 1; sym1__ <= ((M_1 * (M_1 - 1)) / 2); ++sym1__) {
        vars__.emplace_back(L_1_free__[(sym1__ - 1)]);}
    } catch (const std::exception& e) {
      stan::lang::rethrow_located(e, locations_array__[current_statement__]);
      // Next line prevents compiler griping about no return
      throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***"); 
    }
    } // transform_inits() 
    
  inline void get_param_names(std::vector<std::string>& names__) const {
    
    names__.clear();
    names__.emplace_back("b_logk1");
    names__.emplace_back("b_logvnd");
    names__.emplace_back("b_logbpnd");
    names__.emplace_back("b_logk4");
    names__.emplace_back("b_logvb");
    names__.emplace_back("sigma");
    names__.emplace_back("sd_1");
    names__.emplace_back("z_1");
    names__.emplace_back("L_1");
    names__.emplace_back("r_1");
    names__.emplace_back("r_1_logk1_1");
    names__.emplace_back("r_1_logvnd_2");
    names__.emplace_back("r_1_logbpnd_3");
    names__.emplace_back("r_1_logk4_4");
    names__.emplace_back("r_1_logvb_5");
    names__.emplace_back("Cor_1");
    names__.emplace_back("cor_1");
    } // get_param_names() 
    
  inline void get_dims(std::vector<std::vector<size_t>>& dimss__) const
    final {
    dimss__.clear();
    dimss__.emplace_back(std::vector<size_t>{static_cast<size_t>(K_logk1)});
    
    dimss__.emplace_back(std::vector<size_t>{static_cast<size_t>(K_logvnd)});
    
    dimss__.emplace_back(std::vector<size_t>{static_cast<size_t>(K_logbpnd)});
    
    dimss__.emplace_back(std::vector<size_t>{static_cast<size_t>(K_logk4)});
    
    dimss__.emplace_back(std::vector<size_t>{static_cast<size_t>(K_logvb)});
    
    dimss__.emplace_back(std::vector<size_t>{});
    
    dimss__.emplace_back(std::vector<size_t>{static_cast<size_t>(M_1)});
    
    dimss__.emplace_back(std::vector<size_t>{static_cast<size_t>(M_1),
                                             static_cast<size_t>(N_1)});
    
    dimss__.emplace_back(std::vector<size_t>{static_cast<size_t>(M_1),
                                             static_cast<size_t>(M_1)});
    
    dimss__.emplace_back(std::vector<size_t>{static_cast<size_t>(N_1),
                                             static_cast<size_t>(M_1)});
    
    dimss__.emplace_back(std::vector<size_t>{static_cast<size_t>(N_1)});
    
    dimss__.emplace_back(std::vector<size_t>{static_cast<size_t>(N_1)});
    
    dimss__.emplace_back(std::vector<size_t>{static_cast<size_t>(N_1)});
    
    dimss__.emplace_back(std::vector<size_t>{static_cast<size_t>(N_1)});
    
    dimss__.emplace_back(std::vector<size_t>{static_cast<size_t>(N_1)});
    
    dimss__.emplace_back(std::vector<size_t>{static_cast<size_t>(M_1),
                                             static_cast<size_t>(M_1)});
    
    dimss__.emplace_back(std::vector<size_t>{static_cast<size_t>(NC_1)});
    
    } // get_dims() 
    
  inline void constrained_param_names(
                                      std::vector<std::string>& param_names__,
                                      bool emit_transformed_parameters__ = true,
                                      bool emit_generated_quantities__ = true) const
    final {
    
    for (int sym1__ = 1; sym1__ <= K_logk1; ++sym1__) {
      {
        param_names__.emplace_back(std::string() + "b_logk1" + '.' + std::to_string(sym1__));
      }}
    for (int sym1__ = 1; sym1__ <= K_logvnd; ++sym1__) {
      {
        param_names__.emplace_back(std::string() + "b_logvnd" + '.' + std::to_string(sym1__));
      }}
    for (int sym1__ = 1; sym1__ <= K_logbpnd; ++sym1__) {
      {
        param_names__.emplace_back(std::string() + "b_logbpnd" + '.' + std::to_string(sym1__));
      }}
    for (int sym1__ = 1; sym1__ <= K_logk4; ++sym1__) {
      {
        param_names__.emplace_back(std::string() + "b_logk4" + '.' + std::to_string(sym1__));
      }}
    for (int sym1__ = 1; sym1__ <= K_logvb; ++sym1__) {
      {
        param_names__.emplace_back(std::string() + "b_logvb" + '.' + std::to_string(sym1__));
      }}
    param_names__.emplace_back(std::string() + "sigma");
    for (int sym1__ = 1; sym1__ <= M_1; ++sym1__) {
      {
        param_names__.emplace_back(std::string() + "sd_1" + '.' + std::to_string(sym1__));
      }}
    for (int sym1__ = 1; sym1__ <= N_1; ++sym1__) {
      {
        for (int sym2__ = 1; sym2__ <= M_1; ++sym2__) {
          {
            param_names__.emplace_back(std::string() + "z_1" + '.' + std::to_string(sym2__) + '.' + std::to_string(sym1__));
          }}
      }}
    for (int sym1__ = 1; sym1__ <= M_1; ++sym1__) {
      {
        for (int sym2__ = 1; sym2__ <= M_1; ++sym2__) {
          {
            param_names__.emplace_back(std::string() + "L_1" + '.' + std::to_string(sym2__) + '.' + std::to_string(sym1__));
          }}
      }}
    if (emit_transformed_parameters__) {
      for (int sym1__ = 1; sym1__ <= M_1; ++sym1__) {
        {
          for (int sym2__ = 1; sym2__ <= N_1; ++sym2__) {
            {
              param_names__.emplace_back(std::string() + "r_1" + '.' + std::to_string(sym2__) + '.' + std::to_string(sym1__));
            }}
        }}
      for (int sym1__ = 1; sym1__ <= N_1; ++sym1__) {
        {
          param_names__.emplace_back(std::string() + "r_1_logk1_1" + '.' + std::to_string(sym1__));
        }}
      for (int sym1__ = 1; sym1__ <= N_1; ++sym1__) {
        {
          param_names__.emplace_back(std::string() + "r_1_logvnd_2" + '.' + std::to_string(sym1__));
        }}
      for (int sym1__ = 1; sym1__ <= N_1; ++sym1__) {
        {
          param_names__.emplace_back(std::string() + "r_1_logbpnd_3" + '.' + std::to_string(sym1__));
        }}
      for (int sym1__ = 1; sym1__ <= N_1; ++sym1__) {
        {
          param_names__.emplace_back(std::string() + "r_1_logk4_4" + '.' + std::to_string(sym1__));
        }}
      for (int sym1__ = 1; sym1__ <= N_1; ++sym1__) {
        {
          param_names__.emplace_back(std::string() + "r_1_logvb_5" + '.' + std::to_string(sym1__));
        }}
    }
    
    if (emit_generated_quantities__) {
      for (int sym1__ = 1; sym1__ <= M_1; ++sym1__) {
        {
          for (int sym2__ = 1; sym2__ <= M_1; ++sym2__) {
            {
              param_names__.emplace_back(std::string() + "Cor_1" + '.' + std::to_string(sym2__) + '.' + std::to_string(sym1__));
            }}
        }}
      for (int sym1__ = 1; sym1__ <= NC_1; ++sym1__) {
        {
          param_names__.emplace_back(std::string() + "cor_1" + '.' + std::to_string(sym1__));
        }}
    }
    
    } // constrained_param_names() 
    
  inline void unconstrained_param_names(
                                        std::vector<std::string>& param_names__,
                                        bool emit_transformed_parameters__ = true,
                                        bool emit_generated_quantities__ = true) const
    final {
    
    for (int sym1__ = 1; sym1__ <= K_logk1; ++sym1__) {
      {
        param_names__.emplace_back(std::string() + "b_logk1" + '.' + std::to_string(sym1__));
      }}
    for (int sym1__ = 1; sym1__ <= K_logvnd; ++sym1__) {
      {
        param_names__.emplace_back(std::string() + "b_logvnd" + '.' + std::to_string(sym1__));
      }}
    for (int sym1__ = 1; sym1__ <= K_logbpnd; ++sym1__) {
      {
        param_names__.emplace_back(std::string() + "b_logbpnd" + '.' + std::to_string(sym1__));
      }}
    for (int sym1__ = 1; sym1__ <= K_logk4; ++sym1__) {
      {
        param_names__.emplace_back(std::string() + "b_logk4" + '.' + std::to_string(sym1__));
      }}
    for (int sym1__ = 1; sym1__ <= K_logvb; ++sym1__) {
      {
        param_names__.emplace_back(std::string() + "b_logvb" + '.' + std::to_string(sym1__));
      }}
    param_names__.emplace_back(std::string() + "sigma");
    for (int sym1__ = 1; sym1__ <= M_1; ++sym1__) {
      {
        param_names__.emplace_back(std::string() + "sd_1" + '.' + std::to_string(sym1__));
      }}
    for (int sym1__ = 1; sym1__ <= N_1; ++sym1__) {
      {
        for (int sym2__ = 1; sym2__ <= M_1; ++sym2__) {
          {
            param_names__.emplace_back(std::string() + "z_1" + '.' + std::to_string(sym2__) + '.' + std::to_string(sym1__));
          }}
      }}
    for (int sym1__ = 1; sym1__ <= ((M_1 * (M_1 - 1)) / 2); ++sym1__) {
      {
        param_names__.emplace_back(std::string() + "L_1" + '.' + std::to_string(sym1__));
      }}
    if (emit_transformed_parameters__) {
      for (int sym1__ = 1; sym1__ <= M_1; ++sym1__) {
        {
          for (int sym2__ = 1; sym2__ <= N_1; ++sym2__) {
            {
              param_names__.emplace_back(std::string() + "r_1" + '.' + std::to_string(sym2__) + '.' + std::to_string(sym1__));
            }}
        }}
      for (int sym1__ = 1; sym1__ <= N_1; ++sym1__) {
        {
          param_names__.emplace_back(std::string() + "r_1_logk1_1" + '.' + std::to_string(sym1__));
        }}
      for (int sym1__ = 1; sym1__ <= N_1; ++sym1__) {
        {
          param_names__.emplace_back(std::string() + "r_1_logvnd_2" + '.' + std::to_string(sym1__));
        }}
      for (int sym1__ = 1; sym1__ <= N_1; ++sym1__) {
        {
          param_names__.emplace_back(std::string() + "r_1_logbpnd_3" + '.' + std::to_string(sym1__));
        }}
      for (int sym1__ = 1; sym1__ <= N_1; ++sym1__) {
        {
          param_names__.emplace_back(std::string() + "r_1_logk4_4" + '.' + std::to_string(sym1__));
        }}
      for (int sym1__ = 1; sym1__ <= N_1; ++sym1__) {
        {
          param_names__.emplace_back(std::string() + "r_1_logvb_5" + '.' + std::to_string(sym1__));
        }}
    }
    
    if (emit_generated_quantities__) {
      for (int sym1__ = 1; sym1__ <= ((M_1 * (M_1 - 1)) / 2); ++sym1__) {
        {
          param_names__.emplace_back(std::string() + "Cor_1" + '.' + std::to_string(sym1__));
        }}
      for (int sym1__ = 1; sym1__ <= NC_1; ++sym1__) {
        {
          param_names__.emplace_back(std::string() + "cor_1" + '.' + std::to_string(sym1__));
        }}
    }
    
    } // unconstrained_param_names() 
    
  inline std::string get_constrained_sizedtypes() const {
    stringstream s__;
    s__ << "[{\"name\":\"b_logk1\",\"type\":{\"name\":\"vector\",\"length\":" << K_logk1 << "},\"block\":\"parameters\"},{\"name\":\"b_logvnd\",\"type\":{\"name\":\"vector\",\"length\":" << K_logvnd << "},\"block\":\"parameters\"},{\"name\":\"b_logbpnd\",\"type\":{\"name\":\"vector\",\"length\":" << K_logbpnd << "},\"block\":\"parameters\"},{\"name\":\"b_logk4\",\"type\":{\"name\":\"vector\",\"length\":" << K_logk4 << "},\"block\":\"parameters\"},{\"name\":\"b_logvb\",\"type\":{\"name\":\"vector\",\"length\":" << K_logvb << "},\"block\":\"parameters\"},{\"name\":\"sigma\",\"type\":{\"name\":\"real\"},\"block\":\"parameters\"},{\"name\":\"sd_1\",\"type\":{\"name\":\"vector\",\"length\":" << M_1 << "},\"block\":\"parameters\"},{\"name\":\"z_1\",\"type\":{\"name\":\"matrix\",\"rows\":" << M_1 << ",\"cols\":" << N_1 << "},\"block\":\"parameters\"},{\"name\":\"L_1\",\"type\":{\"name\":\"matrix\",\"rows\":" << M_1 << ",\"cols\":" << M_1 << "},\"block\":\"parameters\"},{\"name\":\"r_1\",\"type\":{\"name\":\"matrix\",\"rows\":" << N_1 << ",\"cols\":" << M_1 << "},\"block\":\"transformed_parameters\"},{\"name\":\"r_1_logk1_1\",\"type\":{\"name\":\"vector\",\"length\":" << N_1 << "},\"block\":\"transformed_parameters\"},{\"name\":\"r_1_logvnd_2\",\"type\":{\"name\":\"vector\",\"length\":" << N_1 << "},\"block\":\"transformed_parameters\"},{\"name\":\"r_1_logbpnd_3\",\"type\":{\"name\":\"vector\",\"length\":" << N_1 << "},\"block\":\"transformed_parameters\"},{\"name\":\"r_1_logk4_4\",\"type\":{\"name\":\"vector\",\"length\":" << N_1 << "},\"block\":\"transformed_parameters\"},{\"name\":\"r_1_logvb_5\",\"type\":{\"name\":\"vector\",\"length\":" << N_1 << "},\"block\":\"transformed_parameters\"},{\"name\":\"Cor_1\",\"type\":{\"name\":\"matrix\",\"rows\":" << M_1 << ",\"cols\":" << M_1 << "},\"block\":\"generated_quantities\"},{\"name\":\"cor_1\",\"type\":{\"name\":\"vector\",\"length\":" << NC_1 << "},\"block\":\"generated_quantities\"}]";
    return s__.str();
    } // get_constrained_sizedtypes() 
    
  inline std::string get_unconstrained_sizedtypes() const {
    stringstream s__;
    s__ << "[{\"name\":\"b_logk1\",\"type\":{\"name\":\"vector\",\"length\":" << K_logk1 << "},\"block\":\"parameters\"},{\"name\":\"b_logvnd\",\"type\":{\"name\":\"vector\",\"length\":" << K_logvnd << "},\"block\":\"parameters\"},{\"name\":\"b_logbpnd\",\"type\":{\"name\":\"vector\",\"length\":" << K_logbpnd << "},\"block\":\"parameters\"},{\"name\":\"b_logk4\",\"type\":{\"name\":\"vector\",\"length\":" << K_logk4 << "},\"block\":\"parameters\"},{\"name\":\"b_logvb\",\"type\":{\"name\":\"vector\",\"length\":" << K_logvb << "},\"block\":\"parameters\"},{\"name\":\"sigma\",\"type\":{\"name\":\"real\"},\"block\":\"parameters\"},{\"name\":\"sd_1\",\"type\":{\"name\":\"vector\",\"length\":" << M_1 << "},\"block\":\"parameters\"},{\"name\":\"z_1\",\"type\":{\"name\":\"matrix\",\"rows\":" << M_1 << ",\"cols\":" << N_1 << "},\"block\":\"parameters\"},{\"name\":\"L_1\",\"type\":{\"name\":\"vector\",\"length\":" << ((M_1 * (M_1 - 1)) / 2) << "},\"block\":\"parameters\"},{\"name\":\"r_1\",\"type\":{\"name\":\"matrix\",\"rows\":" << N_1 << ",\"cols\":" << M_1 << "},\"block\":\"transformed_parameters\"},{\"name\":\"r_1_logk1_1\",\"type\":{\"name\":\"vector\",\"length\":" << N_1 << "},\"block\":\"transformed_parameters\"},{\"name\":\"r_1_logvnd_2\",\"type\":{\"name\":\"vector\",\"length\":" << N_1 << "},\"block\":\"transformed_parameters\"},{\"name\":\"r_1_logbpnd_3\",\"type\":{\"name\":\"vector\",\"length\":" << N_1 << "},\"block\":\"transformed_parameters\"},{\"name\":\"r_1_logk4_4\",\"type\":{\"name\":\"vector\",\"length\":" << N_1 << "},\"block\":\"transformed_parameters\"},{\"name\":\"r_1_logvb_5\",\"type\":{\"name\":\"vector\",\"length\":" << N_1 << "},\"block\":\"transformed_parameters\"},{\"name\":\"Cor_1\",\"type\":{\"name\":\"vector\",\"length\":" << ((M_1 * (M_1 - 1)) / 2) << "},\"block\":\"generated_quantities\"},{\"name\":\"cor_1\",\"type\":{\"name\":\"vector\",\"length\":" << NC_1 << "},\"block\":\"generated_quantities\"}]";
    return s__.str();
    } // get_unconstrained_sizedtypes() 
    
  
    // Begin method overload boilerplate
    template <typename RNG>
    inline void write_array(RNG& base_rng__,
                     Eigen::Matrix<double,Eigen::Dynamic,1>& params_r,
                     Eigen::Matrix<double,Eigen::Dynamic,1>& vars,
                     bool emit_transformed_parameters__ = true,
                     bool emit_generated_quantities__ = true,
                     std::ostream* pstream = nullptr) const {
      std::vector<double> params_r_vec(params_r.size());
      for (int i = 0; i < params_r.size(); ++i)
        params_r_vec[i] = params_r(i);
      std::vector<double> vars_vec;
      std::vector<int> params_i_vec;
      write_array(base_rng__, params_r_vec, params_i_vec, vars_vec,
          emit_transformed_parameters__, emit_generated_quantities__, pstream);
      vars.resize(vars_vec.size());
      for (int i = 0; i < vars.size(); ++i)
        vars(i) = vars_vec[i];
    }

    template <bool propto__, bool jacobian__, typename T_>
    inline T_ log_prob(Eigen::Matrix<T_,Eigen::Dynamic,1>& params_r,
               std::ostream* pstream = nullptr) const {
      std::vector<T_> vec_params_r;
      vec_params_r.reserve(params_r.size());
      for (int i = 0; i < params_r.size(); ++i)
        vec_params_r.push_back(params_r(i));
      std::vector<int> vec_params_i;
      return log_prob<propto__,jacobian__,T_>(vec_params_r, vec_params_i, pstream);
    }

    inline void transform_inits(const stan::io::var_context& context,
                         Eigen::Matrix<double, Eigen::Dynamic, 1>& params_r,
                         std::ostream* pstream__ = nullptr) const {
      std::vector<double> params_r_vec;
      std::vector<int> params_i_vec;
      transform_inits(context, params_i_vec, params_r_vec, pstream__);
      params_r.resize(params_r_vec.size());
      for (int i = 0; i < params_r.size(); ++i)
        params_r(i) = params_r_vec[i];
    }

};
}
using stan_model = cmdstanr_test_model_namespace::cmdstanr_test_model;

#ifndef USING_R

// Boilerplate
stan::model::model_base& new_model(
        stan::io::var_context& data_context,
        unsigned int seed,
        std::ostream* msg_stream) {
  stan_model* m = new stan_model(data_context, seed, msg_stream);
  return *m;
}

#endif