#ifndef MODELS_HPP
#define MODELS_HPP
#define STAN__SERVICES__COMMAND_HPP
#include <rstan/rstaninc.hpp>
#ifndef USE_STANC3
#define USE_STANC3
#endif
// Code generated by stanc v2.26.1-4-gd72b68b7-dirty
#include <stan/model/model_header.hpp>
namespace model292eaa50420b56__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; 
stan::math::profile_map profiles__;
static int current_statement__= 0;
static const std::vector<string> locations_array__ = {" (found before start of program)",
                                                      " (in 'anon_model', line 21, column 4 to column 15)",
                                                      " (in 'anon_model', line 22, column 4 to column 18)",
                                                      " (in 'anon_model', line 23, column 4 to column 22)",
                                                      " (in 'anon_model', line 24, column 4 to column 14)",
                                                      " (in 'anon_model', line 27, column 8 to column 32)",
                                                      " (in 'anon_model', line 28, column 8 to column 34)",
                                                      " (in 'anon_model', line 26, column 19 to line 29, column 5)",
                                                      " (in 'anon_model', line 26, column 4 to line 29, column 5)",
                                                      " (in 'anon_model', line 32, column 12 to column 51)",
                                                      " (in 'anon_model', line 31, column 23 to line 33, column 9)",
                                                      " (in 'anon_model', line 31, column 8 to line 33, column 9)",
                                                      " (in 'anon_model', line 30, column 19 to line 34, column 5)",
                                                      " (in 'anon_model', line 30, column 4 to line 34, column 5)",
                                                      " (in 'anon_model', line 37, column 20 to column 21)",
                                                      " (in 'anon_model', line 37, column 15 to column 16)",
                                                      " (in 'anon_model', line 37, column 8 to column 23)",
                                                      " (in 'anon_model', line 39, column 12 to column 31)",
                                                      " (in 'anon_model', line 40, column 12 to column 41)",
                                                      " (in 'anon_model', line 38, column 22 to line 41, column 9)",
                                                      " (in 'anon_model', line 38, column 8 to line 41, column 9)",
                                                      " (in 'anon_model', line 36, column 4 to line 42, column 5)",
                                                      " (in 'anon_model', line 13, column 2 to column 17)",
                                                      " (in 'anon_model', line 14, column 2 to column 17)",
                                                      " (in 'anon_model', line 15, column 2 to column 17)",
                                                      " (in 'anon_model', line 16, column 27 to column 28)",
                                                      " (in 'anon_model', line 16, column 2 to column 30)",
                                                      " (in 'anon_model', line 17, column 18 to column 19)",
                                                      " (in 'anon_model', line 17, column 13 to column 14)",
                                                      " (in 'anon_model', line 17, column 2 to column 21)",
                                                      " (in 'anon_model', line 21, column 12 to column 13)",
                                                      " (in 'anon_model', line 22, column 15 to column 16)",
                                                      " (in 'anon_model', line 23, column 19 to column 20)",
                                                      " (in 'anon_model', line 23, column 11 to column 12)",
                                                      " (in 'anon_model', line 24, column 11 to column 12)"};
class model292eaa50420b56_ final : public model_base_crtp<model292eaa50420b56_> {
private:
  int D;
  int N;
  int L;
  std::vector<int> ll;
  std::vector<Eigen::Matrix<double, 1, -1>> x;
 
public:
  ~model292eaa50420b56_() { }
  
  inline std::string model_name() const final { return "model292eaa50420b56_"; }
  inline std::vector<std::string> model_compile_info() const noexcept {
    return std::vector<std::string>{"stanc_version = stanc3 v2.26.1-4-gd72b68b7-dirty", "stancflags = "};
  }
  
  
  model292eaa50420b56_(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__ = "model292eaa50420b56__namespace::model292eaa50420b56_";
    (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__ = 22;
      context__.validate_dims("data initialization","D","int",
          context__.to_vec());
      D = std::numeric_limits<int>::min();
      
      current_statement__ = 22;
      D = context__.vals_i("D")[(1 - 1)];
      current_statement__ = 22;
      current_statement__ = 22;
      check_greater_or_equal(function__, "D", D, 1);
      current_statement__ = 23;
      context__.validate_dims("data initialization","N","int",
          context__.to_vec());
      N = std::numeric_limits<int>::min();
      
      current_statement__ = 23;
      N = context__.vals_i("N")[(1 - 1)];
      current_statement__ = 23;
      current_statement__ = 23;
      check_greater_or_equal(function__, "N", N, 1);
      current_statement__ = 24;
      context__.validate_dims("data initialization","L","int",
          context__.to_vec());
      L = std::numeric_limits<int>::min();
      
      current_statement__ = 24;
      L = context__.vals_i("L")[(1 - 1)];
      current_statement__ = 24;
      current_statement__ = 24;
      check_greater_or_equal(function__, "L", L, 1);
      current_statement__ = 25;
      validate_non_negative_index("ll", "N", N);
      current_statement__ = 26;
      context__.validate_dims("data initialization","ll","int",
          context__.to_vec(N));
      ll = std::vector<int>(N, std::numeric_limits<int>::min());
      
      current_statement__ = 26;
      assign(ll, nil_index_list(), context__.vals_i("ll"),
        "assigning variable ll");
      current_statement__ = 26;
      for (int sym1__ = 1; sym1__ <= N; ++sym1__) {
        current_statement__ = 26;
        current_statement__ = 26;
        check_greater_or_equal(function__, "ll[sym1__]", ll[(sym1__ - 1)], 1);
      }
      current_statement__ = 26;
      for (int sym1__ = 1; sym1__ <= N; ++sym1__) {
        current_statement__ = 26;
        current_statement__ = 26;
        check_less_or_equal(function__, "ll[sym1__]", ll[(sym1__ - 1)], L);}
      current_statement__ = 27;
      validate_non_negative_index("x", "N", N);
      current_statement__ = 28;
      validate_non_negative_index("x", "D", D);
      current_statement__ = 29;
      context__.validate_dims("data initialization","x","double",
          context__.to_vec(N, D));
      x = std::vector<Eigen::Matrix<double, 1, -1>>(N, Eigen::Matrix<double, 1, -1>(D));
      stan::math::fill(x, std::numeric_limits<double>::quiet_NaN());
      
      {
        std::vector<local_scalar_t__> x_flat__;
        current_statement__ = 29;
        assign(x_flat__, nil_index_list(), context__.vals_r("x"),
          "assigning variable x_flat__");
        current_statement__ = 29;
        pos__ = 1;
        current_statement__ = 29;
        for (int sym1__ = 1; sym1__ <= D; ++sym1__) {
          current_statement__ = 29;
          for (int sym2__ = 1; sym2__ <= N; ++sym2__) {
            current_statement__ = 29;
            assign(x,
              cons_list(index_uni(sym2__),
                cons_list(index_uni(sym1__), nil_index_list())),
              x_flat__[(pos__ - 1)], "assigning variable x");
            current_statement__ = 29;
            pos__ = (pos__ + 1);}}
      }
      current_statement__ = 30;
      validate_non_negative_index("mu", "D", D);
      current_statement__ = 31;
      validate_non_negative_index("sigma", "D", D);
      current_statement__ = 32;
      validate_non_negative_index("beta", "L", L);
      current_statement__ = 33;
      validate_non_negative_index("beta", "D", D);
      current_statement__ = 34;
      validate_non_negative_index("p", "N", N);
    } 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 {
      
    } 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 VecR, typename VecI, stan::require_vector_like_t<VecR>* = nullptr, stan::require_vector_like_vt<std::is_integral, VecI>* = nullptr>
  inline stan::scalar_type_t<VecR> log_prob_impl(VecR& params_r__,
                                                 VecI& params_i__,
                                                 std::ostream* pstream__ = nullptr) const {
    using T__ = stan::scalar_type_t<VecR>;
    using local_scalar_t__ = T__;
    T__ lp__(0.0);
    stan::math::accumulator<T__> lp_accum__;
    static const char* function__ = "model292eaa50420b56__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 {
      
    } 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_impl() 
    
  template <typename RNG, typename VecR, typename VecI, typename VecVar, stan::require_vector_like_vt<std::is_floating_point, VecR>* = nullptr, stan::require_vector_like_vt<std::is_integral, VecI>* = nullptr, stan::require_std_vector_vt<std::is_floating_point, VecVar>* = nullptr>
  inline void write_array_impl(RNG& base_rng__, VecR& params_r__,
                               VecI& params_i__, VecVar& vars__,
                               const bool emit_transformed_parameters__ = true,
                               const 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__ = "model292eaa50420b56__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 {
      if (logical_negation((primitive_value(emit_transformed_parameters__) ||
            primitive_value(emit_generated_quantities__)))) {
        return ;
      } 
      if (logical_negation(emit_generated_quantities__)) {
        return ;
      } 
      std::vector<double> mu;
      mu = std::vector<double>(D, std::numeric_limits<double>::quiet_NaN());
      
      std::vector<double> sigma;
      sigma = std::vector<double>(D, std::numeric_limits<double>::quiet_NaN());
      
      std::vector<Eigen::Matrix<double, -1, 1>> beta;
      beta = std::vector<Eigen::Matrix<double, -1, 1>>(L, Eigen::Matrix<double, -1, 1>(D));
      stan::math::fill(beta, std::numeric_limits<double>::quiet_NaN());
      
      std::vector<double> p;
      p = std::vector<double>(N, std::numeric_limits<double>::quiet_NaN());
      
      current_statement__ = 8;
      for (int d = 1; d <= D; ++d) {
        current_statement__ = 5;
        assign(mu, cons_list(index_uni(d), nil_index_list()),
          normal_rng(0, 1, base_rng__), "assigning variable mu");
        current_statement__ = 6;
        assign(sigma, cons_list(index_uni(d), nil_index_list()),
          gamma_rng(2, 1, base_rng__), "assigning variable sigma");}
      current_statement__ = 13;
      for (int l = 1; l <= L; ++l) {
        current_statement__ = 11;
        for (int d = 1; d <= D; ++d) {
          current_statement__ = 9;
          assign(beta,
            cons_list(index_uni(l),
              cons_list(index_uni(d), nil_index_list())),
            normal_rng(mu[(d - 1)], sigma[(d - 1)], base_rng__),
            "assigning variable beta");}}
      {
        current_statement__ = 14;
        validate_non_negative_index("b", "N", N);
        current_statement__ = 15;
        validate_non_negative_index("b", "D", D);
        std::vector<Eigen::Matrix<double, -1, 1>> b;
        b = std::vector<Eigen::Matrix<double, -1, 1>>(N, Eigen::Matrix<double, -1, 1>(D));
        stan::math::fill(b, std::numeric_limits<double>::quiet_NaN());
        
        current_statement__ = 20;
        for (int n = 1; n <= N; ++n) {
          current_statement__ = 17;
          assign(b, cons_list(index_uni(n), nil_index_list()),
            beta[(ll[(n - 1)] - 1)], "assigning variable b");
          current_statement__ = 18;
          assign(p, cons_list(index_uni(n), nil_index_list()),
            logit(multiply(x[(n - 1)], b[(n - 1)])), "assigning variable p");
        }
      }
      for (int sym1__ = 1; sym1__ <= D; ++sym1__) {
        vars__.emplace_back(mu[(sym1__ - 1)]);}
      for (int sym1__ = 1; sym1__ <= D; ++sym1__) {
        vars__.emplace_back(sigma[(sym1__ - 1)]);}
      for (int sym1__ = 1; sym1__ <= D; ++sym1__) {
        for (int sym2__ = 1; sym2__ <= L; ++sym2__) {
          vars__.emplace_back(beta[(sym2__ - 1)][(sym1__ - 1)]);}}
      for (int sym1__ = 1; sym1__ <= N; ++sym1__) {
        vars__.emplace_back(p[(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_impl() 
    
  template <typename VecVar, typename VecI, stan::require_std_vector_t<VecVar>* = nullptr, stan::require_vector_like_vt<std::is_integral, VecI>* = nullptr>
  inline void transform_inits_impl(const stan::io::var_context& context__,
                                   VecI& params_i__, VecVar& vars__,
                                   std::ostream* pstream__ = nullptr) const {
    using local_scalar_t__ = double;
    vars__.clear();
    vars__.reserve(num_params_r__);
    
    try {
      int pos__;
      pos__ = std::numeric_limits<int>::min();
      
      pos__ = 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_impl() 
    
  inline void get_param_names(std::vector<std::string>& names__) const {
    
    names__.clear();
    names__.emplace_back("mu");
    names__.emplace_back("sigma");
    names__.emplace_back("beta");
    names__.emplace_back("p");
    } // get_param_names() 
    
  inline void get_dims(std::vector<std::vector<size_t>>& dimss__) const {
    dimss__.clear();
    dimss__.emplace_back(std::vector<size_t>{static_cast<size_t>(D)});
    
    dimss__.emplace_back(std::vector<size_t>{static_cast<size_t>(D)});
    
    dimss__.emplace_back(std::vector<size_t>{static_cast<size_t>(L),
                                             static_cast<size_t>(D)});
    
    dimss__.emplace_back(std::vector<size_t>{static_cast<size_t>(N)});
    
    } // 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 {
    
    
    if (emit_transformed_parameters__) {
      
    }
    
    if (emit_generated_quantities__) {
      for (int sym1__ = 1; sym1__ <= D; ++sym1__) {
        {
          param_names__.emplace_back(std::string() + "mu" + '.' + std::to_string(sym1__));
        }}
      for (int sym1__ = 1; sym1__ <= D; ++sym1__) {
        {
          param_names__.emplace_back(std::string() + "sigma" + '.' + std::to_string(sym1__));
        }}
      for (int sym1__ = 1; sym1__ <= D; ++sym1__) {
        {
          for (int sym2__ = 1; sym2__ <= L; ++sym2__) {
            {
              param_names__.emplace_back(std::string() + "beta" + '.' + std::to_string(sym2__) + '.' + std::to_string(sym1__));
            }}
        }}
      for (int sym1__ = 1; sym1__ <= N; ++sym1__) {
        {
          param_names__.emplace_back(std::string() + "p" + '.' + 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 {
    
    
    if (emit_transformed_parameters__) {
      
    }
    
    if (emit_generated_quantities__) {
      for (int sym1__ = 1; sym1__ <= D; ++sym1__) {
        {
          param_names__.emplace_back(std::string() + "mu" + '.' + std::to_string(sym1__));
        }}
      for (int sym1__ = 1; sym1__ <= D; ++sym1__) {
        {
          param_names__.emplace_back(std::string() + "sigma" + '.' + std::to_string(sym1__));
        }}
      for (int sym1__ = 1; sym1__ <= D; ++sym1__) {
        {
          for (int sym2__ = 1; sym2__ <= L; ++sym2__) {
            {
              param_names__.emplace_back(std::string() + "beta" + '.' + std::to_string(sym2__) + '.' + std::to_string(sym1__));
            }}
        }}
      for (int sym1__ = 1; sym1__ <= N; ++sym1__) {
        {
          param_names__.emplace_back(std::string() + "p" + '.' + std::to_string(sym1__));
        }}
    }
    
    } // unconstrained_param_names() 
    
  inline std::string get_constrained_sizedtypes() const {
    stringstream s__;
    s__ << "[{\"name\":\"mu\",\"type\":{\"name\":\"array\",\"length\":" << D << ",\"element_type\":{\"name\":\"real\"}},\"block\":\"generated_quantities\"},{\"name\":\"sigma\",\"type\":{\"name\":\"array\",\"length\":" << D << ",\"element_type\":{\"name\":\"real\"}},\"block\":\"generated_quantities\"},{\"name\":\"beta\",\"type\":{\"name\":\"array\",\"length\":" << L << ",\"element_type\":{\"name\":\"vector\",\"length\":" << D << "}},\"block\":\"generated_quantities\"},{\"name\":\"p\",\"type\":{\"name\":\"array\",\"length\":" << N << ",\"element_type\":{\"name\":\"real\"}},\"block\":\"generated_quantities\"}]";
    return s__.str();
    } // get_constrained_sizedtypes() 
    
  inline std::string get_unconstrained_sizedtypes() const {
    stringstream s__;
    s__ << "[{\"name\":\"mu\",\"type\":{\"name\":\"array\",\"length\":" << D << ",\"element_type\":{\"name\":\"real\"}},\"block\":\"generated_quantities\"},{\"name\":\"sigma\",\"type\":{\"name\":\"array\",\"length\":" << D << ",\"element_type\":{\"name\":\"real\"}},\"block\":\"generated_quantities\"},{\"name\":\"beta\",\"type\":{\"name\":\"array\",\"length\":" << L << ",\"element_type\":{\"name\":\"vector\",\"length\":" << D << "}},\"block\":\"generated_quantities\"},{\"name\":\"p\",\"type\":{\"name\":\"array\",\"length\":" << N << ",\"element_type\":{\"name\":\"real\"}},\"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,
                            const bool emit_transformed_parameters = true,
                            const bool emit_generated_quantities = true,
                            std::ostream* pstream = nullptr) const {
      std::vector<double> vars_vec(vars.size());
      std::vector<int> params_i;
      write_array_impl(base_rng, params_r, params_i, vars_vec,
          emit_transformed_parameters, emit_generated_quantities, pstream);
      vars.resize(vars_vec.size());
      for (int i = 0; i < vars.size(); ++i) {
        vars.coeffRef(i) = vars_vec[i];
      }
    }
    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 {
      write_array_impl(base_rng, params_r, params_i, vars, emit_transformed_parameters, emit_generated_quantities, pstream);
    }
    template <bool propto__, bool jacobian__, typename T_>
    inline T_ log_prob(Eigen::Matrix<T_,Eigen::Dynamic,1>& params_r,
                       std::ostream* pstream = nullptr) const {
      Eigen::Matrix<int, -1, 1> params_i;
      return log_prob_impl<propto__, jacobian__>(params_r, params_i, pstream);
    }
    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 {
      return log_prob_impl<propto__, jacobian__>(params_r, 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 final {
      std::vector<double> params_r_vec(params_r.size());
      std::vector<int> params_i;
      transform_inits_impl(context, params_i, params_r_vec, pstream);
      params_r.resize(params_r_vec.size());
      for (int i = 0; i < params_r.size(); ++i) {
        params_r.coeffRef(i) = params_r_vec[i];
      }
    }
    inline void transform_inits(const stan::io::var_context& context,
                                std::vector<int>& params_i,
                                std::vector<double>& vars,
                                std::ostream* pstream = nullptr) const final {
      transform_inits_impl(context, params_i, vars, pstream);
    }        
};
}
using stan_model = model292eaa50420b56__namespace::model292eaa50420b56_;
#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;
}
stan::math::profile_map& get_stan_profile_data() {
  return model292eaa50420b56__namespace::profiles__;
}
#endif
#endif