what interface are you using, and what version of Stan?

I did a cut-and-paste on the above program into file `foo.stan`

and then compiled and ran it on CmdStan 2.28.1 with the following data file:

```
{
"x" : 1.0,
"y" : 7.0,
"mu_mean" : 5.0,
"sigma" : 2.0
}
```

it all just worked.

```
~/.cmdstan/cmdstan-2.28.1> ./foo sample data file=foo.data.json
method = sample (Default)
sample
num_samples = 1000 (Default)
num_warmup = 1000 (Default)
save_warmup = 0 (Default)
thin = 1 (Default)
adapt
engaged = 1 (Default)
gamma = 0.050000000000000003 (Default)
delta = 0.80000000000000004 (Default)
kappa = 0.75 (Default)
t0 = 10 (Default)
init_buffer = 75 (Default)
term_buffer = 50 (Default)
window = 25 (Default)
algorithm = hmc (Default)
hmc
engine = nuts (Default)
nuts
max_depth = 10 (Default)
metric = diag_e (Default)
metric_file = (Default)
stepsize = 1 (Default)
stepsize_jitter = 0 (Default)
num_chains = 1 (Default)
id = 1 (Default)
data
file = foo.data.json
init = 2 (Default)
random
seed = 1686177967 (Default)
output
file = output.csv (Default)
diagnostic_file = (Default)
refresh = 100 (Default)
sig_figs = -1 (Default)
profile_file = profile.csv (Default)
num_threads = 1 (Default)
Gradient evaluation took 8e-06 seconds
1000 transitions using 10 leapfrog steps per transition would take 0.08 seconds.
Adjust your expectations accordingly!
Iteration: 1 / 2000 [ 0%] (Warmup)
Iteration: 100 / 2000 [ 5%] (Warmup)
Iteration: 200 / 2000 [ 10%] (Warmup)
Iteration: 300 / 2000 [ 15%] (Warmup)
Iteration: 400 / 2000 [ 20%] (Warmup)
Iteration: 500 / 2000 [ 25%] (Warmup)
Iteration: 600 / 2000 [ 30%] (Warmup)
Iteration: 700 / 2000 [ 35%] (Warmup)
Iteration: 800 / 2000 [ 40%] (Warmup)
Iteration: 900 / 2000 [ 45%] (Warmup)
Iteration: 1000 / 2000 [ 50%] (Warmup)
Iteration: 1001 / 2000 [ 50%] (Sampling)
Iteration: 1100 / 2000 [ 55%] (Sampling)
Iteration: 1200 / 2000 [ 60%] (Sampling)
Iteration: 1300 / 2000 [ 65%] (Sampling)
Iteration: 1400 / 2000 [ 70%] (Sampling)
Iteration: 1500 / 2000 [ 75%] (Sampling)
Iteration: 1600 / 2000 [ 80%] (Sampling)
Iteration: 1700 / 2000 [ 85%] (Sampling)
Iteration: 1800 / 2000 [ 90%] (Sampling)
Iteration: 1900 / 2000 [ 95%] (Sampling)
Iteration: 2000 / 2000 [100%] (Sampling)
Elapsed Time: 0.004 seconds (Warm-up)
0.011 seconds (Sampling)
0.015 seconds (Total)
~/.cmdstan/cmdstan-2.28.1> bin/stansummary output.csv
Inference for Stan model: foo_model
1 chains: each with iter=(1000); warmup=(0); thin=(1); 1000 iterations saved.
Warmup took 0.0040 seconds
Sampling took 0.011 seconds
Mean MCSE StdDev 5% 50% 95% N_Eff N_Eff/s R_hat
lp__ -0.89 0.038 0.73 -2.4 -0.59 -0.40 382 34721 1.00
accept_stat__ 0.94 2.8e-03 8.9e-02 0.75 0.98 1.0 1.0e+03 9.2e+04 1.0e+00
stepsize__ 0.94 nan 6.0e-15 0.94 0.94 0.94 nan nan nan
treedepth__ 1.4 1.6e-02 4.9e-01 1.0 1.0 2.0 1.0e+03 9.1e+04 1.0e+00
n_leapfrog__ 2.9 5.8e-02 1.8e+00 1.0 3.0 7.0 9.4e+02 8.5e+04 1.0e+00
divergent__ 0.00 nan 0.0e+00 0.00 0.00 0.00 nan nan nan
energy__ 1.4 5.1e-02 1.0e+00 0.46 1.0 3.5 4.2e+02 3.8e+04 1.0e+00
mu 5.4 0.042 0.88 4.0 5.4 6.9 442 40159 1.0
Samples were drawn using hmc with nuts.
For each parameter, N_Eff is a crude measure of effective sample size,
and R_hat is the potential scale reduction factor on split chains (at
convergence, R_hat=1).
```