Is there a way to specify the number of MCMC iterations?

Hello! For benchmarking purposes, is there a way to use MCMC such that we specify the number of MCMC iterations, rather than the number of desired accepted samples? I’ve tried setting the target_accept_prob field of the NUTS kernel to 0 so that all samples are accepted, but this doesn’t seem to have the right effect.

Here is what i have at the moment:

import pyro
import pyro.poutine as poutine
import pyro.distributions as dist
import torch as torch
from pyro.infer import MCMC, NUTS

def linRegr(x):
  mu = pyro.sample('mu', dist.Normal(0, 3))
  c  = pyro.sample('c', dist.Normal(0, 5))
  σ  = pyro.sample('σ', dist.Uniform(1, 3))
  y  = pyro.sample('y', dist.Normal(mu * x + c, σ))
  return y

def mhLinRegrXYS(n_samples):
  xs = torch.tensor(list(range(0, 100)))
  ys = torch.tensor(list(map (lambda x: (x*3) + 4, xs)))
  conditioned_model = poutine.condition(linRegr, data={'y': torch.tensor(ys)})
  nuts_kernel = NUTS(conditioned_model, target_accept_prob=0)
  mcmc = MCMC(
  samples = mcmc.get_samples()

Thanks a lot.