Adding comment about squeeze

sbi/inference/posteriors/mcmc_posterior.py

@@ -352,6 +352,9 @@ def sample(
  raise NameError(f"The sampling method {method} is not implemented!")
 
  samples = self.theta_transform.inv(transformed_samples)
+ # NOTE: Currently MCMCPosteriors will require a single dimension for the
+ # parameter dimension. With recent ConditionalDensity(Ratio) estimators, we
+ # can have multiple dimensions for the parameter dimension.
  samples = samples.reshape((*sample_shape, -1)) # type: ignore
 
  return samples
@@ -591,11 +594,7 @@ def _slice_np_mcmc(
 
  def multi_obs_potential(params):
  # Params are of shape (num_chains * num_obs, event).
- # We now reshape them to (num_chains, num_obs, event).
- # params = np.reshape(params, (num_chains, num_obs, -1))
-
- # `all_potentials` is of shape (num_chains, num_obs).
- all_potentials = potential_function(params)
+ all_potentials = potential_function(params) # Shape: (num_chains, num_obs)
  return all_potentials.flatten()
 
  posterior_sampler = SliceSamplerMultiChain(