importpymcaspmwithpm.Model()asm:x=pm.Normal("x",mu=0,sigma=1e6)y=pm.Normal.dist(x,shape=(5,))y_sum=pm.Deterministic("y_sum",pm.math.sum(y))withpm.observe(m, {"y_sum":2.0}):trace=pm.sample()

---------------------------------------------------------------------------RuntimeError                              Traceback (most recent call last)Cell In[18], line 3      1#%%      2 with pm.observe(m, {"y_sum": 2.0}):----> 3     trace = pm.sample(nuts_sampler='nutpie')File d:\code\examples\pymc0\.venv\Lib\site-packages\pymc\sampling\mcmc.py:782,in sample(draws, tune, chains, cores, random_seed, progressbar, progressbar_theme, step, var_names, nuts_sampler, initvals, init, jitter_max_retries, n_init, trace, discard_tuned_samples, compute_convergence_checks, keep_warning_stat, return_inferencedata, idata_kwargs, nuts_sampler_kwargs, callback, mp_ctx, blas_cores, model, compile_kwargs,**kwargs)    779     msg = f"Only {draws} samples per chain. Reliable r-hat and ESS diagnostics require longer chains for accurate estimate."    780     _log.warning(msg)--> 782 provided_steps, selected_steps = assign_step_methods(model, step, methods=pm.STEP_METHODS)    783 exclusive_nuts = (    784# User provided an instantiated NUTS step, and nothing else is needed    785     (not selected_steps and len(provided_steps) == 1 and isinstance(provided_steps[0], NUTS))   (...)    792     )    793 )    795if nuts_sampler!="pymc":File d:\code\examples\pymc0\.venv\Lib\site-packages\pymc\sampling\mcmc.py:245,in assign_step_methods(model, step, methods)    243 methods_list: list[type[BlockedStep]]= list(methods or pm.STEP_METHODS)    244 selected_steps: dict[type[BlockedStep], list]= {}--> 245 model_logp=model.logp()    247forvarin model.value_vars:    248if var notin assigned_vars:    249# determine if a gradient can be computedFile d:\code\examples\pymc0\.venv\Lib\site-packages\pymc\model\core.py:714,in Model.logp(self, vars, jacobian, sum)    712 rv_logps: list[TensorVariable] = []    713if rvs:--> 714     rv_logps = transformed_conditional_logp(    715         rvs=rvs,    716         rvs_to_values=self.rvs_to_values,    717         rvs_to_transforms=self.rvs_to_transforms,    718         jacobian=jacobian,    719     )    720     assert isinstance(rv_logps, list)    722# Replace random variables by their value variables in potential termsFile d:\code\examples\pymc0\.venv\Lib\site-packages\pymc\logprob\basic.py:574,in transformed_conditional_logp(rvs, rvs_to_values, rvs_to_transforms, jacobian,**kwargs)    571     transform_rewrite = TransformValuesRewrite(values_to_transforms)# type: ignore[arg-type]    573 kwargs.setdefault("warn_rvs", False)--> 574 temp_logp_terms = conditional_logp(    575     rvs_to_values,    576     extra_rewrites=transform_rewrite,    577     use_jacobian=jacobian,    578**kwargs,    579 )    581# The function returns the logp for every single value term we provided to it.    582# This includes the extra values we plugged in above, so we filter those we    583# actually wanted in the same order they were given in.    584 logp_terms = {}File d:\code\examples\pymc0\.venv\Lib\site-packages\pymc\logprob\basic.py:531,in conditional_logp(rv_values, warn_rvs, ir_rewriter, extra_rewrites,**kwargs)    529 missing_value_terms = set(original_values) - set(values_to_logprobs)    530if missing_value_terms:--> 531     raise RuntimeError(    532         f"The logprob terms of the following value variables could not be derived: {missing_value_terms}"    533     )    535# Ensure same order as input    536 logprobs = cleanup_ir(tuple(values_to_logprobs[v]forvin original_values))RuntimeError: The logprob terms of the following value variables could not be derived: {TensorConstant(TensorType(float64, shape=()), data=array(2.))}