Thanks for your hint to freeze_data_and_dims! Unfortunately, for numpyro or blackjax I’m still getting the same error messages as above.
However, nutpie is now running when I use freeze_data_and_dims! 
The results are looking not so good - but this could also be caused by the model structure, which I could maybe further improve.
Without freeze_data_and_dims I get the following error:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[33], line 4
1 with model_observed:
2 # idata.extend(pm.sample(nuts_sampler='numpyro'))
3 # idata.extend(pm.sample(nuts_sampler='blackjax'))
----> 4 idata.extend(pm.sample(nuts_sampler='nutpie'))
5 # idata.extend(pm.sample(nuts_sampler='pymc'))
6 idata
File /opt/conda/lib/python3.12/site-packages/pymc/sampling/mcmc.py:725, 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, **kwargs)
720 raise ValueError(
721 "Model can not be sampled with NUTS alone. Your model is probably not continuous."
722 )
724 with joined_blas_limiter():
--> 725 return _sample_external_nuts(
726 sampler=nuts_sampler,
727 draws=draws,
728 tune=tune,
729 chains=chains,
730 target_accept=kwargs.pop("nuts", {}).get("target_accept", 0.8),
731 random_seed=random_seed,
732 initvals=initvals,
733 model=model,
734 var_names=var_names,
735 progressbar=progressbar,
736 idata_kwargs=idata_kwargs,
737 compute_convergence_checks=compute_convergence_checks,
738 nuts_sampler_kwargs=nuts_sampler_kwargs,
739 **kwargs,
740 )
742 if isinstance(step, list):
743 step = CompoundStep(step)
File /opt/conda/lib/python3.12/site-packages/pymc/sampling/mcmc.py:307, in _sample_external_nuts(sampler, draws, tune, chains, target_accept, random_seed, initvals, model, var_names, progressbar, idata_kwargs, compute_convergence_checks, nuts_sampler_kwargs, **kwargs)
302 if var_names is not None:
303 warnings.warn(
304 "`var_names` are currently ignored by the nutpie sampler",
305 UserWarning,
306 )
--> 307 compiled_model = nutpie.compile_pymc_model(model)
308 t_start = time.time()
309 idata = nutpie.sample(
310 compiled_model,
311 draws=draws,
(...)
317 **nuts_sampler_kwargs,
318 )
File /opt/conda/lib/python3.12/site-packages/nutpie/compile_pymc.py:391, in compile_pymc_model(model, backend, gradient_backend, **kwargs)
388 backend = "numba"
390 if backend.lower() == "numba":
--> 391 return _compile_pymc_model_numba(model, **kwargs)
392 elif backend.lower() == "jax":
393 return _compile_pymc_model_jax(
394 model, gradient_backend=gradient_backend, **kwargs
395 )
File /opt/conda/lib/python3.12/site-packages/nutpie/compile_pymc.py:207, in _compile_pymc_model_numba(model, **kwargs)
200 with warnings.catch_warnings():
201 warnings.filterwarnings(
202 "ignore",
203 message="Cannot cache compiled function .* as it uses dynamic globals",
204 category=numba.NumbaWarning,
205 )
--> 207 logp_numba = numba.cfunc(c_sig, **kwargs)(logp_numba_raw)
209 expand_shared_names = [var.name for var in expand_fn_pt.get_shared()]
210 expand_numba_raw, c_sig_expand = _make_c_expand_func(
211 n_dim, n_expanded, expand_fn, user_data, expand_shared_names, shared_data
212 )
File /opt/conda/lib/python3.12/site-packages/numba/core/decorators.py:275, in cfunc.<locals>.wrapper(func)
273 if cache:
274 res.enable_caching()
--> 275 res.compile()
276 return res
File /opt/conda/lib/python3.12/site-packages/numba/core/compiler_lock.py:35, in _CompilerLock.__call__.<locals>._acquire_compile_lock(*args, **kwargs)
32 @functools.wraps(func)
33 def _acquire_compile_lock(*args, **kwargs):
34 with self:
---> 35 return func(*args, **kwargs)
File /opt/conda/lib/python3.12/site-packages/numba/core/ccallback.py:68, in CFunc.compile(self)
65 cres = self._cache.load_overload(self._sig,
66 self._targetdescr.target_context)
67 if cres is None:
---> 68 cres = self._compile_uncached()
69 self._cache.save_overload(self._sig, cres)
70 else:
File /opt/conda/lib/python3.12/site-packages/numba/core/ccallback.py:82, in CFunc._compile_uncached(self)
79 sig = self._sig
81 # Compile native function as well as cfunc wrapper
---> 82 return self._compiler.compile(sig.args, sig.return_type)
File /opt/conda/lib/python3.12/site-packages/numba/core/dispatcher.py:80, in _FunctionCompiler.compile(self, args, return_type)
79 def compile(self, args, return_type):
---> 80 status, retval = self._compile_cached(args, return_type)
81 if status:
82 return retval
File /opt/conda/lib/python3.12/site-packages/numba/core/dispatcher.py:94, in _FunctionCompiler._compile_cached(self, args, return_type)
91 pass
93 try:
---> 94 retval = self._compile_core(args, return_type)
95 except errors.TypingError as e:
96 self._failed_cache[key] = e
File /opt/conda/lib/python3.12/site-packages/numba/core/dispatcher.py:107, in _FunctionCompiler._compile_core(self, args, return_type)
104 flags = self._customize_flags(flags)
106 impl = self._get_implementation(args, {})
--> 107 cres = compiler.compile_extra(self.targetdescr.typing_context,
108 self.targetdescr.target_context,
109 impl,
110 args=args, return_type=return_type,
111 flags=flags, locals=self.locals,
112 pipeline_class=self.pipeline_class)
113 # Check typing error if object mode is used
114 if cres.typing_error is not None and not flags.enable_pyobject:
File /opt/conda/lib/python3.12/site-packages/numba/core/compiler.py:744, in compile_extra(typingctx, targetctx, func, args, return_type, flags, locals, library, pipeline_class)
720 """Compiler entry point
721
722 Parameter
(...)
740 compiler pipeline
741 """
742 pipeline = pipeline_class(typingctx, targetctx, library,
743 args, return_type, flags, locals)
--> 744 return pipeline.compile_extra(func)
File /opt/conda/lib/python3.12/site-packages/numba/core/compiler.py:438, in CompilerBase.compile_extra(self, func)
436 self.state.lifted = ()
437 self.state.lifted_from = None
--> 438 return self._compile_bytecode()
File /opt/conda/lib/python3.12/site-packages/numba/core/compiler.py:506, in CompilerBase._compile_bytecode(self)
502 """
503 Populate and run pipeline for bytecode input
504 """
505 assert self.state.func_ir is None
--> 506 return self._compile_core()
File /opt/conda/lib/python3.12/site-packages/numba/core/compiler.py:481, in CompilerBase._compile_core(self)
478 except Exception as e:
479 if (utils.use_new_style_errors() and not
480 isinstance(e, errors.NumbaError)):
--> 481 raise e
483 self.state.status.fail_reason = e
484 if is_final_pipeline:
File /opt/conda/lib/python3.12/site-packages/numba/core/compiler.py:472, in CompilerBase._compile_core(self)
470 res = None
471 try:
--> 472 pm.run(self.state)
473 if self.state.cr is not None:
474 break
File /opt/conda/lib/python3.12/site-packages/numba/core/compiler_machinery.py:364, in PassManager.run(self, state)
361 except Exception as e:
362 if (utils.use_new_style_errors() and not
363 isinstance(e, errors.NumbaError)):
--> 364 raise e
365 msg = "Failed in %s mode pipeline (step: %s)" % \
366 (self.pipeline_name, pass_desc)
367 patched_exception = self._patch_error(msg, e)
File /opt/conda/lib/python3.12/site-packages/numba/core/compiler_machinery.py:356, in PassManager.run(self, state)
354 pass_inst = _pass_registry.get(pss).pass_inst
355 if isinstance(pass_inst, CompilerPass):
--> 356 self._runPass(idx, pass_inst, state)
357 else:
358 raise BaseException("Legacy pass in use")
File /opt/conda/lib/python3.12/site-packages/numba/core/compiler_lock.py:35, in _CompilerLock.__call__.<locals>._acquire_compile_lock(*args, **kwargs)
32 @functools.wraps(func)
33 def _acquire_compile_lock(*args, **kwargs):
34 with self:
---> 35 return func(*args, **kwargs)
File /opt/conda/lib/python3.12/site-packages/numba/core/compiler_machinery.py:311, in PassManager._runPass(self, index, pss, internal_state)
309 mutated |= check(pss.run_initialization, internal_state)
310 with SimpleTimer() as pass_time:
--> 311 mutated |= check(pss.run_pass, internal_state)
312 with SimpleTimer() as finalize_time:
313 mutated |= check(pss.run_finalizer, internal_state)
File /opt/conda/lib/python3.12/site-packages/numba/core/compiler_machinery.py:273, in PassManager._runPass.<locals>.check(func, compiler_state)
272 def check(func, compiler_state):
--> 273 mangled = func(compiler_state)
274 if mangled not in (True, False):
275 msg = ("CompilerPass implementations should return True/False. "
276 "CompilerPass with name '%s' did not.")
File /opt/conda/lib/python3.12/site-packages/numba/core/untyped_passes.py:1731, in LiteralUnroll.run_pass(self, state)
1729 pm.add_pass(RewriteSemanticConstants, "rewrite semantic constants")
1730 pm.finalize()
-> 1731 pm.run(state)
1732 return True
File /opt/conda/lib/python3.12/site-packages/numba/core/compiler_machinery.py:364, in PassManager.run(self, state)
361 except Exception as e:
362 if (utils.use_new_style_errors() and not
363 isinstance(e, errors.NumbaError)):
--> 364 raise e
365 msg = "Failed in %s mode pipeline (step: %s)" % \
366 (self.pipeline_name, pass_desc)
367 patched_exception = self._patch_error(msg, e)
File /opt/conda/lib/python3.12/site-packages/numba/core/compiler_machinery.py:356, in PassManager.run(self, state)
354 pass_inst = _pass_registry.get(pss).pass_inst
355 if isinstance(pass_inst, CompilerPass):
--> 356 self._runPass(idx, pass_inst, state)
357 else:
358 raise BaseException("Legacy pass in use")
File /opt/conda/lib/python3.12/site-packages/numba/core/compiler_lock.py:35, in _CompilerLock.__call__.<locals>._acquire_compile_lock(*args, **kwargs)
32 @functools.wraps(func)
33 def _acquire_compile_lock(*args, **kwargs):
34 with self:
---> 35 return func(*args, **kwargs)
File /opt/conda/lib/python3.12/site-packages/numba/core/compiler_machinery.py:311, in PassManager._runPass(self, index, pss, internal_state)
309 mutated |= check(pss.run_initialization, internal_state)
310 with SimpleTimer() as pass_time:
--> 311 mutated |= check(pss.run_pass, internal_state)
312 with SimpleTimer() as finalize_time:
313 mutated |= check(pss.run_finalizer, internal_state)
File /opt/conda/lib/python3.12/site-packages/numba/core/compiler_machinery.py:273, in PassManager._runPass.<locals>.check(func, compiler_state)
272 def check(func, compiler_state):
--> 273 mangled = func(compiler_state)
274 if mangled not in (True, False):
275 msg = ("CompilerPass implementations should return True/False. "
276 "CompilerPass with name '%s' did not.")
File /opt/conda/lib/python3.12/site-packages/numba/core/typed_passes.py:112, in BaseTypeInference.run_pass(self, state)
106 """
107 Type inference and legalization
108 """
109 with fallback_context(state, 'Function "%s" failed type inference'
110 % (state.func_id.func_name,)):
111 # Type inference
--> 112 typemap, return_type, calltypes, errs = type_inference_stage(
113 state.typingctx,
114 state.targetctx,
115 state.func_ir,
116 state.args,
117 state.return_type,
118 state.locals,
119 raise_errors=self._raise_errors)
120 state.typemap = typemap
121 # save errors in case of partial typing
File /opt/conda/lib/python3.12/site-packages/numba/core/typed_passes.py:93, in type_inference_stage(typingctx, targetctx, interp, args, return_type, locals, raise_errors)
91 infer.build_constraint()
92 # return errors in case of partial typing
---> 93 errs = infer.propagate(raise_errors=raise_errors)
94 typemap, restype, calltypes = infer.unify(raise_errors=raise_errors)
96 return _TypingResults(typemap, restype, calltypes, errs)
File /opt/conda/lib/python3.12/site-packages/numba/core/typeinfer.py:1083, in TypeInferer.propagate(self, raise_errors)
1080 oldtoken = newtoken
1081 # Errors can appear when the type set is incomplete; only
1082 # raise them when there is no progress anymore.
-> 1083 errors = self.constraints.propagate(self)
1084 newtoken = self.get_state_token()
1085 self.debug.propagate_finished()
File /opt/conda/lib/python3.12/site-packages/numba/core/typeinfer.py:182, in ConstraintNetwork.propagate(self, typeinfer)
180 errors.append(utils.chain_exception(new_exc, e))
181 elif utils.use_new_style_errors():
--> 182 raise e
183 else:
184 msg = ("Unknown CAPTURED_ERRORS style: "
185 f"'{config.CAPTURED_ERRORS}'.")
File /opt/conda/lib/python3.12/site-packages/numba/core/typeinfer.py:160, in ConstraintNetwork.propagate(self, typeinfer)
157 with typeinfer.warnings.catch_warnings(filename=loc.filename,
158 lineno=loc.line):
159 try:
--> 160 constraint(typeinfer)
161 except ForceLiteralArg as e:
162 errors.append(e)
File /opt/conda/lib/python3.12/site-packages/numba/core/typeinfer.py:583, in CallConstraint.__call__(self, typeinfer)
581 fnty = typevars[self.func].getone()
582 with new_error_context("resolving callee type: {0}", fnty):
--> 583 self.resolve(typeinfer, typevars, fnty)
File /opt/conda/lib/python3.12/site-packages/numba/core/typeinfer.py:606, in CallConstraint.resolve(self, typeinfer, typevars, fnty)
604 fnty = fnty.instance_type
605 try:
--> 606 sig = typeinfer.resolve_call(fnty, pos_args, kw_args)
...
AttributeError: module 'numpy' has no attribute 'bool'.
`np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations