--------------------------------------------------------------------------- TypeError Traceback (most recent call last) Cell In[9], line 13 9 obj = check_parameters(x, theta > 0, msg="theta > 0") 11 x0, _ = pytensor.tensor.optimize.minimize(objective=obj, x=x) ---> 13 tg.grad(x0, theta) File ~/Michal_Linux/git/GSoC/pymc-extras/.pixi/envs/default/lib/python3.12/site-packages/pytensor/gradient.py:746, in grad(cost, wrt, consider_constant, disconnected_inputs, add_names, known_grads, return_disconnected, null_gradients) 743 if hasattr(g.type, "dtype"): 744 assert g.type.dtype in pytensor.tensor.type.float_dtypes --> 746 _rval: Sequence[Variable] = _populate_grad_dict( 747 var_to_app_to_idx, grad_dict, _wrt, cost_name 748 ) 750 rval: MutableSequence[Variable | None] = list(_rval) 752 for i in range(len(_rval)): File ~/Michal_Linux/git/GSoC/pymc-extras/.pixi/envs/default/lib/python3.12/site-packages/pytensor/gradient.py:1540, in _populate_grad_dict(var_to_app_to_idx, grad_dict, wrt, cost_name) 1537 # end if cache miss 1538 return grad_dict[var] -> 1540 rval = [access_grad_cache(elem) for elem in wrt] 1542 return rval File ~/Michal_Linux/git/GSoC/pymc-extras/.pixi/envs/default/lib/python3.12/site-packages/pytensor/gradient.py:1495, in _populate_grad_dict.<locals>.access_grad_cache(var) 1493 for node in node_to_idx: 1494 for idx in node_to_idx[node]: -> 1495 term = access_term_cache(node)[idx] 1497 if not isinstance(term, Variable): 1498 raise TypeError( 1499 f"{node.op}.grad returned {type(term)}, expected" 1500 " Variable instance." 1501 ) File ~/Michal_Linux/git/GSoC/pymc-extras/.pixi/envs/default/lib/python3.12/site-packages/pytensor/gradient.py:1170, in _populate_grad_dict.<locals>.access_term_cache(node) 1167 if node not in term_dict: 1168 inputs = node.inputs -> 1170 output_grads = [access_grad_cache(var) for var in node.outputs] 1172 # list of bools indicating if each output is connected to the cost 1173 outputs_connected = [ 1174 not isinstance(g.type, DisconnectedType) for g in output_grads 1175 ] File ~/Michal_Linux/git/GSoC/pymc-extras/.pixi/envs/default/lib/python3.12/site-packages/pytensor/gradient.py:1495, in _populate_grad_dict.<locals>.access_grad_cache(var) 1493 for node in node_to_idx: 1494 for idx in node_to_idx[node]: -> 1495 term = access_term_cache(node)[idx] 1497 if not isinstance(term, Variable): 1498 raise TypeError( 1499 f"{node.op}.grad returned {type(term)}, expected" 1500 " Variable instance." 1501 ) File ~/Michal_Linux/git/GSoC/pymc-extras/.pixi/envs/default/lib/python3.12/site-packages/pytensor/gradient.py:1170, in _populate_grad_dict.<locals>.access_term_cache(node) 1167 if node not in term_dict: 1168 inputs = node.inputs -> 1170 output_grads = [access_grad_cache(var) for var in node.outputs] 1172 # list of bools indicating if each output is connected to the cost 1173 outputs_connected = [ 1174 not isinstance(g.type, DisconnectedType) for g in output_grads 1175 ] [... skipping similar frames: _populate_grad_dict.<locals>.access_grad_cache at line 1495 (2 times), _populate_grad_dict.<locals>.access_term_cache at line 1170 (1 times)] File ~/Michal_Linux/git/GSoC/pymc-extras/.pixi/envs/default/lib/python3.12/site-packages/pytensor/gradient.py:1170, in _populate_grad_dict.<locals>.access_term_cache(node) 1167 if node not in term_dict: 1168 inputs = node.inputs -> 1170 output_grads = [access_grad_cache(var) for var in node.outputs] 1172 # list of bools indicating if each output is connected to the cost 1173 outputs_connected = [ 1174 not isinstance(g.type, DisconnectedType) for g in output_grads 1175 ] File ~/Michal_Linux/git/GSoC/pymc-extras/.pixi/envs/default/lib/python3.12/site-packages/pytensor/gradient.py:1495, in _populate_grad_dict.<locals>.access_grad_cache(var) 1493 for node in node_to_idx: 1494 for idx in node_to_idx[node]: -> 1495 term = access_term_cache(node)[idx] 1497 if not isinstance(term, Variable): 1498 raise TypeError( 1499 f"{node.op}.grad returned {type(term)}, expected" 1500 " Variable instance." 1501 ) File ~/Michal_Linux/git/GSoC/pymc-extras/.pixi/envs/default/lib/python3.12/site-packages/pytensor/gradient.py:1325, in _populate_grad_dict.<locals>.access_term_cache(node) 1317 if o_shape != g_shape: 1318 raise ValueError( 1319 "Got a gradient of shape " 1320 + str(o_shape) 1321 + " on an output of shape " 1322 + str(g_shape) 1323 ) -> 1325 input_grads = node.op.L_op(inputs, node.outputs, new_output_grads) 1327 if input_grads is None: 1328 raise TypeError( 1329 f"{node.op}.grad returned NoneType, expected iterable." 1330 ) File ~/Michal_Linux/git/GSoC/pymc-extras/.pixi/envs/default/lib/python3.12/site-packages/pytensor/graph/op.py:399, in Op.L_op(self, inputs, outputs, output_grads) 372 def L_op( 373 self, 374 inputs: Sequence[Variable], 375 outputs: Sequence[Variable], 376 output_grads: Sequence[Variable], 377 ) -> list[Variable]: 378 r"""Construct a graph for the L-operator.  379   380 The L-operator computes a row vector times the Jacobian.  (...) 397   398 """ --> 399 return self.grad(inputs, output_grads) File ~/Michal_Linux/git/GSoC/pymc-extras/.pixi/envs/default/lib/python3.12/site-packages/pytensor/tensor/basic.py:696, in ScalarFromTensor.grad(self, inp, grads) 694 (_s,) = inp 695 (dt,) = grads --> 696 return [tensor_from_scalar(dt)] File ~/Michal_Linux/git/GSoC/pymc-extras/.pixi/envs/default/lib/python3.12/site-packages/pytensor/graph/op.py:294, in Op.__call__(self, name, return_list, *inputs, **kwargs) 250 def __call__( 251 self, *inputs: Any, name=None, return_list=False, **kwargs 252 ) -> Variable | list[Variable]: 253 r"""Construct an `Apply` node using :meth:`Op.make_node` and return its outputs.  254   255 This method is just a wrapper around :meth:`Op.make_node`.  (...) 292   293 """ --> 294 node = self.make_node(*inputs, **kwargs) 295 if name is not None: 296 if len(node.outputs) == 1: File ~/Michal_Linux/git/GSoC/pymc-extras/.pixi/envs/default/lib/python3.12/site-packages/pytensor/tensor/basic.py:620, in TensorFromScalar.make_node(self, s) 618 def make_node(self, s): 619 if not isinstance(s.type, ps.ScalarType): --> 620 raise TypeError("Input must be a `ScalarType` `Type`") 622 return Apply(self, [s], [tensor(dtype=s.type.dtype, shape=())]) TypeError: Input must be a `ScalarType` `Type`