[BUG] Batch fitness does not work with pygmo.unconstrain()

**Describe the bug**

Hi!

Thank you for your work on this library. It works very well and is blazing fast 😊

It seems like the combination of:

1. Trying to perform batch fitness evaluation on a problem through the method `batch_fitness`
2. With an algorithm capable of using batch fitness (the ones with method `.set_bfe()` defined)
3. In a constrained problem turned into unconstrained through `[pygmo.unconstrain](https://esa.github.io/pygmo2/problems.html#pygmo.unconstrain)`

Does not work and throws an exception that comes from the C++ code.

**To Reproduce**

Here's a simple reproducible example:

```python
import numpy as np
import pygmo as pg

# Define problem:
class MyProblem:

def fitness(self, x):

feval_res = x[0] - x[1]**2

# Inequality constraints: x[0] + x[1] <=10
inequality_constraints = x[0] + x[1] - 10

# Return:
return np.array([feval_res, inequality_constraints])

def batch_fitness(self, x):

x_matrix = x.reshape(-1, 2)

feval_res = x_matrix[:,0] - x_matrix[:,1]**2

inequality_constraints = x_matrix[:,0] + x_matrix[:,1] - 10

return np.hstack([feval_res.reshape(-1,1),
inequality_constraints.reshape(-1,1)
]).reshape(-1)

def has_batch_fitness(self):
return True

def get_nobj(self):
return 1

def get_nec(self):
return 0

def get_nic(self):
return 1

def get_bounds(self):
return np.array([[0, 2],
[8, 9]])

problem = MyProblem()

# Define algo which supports
# batch_fitness:
alg = pg.cmaes(gen=50,
ftol=-1.0,
xtol=-1.0,
sigma0=0.1,
force_bounds=True,
seed=42)
algo = pg.algorithm(alg)

# Turn constrained problem into
# unconstrained one:
unconstrained_problem = pg.unconstrain(problem, method="death penalty")

# Create population
pop = pg.population(unconstrained_problem, size=500, seed=42)

# Evolve it. Non-batch works
# fine:
pop = algo.evolve(pop)

# Now batch:
batch_evaluator = pg.bfe()
alg.set_bfe(batch_evaluator)

algo = pg.algorithm(alg)

# Re-create population:
pop = pg.population(unconstrained_problem, size=500, seed=42)

# Evolve it. But batch fitness
# fails:
pop = algo.evolve(pop)
```

Running the code above fails with:

```python
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[23], line 12
8 pop = pg.population(unconstrained_problem, size=500, seed=42)
10 # Evolve it. But batch fitness
11 # fails:
---> 12 pop = algo.evolve(pop)

ValueError:
function: feasibility_f
where: D:\bld\pagmo_1705775542311\work\src\problem.cpp, 714
what: The fitness passed as argument has dimension of: 1, while the problem defines a fitness size of: 2
```

It seems that for some reason the fitness passed to `feasibility_f` is incorrect.

**Expected behavior**
No exception should be thrown,

**Environment (please complete the following information):**
- OS: all
- Installation method: conda
- Version: `2.19.6`

Thank you!