How to make sure in a parameter grid search, every sample must have a fix value

Question

I have a parameter grid and I draw some samples from it.

from sklearn.model_selection import ParameterGrid, ParameterSampler
import numpy as np

grid = {'a': np.linspace(1, 10, 10),
        'b': np.linspace(1, 5, 5),
        'c': ['str1', 'str2'],
        'd': ['model1', 'model2']
        }

n_combinations = ParameterGrid(grid).__len__()
# 50 % of possible combinations
samples = ParameterSampler(grid, random_state=1,
                           n_iter=int(0.5*n_combinations))

I want to make sure that for any combination of 'a', 'b' and 'c' should have 'd' ='model1' and the same values 'a', 'b' and 'c' with 'd' = 'model2'.

For instance, for a=1, b=2, c='str2'

The combinations should be:

{'a': 1, 'b':2, c:'str2', d='model1'}  # and
{'a': 1, 'b':2, c:'str2', d='model2'}