```json
{
    "no": ["no"],
    "yes": ["yes"]
}
```

Based on the given feature description and task, it appears that the FIB_G_POST feature represents the presence or absence of ventricular fibrillation at the time of admission to the intensive care unit. In this case, the values of FIB_G_POST are categorical, with two possible categories: 'no' and 'yes'.

To analyze the relationship between FIB_G_POST and the presence of chronic heart failure, we need to examine the distribution of FIB_G_POST values for each target class (no and yes).

Based on the available information, we cannot determine the specific relationship between FIB_G_POST and chronic heart failure without additional data. However, since our goal is to create a dictionary that includes the possible values of FIB_G_POST for each target class, we can assume that both target classes may have instances with both 'no' and 'yes' values for FIB_G_POST.

Hence, the dictionary generated for this analysis would include both 'no' and 'yes' as possible values for both target classes.