Based on prior knowledge and analysis, we can determine the relationship between the feature "K_SH_POST" and the task of identifying chronic heart failure in the patient's myocardial infarction complications data.

To generate the dictionary, we need to examine the different possible values of the feature "K_SH_POST" for each target class.

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

In this case, since "K_SH_POST" is a categorical variable with categories "yes" and "no," the possible values for the target class "no" and "yes" are the same as the categories of the feature "K_SH_POST."

Please note that in this example, the possible values for both target classes are the same as the categories of the feature. If there were additional categories for "K_SH_POST" that were hard to predict or didn't have any occurrences in either target class, we would exclude them from the dictionary. However, in this case, both "no" and "yes" categories are included in the dictionary.