Based on the given feature "GT_POST" and the task of determining whether the myocardial infarction complications data of this patient shows chronic heart failure, we can analyze the relationship between the feature and the target variable.

In this case, the feature "GT_POST" represents the presence or absence of paroxysms of ventricular tachycardia at the time of admission to the intensive care unit. To determine the relationship with chronic heart failure, we need information on each category of "GT_POST" (no and yes) for both target classes (no chronic heart failure and chronic heart failure).

Without further information about the data, it is difficult to predict specific values of "GT_POST" for each target class. However, we can create a dictionary of possible values for each class based on common assumptions:

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

In this case, we assume that the presence of ventricular tachycardia (yes) would be more likely to be associated with chronic heart failure. However, without additional data or analysis, it is difficult to make a definitive determination.

The above dictionary provides possible values for each target class. If there are specific values that are hard to predict based on prior knowledge, they can be excluded from the dictionary. However, each list should not be empty to account for all possible outcomes.