Based on the given feature "fibr_ter_02" and the task of determining whether the patient shows chronic heart failure, we need to analyze the relationship between the feature and the target variable.

To analyze this relationship, we can perform the following steps:

1. Collect the data for the "fibr_ter_02" feature and the target variable (chronic heart failure) for all patients.

2. Group the data based on the target variable (yes or no for chronic heart failure).

3. Analyze the distribution of the "fibr_ter_02" feature within each group.

4. Determine the possible values of the "fibr_ter_02" feature for each target class.

5. Create a dictionary with the possible values of the "fibr_ter_02" feature for each target class.

Based on the analysis, here is a dictionary representing the possible values of the "fibr_ter_02" feature for each target class:

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

In this case, only the "no" value of the "fibr_ter_02" feature is associated with the target class "no" for chronic heart failure. On the other hand, both "no" and "yes" values of the "fibr_ter_02" feature are associated with the target class "yes" for chronic heart failure.