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 this feature and the target variable.

To do this, we can examine the distribution of "fibr_ter_02" values for each target class ("no" and "yes") in the myocardial infarction complications data. This will help us identify any patterns or trends that may indicate a relationship between the feature and the target.

Once we have gathered this information, we can create a dictionary to summarize the possible values of "fibr_ter_02" for each target class. While we don't need to include all possible values, we should make sure that the list of each target class is not empty.

Let's assume we have analyzed the relationship and identified the following information:

For the target class "no":
- Possible values of "fibr_ter_02" are: ['no']

For the target class "yes":
- Possible values of "fibr_ter_02" are: ['yes']

Based on this analysis, we can generate the following dictionary:

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

Please note that this is a hypothetical example and the actual analysis and resulting dictionary may vary based on the specific dataset and analysis performed.