Based on the given task "Does the myocardial infarction complications data of this patient show chronic heart failure?" and the feature "fibr_ter_02, Fibrinolytic therapy by Celiasum 1m IU", we can analyze the relationship between the feature and the target variable "chronic heart failure (CHF)".

To analyze the relationship, we can examine the distribution of the feature values for each target class.

Here is a dictionary summarizing the relationship between the feature "fibr_ter_02" and the target variable "chronic heart failure":

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

Explanation:
- For patients without chronic heart failure (no), the possible value of the feature "fibr_ter_02" is "no".
- For patients with chronic heart failure (yes), the possible value of the feature "fibr_ter_02" is "yes".

Since the feature "fibr_ter_02" is a categorical variable with two distinct categories ('no' and 'yes'), we can directly map each category to the respective target class.

Note: If there are other possible values for the feature "fibr_ter_02" that are hard to predict, they can be excluded from the dictionary. However, in this case, the values 'no' and 'yes' are the only relevant categories, hence they are included in the dictionary.