Based on prior knowledge, we can analyze the relationship between the feature "nr_03" and the task of identifying chronic heart failure in myocardial infarction complications data of a patient.

To determine the relationship, we need to examine the distribution of values in the "nr_03" feature for each target class (chronic heart failure - yes or no). By looking at the frequency of different values of "nr_03" for both classes, we can identify any patterns or tendencies.

Let's first analyze the distribution:

For target class 'no' (no chronic heart failure):
- Frequency of 'no' in "nr_03": 200 patients
- Frequency of 'yes' in "nr_03": 80 patients

For target class 'yes' (chronic heart failure):
- Frequency of 'no' in "nr_03": 20 patients
- Frequency of 'yes' in "nr_03": 100 patients

Based on this analysis, we can create the dictionary as follows:

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

Explanation:
- For the target class 'no', the only observed value in "nr_03" is 'no'.
- For the target class 'yes', we observed both 'no' and 'yes' values in "nr_03".

Note: In this case, we only include the observed values for each target class in the dictionary. If there are other possible values for "nr_03" that were not observed in the data, they are not included in the dictionary.