Based on prior knowledge, we can analyze the relationship between the feature "nr_03" (Paroxysms of atrial fibrillation in the anamnesis) and the target variable "chronic heart failure."

There are two possible categories for the feature "nr_03," which are "no" and "yes." We need to determine how these categories relate to the presence of chronic heart failure.

To perform this analysis, we would need a dataset that contains information about myocardial infarction complications and chronic heart failure for multiple patients. This dataset would allow us to examine the relationship between the feature "nr_03" and the target variable "chronic heart failure."

Without access to such a dataset, we cannot definitively determine the relationship between the feature "nr_03" and the presence of chronic heart failure.

However, we can still create a dictionary to summarize the possible values of feature "nr_03" for both the "no" and "yes" classes of the target variable.

Based on the information provided, the dictionary would have the following format:

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

Note that we only include the possible values of the feature "nr_03" that are mentioned in the data. If there are other possible values not mentioned, we would not include them in the dictionary to ensure that the lists of each target class are not empty.