Based on the given feature description, we want to analyze the relationship between the feature "nr_01" (Premature atrial contractions in the anamnesis) and the presence of chronic heart failure in myocardial infarction complications.

To analyze this relationship, we need data that includes both the feature "nr_01" and the target variable indicating the presence or absence of chronic heart failure. Without the actual data, it is difficult to determine the specific values of "nr_01" that correspond to each class of the target variable.

However, we can make a general analysis based on prior knowledge and assumptions. Premature atrial contractions refer to abnormal contractions originating from the atria of the heart. While they can occur in healthy individuals, they may also indicate underlying heart conditions.

If we assume that "nr_01" being "yes" indicates a higher likelihood of chronic heart failure, we can deduce that the possible values of "nr_01" for the target class "no" would most likely be "no" (indicating the absence of premature atrial contractions). On the other hand, the possible values of "nr_01" for the target class "yes" could include "yes" (indicating the presence of premature atrial contractions).

Considering these assumptions, we can create a dictionary with the following format:

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

Please note that without actual data, it is difficult to provide a more accurate analysis and determine the exact values of "nr_01" for each target class. The provided solution is based on general assumptions and prior knowledge.