Based on prior knowledge, we can analyze the relationship between the feature "nr_01" (Premature atrial contractions in the anamnesis) and the target variable "chronic heart failure". 

Since the feature "nr_01" is categorical with two categories - "no" and "yes", we need to examine how the presence or absence of premature atrial contractions in the anamnesis relates to chronic heart failure.

To analyze this relationship, we would need access to data that includes both the feature "nr_01" and the target variable "chronic heart failure" for a set of patients. With this data, we can examine the distribution of "nr_01" values for each target class and determine any patterns or correlations.

Based on the analysis, we can then create a dictionary summarizing the relationship between the feature "nr_01" and the target variable "chronic heart failure". The dictionary will include lists of possible values of the "nr_01" feature for each target class.

For example, if the analysis reveals that patients with chronic heart failure are more likely to have premature atrial contractions in the anamnesis, the resulting dictionary could be:

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

This indicates that for patients with chronic heart failure, the "nr_01" feature can have values "no" or "yes". For patients without chronic heart failure, the "nr_01" feature only has the value "no".

However, without access to actual data, it is not possible to provide a definitive analysis and create an accurate dictionary. The analysis and dictionary creation are dependent on the specific data and patterns observed.