Based on prior knowledge, there isn't a direct correlation between premature atrial contractions (feature nr_01) and chronic heart failure (target). However, it is still worthwhile to analyze the relationship between these two variables.

To generate the dictionary, we need to identify the possible values of feature nr_01 for each target class. We will analyze the distribution of these values for patients with and without chronic heart failure.

Assuming we have a dataset containing the feature nr_01, the target variable (chronic heart failure), and other relevant variables for a set of patients, we can perform the necessary analysis by following these steps:

1. Filter the dataset to include only the patients with the myocardial infarction complications data.
2. Split the dataset into two groups based on the presence or absence of chronic heart failure.
3. For each group, create a list of unique values of feature nr_01.

Let's assume we have performed this analysis and obtained the following results:

For patients without chronic heart failure:
- nr_01: ['no', 'yes']

For patients with chronic heart failure:
- nr_01: ['no', 'yes']

Based on the above result, we conclude that the possible values of feature nr_01 for both target classes (chronic heart failure, no chronic heart failure) are ['no', 'yes']. 

Therefore, the dictionary representing the relationship between feature nr_01 and the target variable "Does the myocardial infarction complications data of this patient show chronic heart failure?" would be:

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

Please note that the order of values within each list can be different depending on the dataset used for analysis, but all the possible values must be included.