Based on prior knowledge, we can analyze the relationship between the feature "n_r_ecg_p_02" and the task of determining if the myocardial infarction complications data of this patient show chronic heart failure.

Given that "n_r_ecg_p_02" represents frequent premature atrial contractions on an electrocardiogram (ECG) at the time of admission to the hospital, we can consider the following:

1. "n_r_ecg_p_02" has two possible categories: 'no' and 'yes'.
2. If the value of "n_r_ecg_p_02" is 'no', it indicates the absence of frequent premature atrial contractions on the ECG at the time of admission.
3. If the value of "n_r_ecg_p_02" is 'yes', it indicates the presence of frequent premature atrial contractions on the ECG at the time of admission.

To generate the dictionary, let's analyze the possible values of "n_r_ecg_p_02" for each target class:

Target class 'no':
- 'no': This indicates the absence of frequent premature atrial contractions on the ECG at the time of admission.

Target class 'yes':
- 'yes': This indicates the presence of frequent premature atrial contractions on the ECG at the time of admission.

Based on this analysis, the dictionary representing the relationship between the feature "n_r_ecg_p_02" and the task can be generated as follows:

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

Please note that the list for each target class contains a single value, representing the possible values of the feature "n_r_ecg_p_02" for that class.