```json
{
    "no": ["no"],
    "yes": ["yes"]
}
```
Based on the given feature "n_r_ecg_p_01" which represents "Premature atrial contractions on ECG at the time of admission to hospital", it is a categorical variable with two categories: "no" and "yes". 

To analyze the relationship between this feature and the task of predicting chronic heart failure, we can observe that the feature represents the presence or absence of premature atrial contractions on the electrocardiogram at the time of admission to the hospital. 

We can infer that the presence of premature atrial contractions on the ECG may indicate a higher likelihood of chronic heart failure, while the absence of premature atrial contractions may indicate a lower likelihood.

In the provided data, there are only two categories: "no" and "yes". So, for the target class "no" (indicating no chronic heart failure), the only possible value of feature "n_r_ecg_p_01" is "no". Similarly, for the target class "yes" (indicating chronic heart failure), the only possible value of feature "n_r_ecg_p_01" is "yes".

Hence, the dictionary representation of the relationship between the feature "n_r_ecg_p_01" and chronic heart failure is as follows:

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