Based on prior knowledge, we can analyze the relationship between the feature "IM_PG_P" (Presence of a right ventricular myocardial infarction) and the task of determining whether the patient shows chronic heart failure.

To analyze this relationship, we need data that includes the target variable "chronic heart failure" and the feature "IM_PG_P". By examining the data, we can determine the possible values of "IM_PG_P" for each target class (yes or no).

Based on the given feature description, the possible values of "IM_PG_P" are 'no' and 'yes'. 

To generate the dictionary, we need to look at the values of "IM_PG_P" for each target class ('no' and 'yes').

Let's assume we have the following data:

| IM_PG_P | Chronic Heart Failure |
|---------|----------------------|
| no      | yes                  |
| yes     | yes                  |
| no      | no                   |
| yes     | no                   |
| no      | no                   |

Based on this data, we can create the following dictionary:

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

Note that we only include the values of "IM_PG_P" that appear in the data for each target class. If there are other possible values that don't appear in the data, we don't include them in the dictionary.

In this case, the dictionary indicates that for the target class "no" (chronic heart failure), the possible values of "IM_PG_P" are only "no". For the target class "yes" (chronic heart failure), the possible values of "IM_PG_P" are "yes" and "yes".

Keep in mind that this example is based on hypothetical data and the actual data might result in a different dictionary. Also, note that the values in the dictionary are strings, as specified in the task.