Based on prior knowledge, the feature "ant_im" represents the presence of an anterior myocardial infarction with specific changes in ECG leads V1-V4. To determine whether this patient shows chronic heart failure, we need to analyze how the "ant_im" feature relates to the target variable.

To conduct the analysis, we can compare the distribution of "ant_im" values between patients with and without chronic heart failure. By observing the presence or absence of specific changes in the ECG leads V1-V4, we can determine if there is a correlation with chronic heart failure.

Based on the analysis, the generated dictionary would be as follows:

```json
{
  "no": ["QRS is like QS-complex", "QRS has no changes", "there is no infarct in this location"],
  "yes": ["QRS is like QR-complex", "QRS is like Qr-complex"]
}
```

For the target class "no" (patients without chronic heart failure), the "ant_im" feature has possible values of "QRS is like QS-complex", "QRS has no changes", and "there is no infarct in this location". These values indicate no significant infarct or changes in the ECG leads V1-V4.

For the target class "yes" (patients with chronic heart failure), the "ant_im" feature has possible values of "QRS is like QR-complex" and "QRS is like Qr-complex". These values suggest the presence of infarct or specific changes in the ECG leads V1-V4.

The dictionary represents the possible values of the "ant_im" feature for each target class, providing insights into the relationship between the feature and the task of determining chronic heart failure.