Based on the feature description and task, we can analyze the relationship between the feature "lat_im" and the presence of chronic heart failure in patients with myocardial infarction complications.

To do this analysis, we need data that includes both the "lat_im" feature and the presence of chronic heart failure in patients. Without access to such data, it is not possible to determine the relationship between the feature and the target accurately.

However, assuming we had access to the relevant data, we would analyze the relationship between the "lat_im" feature and chronic heart failure by comparing the distribution of different "lat_im" values among patients with and without chronic heart failure. We would look for patterns or significant differences in the distribution that could indicate a relationship.

Based on this analysis, we would then generate a dictionary that lists the possible values of the "lat_im" feature for each target class (yes or no), as requested:

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

In this case, we assume that all possible values of the "lat_im" feature are applicable to both target classes (yes and no) since no specific information is provided about which values are relevant or indicate the presence of chronic heart failure.