Based on the feature description and task, we need to analyze how the variable "lat_im" relates to the presence of chronic heart failure.

Since the feature "lat_im" represents the presence of a lateral myocardial infarction, we can infer that patients with a lateral myocardial infarction may have a higher likelihood of developing chronic heart failure. Therefore, we can expect that the values of "lat_im" indicating the presence of myocardial infarction ("QRS has no changes", "QRS is like Qr-complex", "QRS is like QS-complex", "QRS is like QR-complex") will be associated with the presence of chronic heart failure.

However, it is difficult to determine the relationship for the values indicating the absence of myocardial infarction ("there is no infarct in this location") as this could either indicate a lower likelihood or be unrelated to chronic heart failure.

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

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

In this dictionary, the values for the "no" class are the possible values of the "lat_im" feature that are associated with the absence of chronic heart failure. The values for the "yes" class are the possible values of the "lat_im" feature that are associated with the presence of chronic heart failure.