Based on the given feature description, we can analyze the relationship between the feature "post_im" and the presence of chronic heart failure in the patient.

To determine if the patient shows chronic heart failure or not, we need to consider the different categories of the "post_im" feature and see if any specific category is associated with chronic heart failure.

Let's analyze the possible relationship:

1. If the "post_im" category is "there is no infarct in this location", it is unlikely to be related to chronic heart failure.

2. If the "post_im" category is "QRS has no changes" or "QRS is like Qr-complex", it is less likely to be related to chronic heart failure.

3. If the "post_im" category is "QRS is like QR-complex" or "QRS is like QS-complex", it might be slightly more likely to be related to chronic heart failure, as these changes in the QRS complex could indicate myocardial infarction in the posterior region.

Based on this analysis, we can create a dictionary to summarize the relationship between the "post_im" feature and the presence of chronic heart failure:

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

Please note that we have not included the exact values from the feature that are hard to predict the relationship with the target class. Also, this analysis is based on general knowledge and further analysis might be required considering the specific dataset and domain expertise.