Based on prior knowledge, we cannot directly determine the relationship between the feature "O_L_POST" (Pulmonary edema at the time of admission to the intensive care unit) and the presence of chronic heart failure. Pulmonary edema can be caused by various factors and may or may not be associated with chronic heart failure.

To analyze the relationship between the feature and the target variable, we need to examine the data and see if there is any pattern or trend that emerges. By looking at the data and considering any additional information, such as medical domain knowledge or previous research, we might be able to identify potential links between the feature and the presence of chronic heart failure.

Let's assume we have analyzed the data and found the following associations:

- For patients with chronic heart failure, the possible values of "O_L_POST" are:
  - ["no", "yes"] (both values are present)

- For patients without chronic heart failure, the possible values of "O_L_POST" are:
  - ["no", "yes"] (both values are present)

Based on this analysis, we can create the dictionary:

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

Please note that this is a hypothetical example based on assumptions. In reality, the relationship between the feature and the target variable should be determined by analyzing the actual data.