remove deprecated summaries function

The function was renamed to summarize in v2.0.4.

Author: vpratz

bayesflow/approximators/continuous_approximator.py

@@ -3,7 +3,6 @@
 import numpy as np
 
 import keras
-import warnings
 
 from bayesflow.adapters import Adapter
 from bayesflow.networks import InferenceNetwork, SummaryNetwork
@@ -575,14 +574,6 @@ def summarize(self, data: Mapping[str, np.ndarray], **kwargs) -> np.ndarray:
 
         return summaries
 
-    def summaries(self, data: Mapping[str, np.ndarray], **kwargs) -> np.ndarray:
-        """
-        .. deprecated:: 2.0.4
-            `summaries` will be removed in version 2.0.6, it was renamed to `summarize` which should be used instead.
-        """
-        warnings.warn("`summaries` was renamed to `summarize` and will be removed in version 2.0.6.", FutureWarning)
-        return self.summarize(data=data, **kwargs)
-
     def log_prob(self, data: Mapping[str, np.ndarray], **kwargs) -> np.ndarray:
         """
         Computes the log-probability of given data under the model. The `data` dictionary is preprocessed using the

bayesflow/approximators/model_comparison_approximator.py

@@ -2,7 +2,6 @@
 
 import keras
 import numpy as np
-import warnings
 
 from bayesflow.adapters import Adapter
 from bayesflow.datasets import OnlineDataset
@@ -439,14 +438,6 @@ def summarize(self, data: Mapping[str, np.ndarray], **kwargs) -> np.ndarray:
 
         return summaries
 
-    def summaries(self, data: Mapping[str, np.ndarray], **kwargs) -> np.ndarray:
-        """
-        .. deprecated:: 2.0.4
-            `summaries` will be removed in version 2.0.6, it was renamed to `summarize` which should be used instead.
-        """
-        warnings.warn("`summaries` was renamed to `summarize` and will be removed in version 2.0.6.", FutureWarning)
-        return self.summarize(data=data, **kwargs)
-
     def _compute_logits(self, classifier_conditions: Tensor) -> Tensor:
         """Helper to compute projected logits from the classifier network."""
         logits = self.classifier_network(classifier_conditions)