Update issue templates

.github/ISSUE_TEMPLATE/bug_report.md

@@ -0,0 +1,36 @@
+---
+name: Bug report
+about: Create a bug report to help us improve BayesFlow
+title: "[BUG]"
+labels: ''
+assignees: ''
+
+---
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+**To Reproduce**
+Minimal steps to reproduce the behavior:
+1. Import '...'
+2. Create network '....'
+3. Call '....'
+4. See error
+
+**Expected behavior**
+A clear and concise description of what you expected to happen.
+
+**Traceback**
+If you encounter an error, please provide a complete traceback to help explain your problem.
+
+**Environment**
+- OS: [e.g. Ubuntu]
+- Python Version: [e.g. 3.11]
+- Backend: [e.g. jax, tensorflow, pytorch]
+- BayesFlow Version: [e.g. 2.0.2]
+
+**Additional context**
+Add any other context about the problem here.
+
+**Minimality**
+- [ ] I verify that my example is minimal, does not rely on third-party packages, and is most likely an issue in BayesFlow.

.github/ISSUE_TEMPLATE/feature_request.md

@@ -0,0 +1,20 @@
+---
+name: Feature request
+about: Suggest a new feature to be implemented in BayesFlow
+title: "[FEATURE]"
+labels: feature
+assignees: ''
+
+---
+
+**Is your feature request related to a problem? Please describe.**
+A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+
+**Describe the solution you'd like**
+A clear and concise description of what you want to happen.
+
+**Describe alternatives you've considered**
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context**
+Add any other context or screenshots about the feature request here.

bayesflow/utils/optimal_transport/log_sinkhorn.py

@@ -1,7 +1,6 @@
 import keras
 
 from .. import logging
-from ..tensor_utils import is_symbolic_tensor
 
 from .euclidean import euclidean
 
@@ -27,9 +26,6 @@ def log_sinkhorn_plan(x1, x2, regularization: float = 1.0, rtol=1e-5, atol=1e-8,
 
     log_plan = cost / -(regularization * keras.ops.mean(cost) + 1e-16)
 
-    if is_symbolic_tensor(log_plan):
-        return log_plan
-
     def contains_nans(plan):
         return keras.ops.any(keras.ops.isnan(plan))