[no ci] add early stage disclaimer to user guide

Author: vpratz

docsrc/source/user_guide/approximators.ipynb

@@ -7,6 +7,8 @@
    "source": [
     "# Approximators\n",
     "\n",
+    "*Disclaimer: This guide is in an early stage. We welcome contributions to the guide in form of issues and pull requests.*\n",
+    "\n",
     "Neural approximators provide an approximation of a distribution or a value. To achieve this, they combine the things we have discussed in the previous chapters: simulated data, adapters, summary networks and inference networks. Approximators are at the heart of BayesFlow, as they organize the different components and provide the `fit()` function used for training."
    ]
   }

docsrc/source/user_guide/data_processing.ipynb

@@ -7,6 +7,8 @@
    "source": [
     "# Data Processing: Adapters\n",
     "\n",
+    "*Disclaimer: This guide is in an early stage. We welcome contributions to the guide in form of issues and pull requests.*\n",
+    "\n",
     "To ensure that the training data generated by a simulator can be used for deep learning, we have to bring our data into the structure required by BayesFlow. The {py:class}`~bayesflow.adapters.Adapter` class provides multiple flexible functionalities, from standardization to renaming, and many more.\n",
     "\n",
     "## BayesFlow's Data Structure\n",

docsrc/source/user_guide/diagnostics.ipynb

@@ -7,6 +7,8 @@
    "source": [
     "# Diagnostics and Visualizations\n",
     "\n",
+    "*Disclaimer: This guide is in an early stage. We welcome contributions to the guide in form of issues and pull requests.*\n",
+    "\n",
     "There are many factors that influence whether training succeeds and how well we can approximate a target. In this light, checking the results and diagnosing potential problems is an important part of the workflow.\n",
     "\n",
     "## Loss\n",

docsrc/source/user_guide/generative_models.ipynb

@@ -5,7 +5,9 @@
    "id": "62abf622-68b3-454c-9424-384b41ad6a38",
    "metadata": {},
    "source": [
-    "# Generative Models"
+    "# Generative Models\n",
+    "\n",
+    "*Disclaimer: This guide is in an early stage. We welcome contributions to the guide in form of issues and pull requests.*"
    ]
   },
   {

docsrc/source/user_guide/inference_networks.ipynb

@@ -7,6 +7,8 @@
    "source": [
     "# Inference Networks\n",
     "\n",
+    "*Disclaimer: This guide is in an early stage. We welcome contributions to the guide in form of issues and pull requests.*\n",
+    "\n",
     "Inference networks form the backbone of neural amortized Bayesian inference methods. They are generative models (usually _invertible_ ones, but they do not have to be), that can transform samples from a simple distribution (e.g., a unit Gaussian) to a complicated one (e.g., a posterior distribution).\n",
     "\n",
     "You can find the inference networks in the {py:mod}`~bayesflow.networks` module. You can identify them by the \"Bases: {py:class}`~bayesflow.networks.InferenceNetwork`\" label.\n",

docsrc/source/user_guide/introduction.md

@@ -1,5 +1,7 @@
 # Introduction
 
+*Disclaimer: This guide is in an early stage. We welcome contributions to the guide in form of issues and pull requests.*
+
 Welcome to the User Guide! This guide is still in a very early stage, but we plan to evolve it into a comprehensive guide to using BayesFlow.
 
 ## Why (and When) Do We Need Amortized Bayesian Inference (ABI)?

docsrc/source/user_guide/saving_loading.ipynb

@@ -7,6 +7,8 @@
    "source": [
     "# Saving & Loading Models\n",
     "\n",
+    "*Disclaimer: This guide is in an early stage. We welcome contributions to the guide in form of issues and pull requests.*\n",
+    "\n",
     "Saving and loading of models takes place via our backend, [Keras 3](https://keras.io/). Objects that can be saved have a `save` method, which allows saving to a `.keras` file.\n",
     "\n",
     "The [`keras.saving.load_model`](https://keras.io/api/models/model_saving_apis/model_saving_and_loading/#load_model-function) function can be used to load the stored models. There is a lot more to say about this topic. For now, refer to the respective [Keras guide](https://keras.io/guides/serialization_and_saving/).\n",

docsrc/source/user_guide/summary_networks.ipynb

@@ -7,6 +7,8 @@
    "source": [
     "# Summary Networks\n",
     "\n",
+    "*Disclaimer: This guide is in an early stage. We welcome contributions to the guide in form of issues and pull requests.*\n",
+    "\n",
     "Learnable summary statistics provide several advantages for amortized Bayesian inference. By reducing the dimensionality of the data, they enable more efficient processing by the inference network. Additionally, they can encode knowledge about the data (e.g., that different observations are exchangeable) directly into the network architecture. Finally, the resulting _summary space_ can serve as a diagnostic tool. In some cases, we also require a summary network to convert observations of variable size into a fixed-size representation.\n",
     "\n",
     "You can find the summary networks in the {py:mod}`~bayesflow.networks` module. You can identify them by the \"Bases: {py:class}`~bayesflow.networks.SummaryNetwork`\" label."

docsrc/source/user_guide/workflows.ipynb

@@ -7,6 +7,8 @@
    "source": [
     "# Workflows\n",
     "\n",
+    "*Disclaimer: This guide is in an early stage. We welcome contributions to the guide in form of issues and pull requests.*\n",
+    "\n",
     "Workflows are an abstraction on top of the approximator, that expose methods for training and inference in a more abstract, and therefore simplified, fashion.\n",
     "\n",
     "## BasicWorkflow\n",