diff --git a/examples/introduction_development.ipynb b/examples/introduction_development.ipynb
index 3b9ceb61..7e6fad03 100644
--- a/examples/introduction_development.ipynb
+++ b/examples/introduction_development.ipynb
@@ -240,20 +240,6 @@
     "    def __init__(self, model, disable_model_checks=False):\n",
     "        pass\n",
     "\n",
-    "    def fit(self, *args, **kwargs):\n",
-    "        \"\"\"\n",
-    "        Train analyzer like a Keras model.\n",
-    "        Does not need to be implemented.\n",
-    "        \"\"\"\n",
-    "        pass\n",
-    "\n",
-    "    def fit_generator(self, *args, **kwargs):\n",
-    "        \"\"\"\n",
-    "        Train analyzer like a Keras model.\n",
-    "        Does not need to be implemented.\n",
-    "        \"\"\"\n",
-    "        pass\n",
-    "\n",
     "    def analyze(self, X):\n",
     "        \"\"\"\n",
     "        Analyze the behavior of model on input `X`.\n",
@@ -977,7 +963,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.9.7 ('innvestigate-pdNhrmV2-py3.9')",
    "language": "python",
    "name": "python3"
   },
@@ -992,6 +978,11 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.9.7"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "6d5a31c854ee050015bee53401f829bd4e104bd774bbc6a8c859288cc9ab765e"
+   }
   }
  },
  "nbformat": 4,
diff --git a/src/innvestigate/analyzer/base.py b/src/innvestigate/analyzer/base.py
index 61cafeca..19b846ee 100644
--- a/src/innvestigate/analyzer/base.py
+++ b/src/innvestigate/analyzer/base.py
@@ -31,7 +31,6 @@ class AnalyzerBase(metaclass=ABCMeta):
 
     >>> model = create_keras_model()
     >>> a = Analyzer(model)
-    >>> a.fit(X_train)  # If analyzer needs training.
     >>> analysis = a.analyze(X_test)
     >>>
     >>> state = a.save()
@@ -127,39 +126,6 @@ def _do_model_checks(self) -> None:
 
         self._model_check_done = True
 
-    def fit(self, *_args, disable_no_training_warning: bool = False, **_kwargs):
-        """
-        Stub that eats arguments. If an analyzer needs training
-        include :class:`TrainerMixin`.
-
-        :param disable_no_training_warning: Do not warn if this function is
-          called despite no training is needed.
-        """
-        if not disable_no_training_warning:
-            # issue warning if no training is foreseen, but fit() is still called.
-            warnings.warn(
-                "This analyzer does not need to be trained." " Still fit() is called.",
-                RuntimeWarning,
-            )
-
-    def fit_generator(
-        self, *_args, disable_no_training_warning: bool = False, **_kwargs
-    ):
-        """
-        Stub that eats arguments. If an analyzer needs training
-        include :class:`TrainerMixin`.
-
-        :param disable_no_training_warning: Do not warn if this function is
-          called despite no training is needed.
-        """
-        if not disable_no_training_warning:
-            # issue warning if no training is foreseen, but fit() is still called.
-            warnings.warn(
-                "This analyzer does not need to be trained."
-                " Still fit_generator() is called.",
-                RuntimeWarning,
-            )
-
     @abstractmethod
     def analyze(
         self, X: OptionalList[np.ndarray], *args: Any, **kwargs: Any
@@ -248,52 +214,3 @@ def load_npz(fname):
         class_name = npz_file["class_name"].item()
         state = npz_file["state"].item()
         return AnalyzerBase.load(class_name, state)
-
-
-###############################################################################
-
-
-class TrainerMixin:
-    """Mixin for analyzer that adapt to data.
-
-    This convenience interface exposes a Keras like training routing
-    to the user.
-    """
-
-    # TODO: extend with Y
-    def fit(self, X: np.ndarray | None = None, batch_size: int = 32, **kwargs) -> None:
-        """
-        Takes the same parameters as Keras's :func:`model.fit` function.
-        """
-        generator = isequence.BatchSequence(X, batch_size)
-        return self._fit_generator(generator, **kwargs)  # type: ignore
-
-    def fit_generator(self, *args, **kwargs):
-        """
-        Takes the same parameters as Keras's :func:`model.fit_generator`
-        function.
-        """
-        return self._fit_generator(*args, **kwargs)
-
-    def _fit_generator(self, *_args, **_kwargs):
-        raise NotImplementedError()
-
-
-class OneEpochTrainerMixin(TrainerMixin):
-    """Exposes the same interface and functionality as :class:`TrainerMixin`
-    except that the training is limited to one epoch.
-    """
-
-    def fit(self, *args, **kwargs) -> None:
-        """
-        Same interface as :func:`fit` of :class:`TrainerMixin` except that
-        the parameter epoch is fixed to 1.
-        """
-        return super().fit(*args, epochs=1, **kwargs)
-
-    def fit_generator(self, *args, steps: int = None, **kwargs):
-        """
-        Same interface as :func:`fit_generator` of :class:`TrainerMixin` except that
-        the parameter epoch is fixed to 1.
-        """
-        return super().fit_generator(*args, steps_per_epoch=steps, epochs=1, **kwargs)
diff --git a/tests/dryrun.py b/tests/dryrun.py
index ed7a99d5..51ae6537 100644
--- a/tests/dryrun.py
+++ b/tests/dryrun.py
@@ -97,12 +97,10 @@ def _apply_test(self, model: Model) -> None:
         # Generate random training input
         input_shape = model.input_shape[1:]
         x = np.random.rand(1, *input_shape).astype(np.float32)
-        x_fit = np.random.rand(16, *input_shape).astype(np.float32)
         # Call model with test input
         model.predict(x)
         # Get analyzer.
         analyzer = self._method(model)
-        analyzer.fit(x_fit)
         # Generate random test input
         analysis = analyzer.analyze(x)
         assert tuple(analysis.shape) == (1,) + input_shape