linear regression example: minor cleaning

Author: paul-buerkner

examples/Linear_Regression.ipynb

@@ -44,21 +44,6 @@
     "Here I use JAX but you can freely change that and the notebook will work all the same."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-01T23:25:49.380507Z",
-     "start_time": "2024-12-01T23:25:49.264127Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 4,
@@ -114,8 +99,8 @@
    },
    "outputs": [],
    "source": [
-    "# TODO: do we have to require \"batch_shape\" to the function passed to meta_fn?\n",
-    "def meta(batch_shape):\n",
+    "def meta(batch_size):\n",
+    "    # batch_size needs to be present but will be ignored here\n",
     "    # N: number of observation in a dataset\n",
     "    N = np.random.randint(5, 15)\n",
     "    return dict(N=N)\n",
@@ -272,7 +257,7 @@
     "id": "8wiGFZnE8VOe"
    },
    "source": [
-    "To ensure that the training data generated by the simulator can be used for deep learning, we have do a bunch of transformations via `(data_)adapter` objects. Here, I build my own `adapter` from scratch but later on bayesflow will also provide default adapters that will already automate most of the commonly required steps."
+    "To ensure that the training data generated by the simulator can be used for deep learning, we have do a bunch of transformations via `adapter` objects. Here, I build my own `adapter` from scratch but later on bayesflow will also provide default adapters that will already automate most of the commonly required steps."
    ]
   },
   {