Fix docs typo

Author: thomaspinder

docs/index.rst

@@ -8,9 +8,9 @@ Gaussian process 'Hello World' example
 
 To gain an intuition for the exposed API provided by GPJax, a simple example of that derives the Gaussian process posterior for regression can be represented by::
 
+    import gpjax
     import jax.numpy as jnp
     import jax.random as jr
-    import gpjax
     key = jr.PRNGKey(123)
 
     X = jnp.linspace(-2., 2., 100)
@@ -24,7 +24,6 @@ To gain an intuition for the exposed API provided by GPJax, a simple example of
     posterior = f_prior * likelihood
 
 
-
 .. toctree::
     :maxdepth: 1
     :caption: Getting Started

docs/nbs/spatial_modelling.ipynb

@@ -37,7 +37,7 @@
    "id": "adjacent-elite",
    "metadata": {},
    "source": [
-    "# Introduction\n",
+    "## Introduction\n",
     "\n",
     "We'll be working through a simple spatial modelling workflow in this notebook. In reality, the process is often more complicated than what is shown here as exogenous data is often used to better understand the latent process. Further, complex kernel structures are frequently used as the underlying process is often complex and non-stationary. Finally, spatial data can often be challenging to work with due to incosistent sampling schemes and anomalous data measurements. That being said, this notebook should serve as a good introduction to using GPJax with spatial inputs.\n",
     "\n",
@@ -751,4 +751,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}