Update notebook

docs/examples/parallel-computing-with-dask.ipynb

@@ -105,11 +105,11 @@
     "  - Data is loaded into memory and **computation** is performed in **streaming fashion**, **block-by-block**\n",
     "- Computation is controlled by multi-processing or thread pool\n",
     "\n",
+    "&mdash; <cite>https://docs.xarray.dev/en/stable/user-guide/dask.html</cite>\n",
+    "\n",
     "<div style=\"text-align:center\">\n",
     "  <img src=\"../_static/dask-array.png\" alt=\"Dask Array\" style=\"display: inline-block; width:300px;\">\n",
-    "</div>\n",
-    "\n",
-    "&mdash; <cite>https://docs.xarray.dev/en/stable/user-guide/dask.html</cite>\n"
+    "</div>\n"
    ]
   },
   {
@@ -275,7 +275,7 @@
    "source": [
     "## Using a Dask Cluster for Scalable Computations\n",
     "\n",
-    "- All of the large-scale Dask collections like Dask Array, Dask DataFrame, and Dask Bag and the fine-grained APIs like delayed and futures generate task graphs where each node in the graph is a normal Python function and edges between nodes are normal Python objects that are created by one task as outputs and used as inputs in another task.\n",
+    "- All of the large-scale Dask collections like Dask Array, Dask DataFrame, and Dask Bag and the fine-grained APIs like delayed and futures **generate task graphs** where each node in the graph is a normal Python function and edges between nodes are normal Python objects that are created by one task as outputs and used as inputs in another task.\n",
     "\n",
     "- After Dask generates these task graphs, it needs to execute them on parallel hardware. This is the job of a **task scheduler**.\n",
     "\n",