fix: issue with session crashing with paddle demo on Colab's T4

examples_and_demos/dinov2_to_paddle.ipynb

@@ -79,27 +79,11 @@
     "import numpy as np\n",
     "from transformers import AutoImageProcessor, AutoModelForImageClassification\n",
     "import ivy\n",
+    "device = \"gpu\" if paddle.device.cuda.device_count() else \"cpu\"\n",
     "torch.manual_seed(0)\n",
     "paddle.seed(0)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Set the default device, this step isn’t required if you’re transpiling on a CPU:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
-    "ivy.set_default_device(\"gpu:0\" if torch.cuda.is_available() else \"cpu\")"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -153,7 +137,7 @@
    "outputs": [],
    "source": [
     "processor = AutoImageProcessor.from_pretrained(\"facebook/dinov2-base-imagenet1k-1-layer\")\n",
-    "model = AutoModelForImageClassification.from_pretrained(\"facebook/dinov2-base-imagenet1k-1-layer\").to(device)\n",
+    "model = AutoModelForImageClassification.from_pretrained(\"facebook/dinov2-base-imagenet1k-1-layer\")\n",
     "id2label = model.config.id2label"
    ]
   },
@@ -178,7 +162,7 @@
     }
    ],
    "source": [
-    "inputs = processor(images=image, return_tensors=\"pt\").to(device)\n",
+    "inputs = processor(images=image, return_tensors=\"pt\")\n",
     "outputs = model(**inputs)\n",
     "logits = outputs.logits\n",
     "logits_np = logits.detach().cpu().numpy()\n",
@@ -190,7 +174,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now, we will transpile the model to PaddlePaddle using ivy.transpile, here’s a simple [example](https://unify.ai/docs/ivy/demos/learn_the_basics/04_transpile_code.html) explaining the usage. If you’re transpiling on a GPU, then based on your machine specs you might have to set `FLAGS_fraction_of_gpu_memory_to_use` to `auto_growth`."
+    "Now, we will transpile the model to PaddlePaddle using ivy.transpile, here’s a simple [example](https://unify.ai/docs/ivy/demos/learn_the_basics/04_transpile_code.html) explaining the usage."
    ]
   },
   {
@@ -237,7 +221,7 @@
     }
    ],
    "source": [
-    "paddle_inputs = {\"pixel_values\": paddle.to_tensor(inputs[\"pixel_values\"].cpu().numpy(), stop_gradient=False)}\n",
+    "paddle_inputs = {\"pixel_values\": paddle.to_tensor(inputs[\"pixel_values\"].cpu().numpy(), stop_gradient=False, place=\"cpu\")}\n",
     "outputs = transpiled_model(**paddle_inputs)\n",
     "logits = outputs.logits\n",
     "logits_np_transpiled = logits.numpy()\n",
@@ -352,6 +336,7 @@
     }
    ],
    "source": [
+    "transpiled_model = transpiled_model.to(device)\n",
     "epochs = 5\n",
     "loss_epoch_arr = []\n",
     "\n",