Fix structural pruning sparsity notebook

- Enabled tensor preservation option explicitly when creating TFLite
  interpreter.
- The convolution weight search is now done via the operator lookup.

PiperOrigin-RevId: 595845109

tensorflow_model_optimization/g3doc/guide/pruning/pruning_with_sparsity_2_by_4.ipynb

@@ -74,7 +74,7 @@
         "id": "FbORZA_bQx1G"
       },
       "source": [
-        "Structural pruning weights from your model to make it sparse in specific pattern can accelerate model inference time with appropriate HW supports. \n",
+        "Structural pruning weights from your model to make it sparse in specific pattern can accelerate model inference time with appropriate HW supports.\n",
         "\n",
         "This tutorial shows you how to:\n",
         "* Define and train a model on the mnist dataset with a specific structural sparsity\n",
@@ -459,7 +459,7 @@
       "outputs": [],
       "source": [
         "# Load tflite file with the created pruned model\n",
-        "interpreter = tf.lite.Interpreter(model_path=tflite_file)\n",
+        "interpreter = tf.lite.Interpreter(model_path=tflite_file, experimental_preserve_all_tensors=True)\n",
         "interpreter.allocate_tensors()\n",
         "\n",
         "details = interpreter.get_tensor_details()\n",
@@ -630,9 +630,10 @@
       "outputs": [],
       "source": [
         "# Get weights of the convolutional layer that has been pruned with 2 by 4 sparsity.\n",
-        "tensor_name = 'structural_pruning/Conv2D'\n",
-        "detail = [x for x in details if tensor_name in x[\"name\"]]\n",
-        "tensor_data = interpreter.tensor(detail[1][\"index\"])()\n",
+        "op_details = interpreter._get_ops_details()\n",
+        "op_name = 'CONV_2D'\n",
+        "op_detail = [x for x in op_details if op_name in x[\"op_name\"]]\n",
+        "tensor_data = interpreter.tensor(op_detail[1][\"inputs\"][1])()\n",
         "print(f\"Shape of the weight tensor is {tensor_data.shape}\")"
       ]
     },