diff --git a/delegate/DelegateQuickStartGuide.md b/delegate/DelegateQuickStartGuide.md
index b7cdef8..c24a17b 100644
--- a/delegate/DelegateQuickStartGuide.md
+++ b/delegate/DelegateQuickStartGuide.md
@@ -42,7 +42,11 @@
  * numpy (Depends on TfLite version)
  * tflite_runtime (>=2.5, depends on Arm NN Delegate)
 
-If you haven't built the delegate yet then take a look at the [build guide](./BuildGuideNative.md). Otherwise, you can download the binaries [here](https://github.com/ARM-software/armnn/releases/).
+If you haven't built the delegate yet then take a look at the [build guide](./BuildGuideNative.md). Otherwise, you can download the binaries [here](https://github.com/ARM-software/armnn/releases/). Set the following environment variable to the location of the .so binary files:
+
+```bash
+export LD_LIBRARY_PATH=<path_to_so_binary_files>
+```
 
 We recommend creating a virtual environment for this tutorial. For the following code to work python3 is needed. Please
 also check the documentation of the TfLite version you want to use. There might be additional prerequisites for the python
@@ -73,7 +77,7 @@
 But in our case, with Tensorflow Lite 2.5.0, we can install through:
 
 ```
-pip3 install --extra-index-url https://google-coral.github.io/py-repo/ tflite_runtime
+pip3 install --extra-index-url https://google-coral.github.io/py-repo/ tflite_runtime==2.5.0
 ```
 
 Your virtual environment is now all setup. Copy the final python script into a python file e.g. 
diff --git a/delegate/python/test/test_external_delegate.py b/delegate/python/test/test_external_delegate.py
index f01a2d3..a8dd8e6 100644
--- a/delegate/python/test/test_external_delegate.py
+++ b/delegate/python/test/test_external_delegate.py
@@ -1,4 +1,4 @@
-# Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+# Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
 # SPDX-License-Identifier: MIT
 
 import numpy as np
@@ -66,7 +66,7 @@
         os.remove(binary_file)
 
     # Create blank binary file to write to.
-    open(binary_file, 'a').close()
+    open(binary_file, "a").close()
     assert (os.path.exists(binary_file))
     assert (os.stat(binary_file).st_size == 0)
 
@@ -102,11 +102,11 @@
 def test_external_delegate_gpu_fastmath(delegate_dir, test_data_folder):
     # create armnn delegate with enable-fast-math
     # fast-math is only enabled on Conv2d layer, so use conv2d model.
-    armnn_delegate = tflite.load_delegate(delegate_dir, options = {'backends': 'GpuAcc',
-                                                                   'enable-fast-math': '1',
+    armnn_delegate = tflite.load_delegate(delegate_dir, options = {"backends": "GpuAcc",
+                                                                   "enable-fast-math": "1",
                                                                    "logging-severity": "info"})
 
-    model_file_name = 'conv2d.tflite'
+    model_file_name = "conv2d.tflite"
 
     inputShape = [ 1, 5, 5, 1 ]
     outputShape = [ 1, 3, 3, 1 ]
@@ -131,15 +131,15 @@
     compare_outputs(armnn_outputs, [expected_output])
 
 @pytest.mark.CpuAccTest
-def test_external_delegate_cpu_options(capfd, delegate_dir, test_data_folder):
+def test_external_delegate_cpu_options(delegate_dir, test_data_folder):
     # create armnn delegate with enable-fast-math and number-of-threads options
     # fast-math is only enabled on Conv2d layer, so use conv2d model.
-    armnn_delegate = tflite.load_delegate(delegate_dir, options = {'backends': 'CpuAcc',
-                                                                   'enable-fast-math': '1',
-                                                                   'number-of-threads': '4',
+    armnn_delegate = tflite.load_delegate(delegate_dir, options = {"backends": "CpuAcc",
+                                                                   "enable-fast-math": "1",
+                                                                   "number-of-threads": "4",
                                                                    "logging-severity": "info"})
 
-    model_file_name = 'conv2d.tflite'
+    model_file_name = "conv2d.tflite"
 
     inputShape = [ 1, 5, 5, 1 ]
     outputShape = [ 1, 3, 3, 1 ]
@@ -163,9 +163,6 @@
     # check results
     compare_outputs(armnn_outputs, [expected_output])
 
-    captured = capfd.readouterr()
-    assert 'Set CPPScheduler to Linear mode, with 4 threads to use' in captured.out
-
 def test_external_delegate_options_wrong_logging_level(delegate_dir):
     with pytest.raises(ValueError):
         tflite.load_delegate(
@@ -174,9 +171,10 @@
 
 def test_external_delegate_options_debug(capfd, delegate_dir, test_data_folder):
     # create armnn delegate with debug option
-    armnn_delegate = tflite.load_delegate(delegate_dir, options = {'backends': 'CpuRef', 'debug-data': '1'})
+    armnn_delegate = tflite.load_delegate(delegate_dir, options = {"backends": "CpuRef",
+                                                                   "debug-data": "1"})
 
-    model_file_name = 'fp32_model.tflite'
+    model_file_name = "fp32_model.tflite"
 
     tensor_shape = [1, 2, 2, 1]
 
@@ -192,16 +190,16 @@
     compare_outputs(armnn_outputs, [expected_output])
 
     captured = capfd.readouterr()
-    assert 'layerGuid' in captured.out
+    assert "layerGuid" in captured.out
 
 
 def test_external_delegate_options_fp32_to_fp16(capfd, delegate_dir, test_data_folder):
     # create armnn delegate with reduce-fp32-to-fp16 option
-    armnn_delegate = tflite.load_delegate(delegate_dir, options = {'backends': 'CpuRef',
-                                                                   'debug-data': '1',
-                                                                   'reduce-fp32-to-fp16': '1'})
+    armnn_delegate = tflite.load_delegate(delegate_dir, options = {"backends": "CpuRef",
+                                                                   "debug-data": "1",
+                                                                   "reduce-fp32-to-fp16": "1"})
 
-    model_file_name = 'fp32_model.tflite'
+    model_file_name = "fp32_model.tflite"
 
     tensor_shape = [1, 2, 2, 1]
 
@@ -217,16 +215,16 @@
     compare_outputs(armnn_outputs, [expected_output])
 
     captured = capfd.readouterr()
-    assert 'convert_fp32_to_fp16' in captured.out
-    assert 'convert_fp16_to_fp32' in captured.out
+    assert "convert_fp32_to_fp16" in captured.out
+    assert "convert_fp16_to_fp32" in captured.out
 
 def test_external_delegate_options_fp32_to_bf16(capfd, delegate_dir, test_data_folder):
     # create armnn delegate with reduce-fp32-to-bf16 option
-    armnn_delegate = tflite.load_delegate(delegate_dir, options = {'backends': 'CpuRef',
-                                                                   'debug-data': '1',
-                                                                   'reduce-fp32-to-bf16': '1'})
+    armnn_delegate = tflite.load_delegate(delegate_dir, options = {"backends": "CpuRef",
+                                                                   "debug-data": "1",
+                                                                   "reduce-fp32-to-bf16": "1"})
 
-    model_file_name = 'conv2d.tflite'
+    model_file_name = "conv2d.tflite"
 
     inputShape = [ 1, 5, 5, 1 ]
     outputShape = [ 1, 3, 3, 1 ]
@@ -251,14 +249,14 @@
     compare_outputs(armnn_outputs, [expected_output])
 
     captured = capfd.readouterr()
-    assert 'convert_fp32_to_bf16' in captured.out
+    assert "convert_fp32_to_bf16" in captured.out
 
 def test_external_delegate_options_memory_import(delegate_dir, test_data_folder):
     # create armnn delegate with memory-import option
-    armnn_delegate = tflite.load_delegate(delegate_dir, options = {'backends': 'CpuAcc,CpuRef',
-                                                                   'memory-import': '1'})
+    armnn_delegate = tflite.load_delegate(delegate_dir, options = {"backends": "CpuAcc,CpuRef",
+                                                                   "memory-import": "1"})
 
-    model_file_name = 'fallback_model.tflite'
+    model_file_name = "fallback_model.tflite"
 
     tensor_shape = [1, 2, 2, 1]