From 4e449f519c64bf0768daad3b82f7c16bd9f813cc Mon Sep 17 00:00:00 2001
From: Sebastian Larsson <sebastian.larsson@arm.com>
Date: Wed, 11 Feb 2026 13:39:24 +0100
Subject: [PATCH] Arm backend: Format docs for remaining files in
 backends/arm/test/

Change-Id: I6d07cc2f917a719bf945e47315ef383f93ac9450
Signed-off-by: Sebastian Larsson <sebastian.larsson@arm.com>
---
 .lintrunner.toml                              |  1 -
 backends/arm/test/common.py                   | 42 ++++++-----
 backends/arm/test/conftest.py                 | 21 +++---
 backends/arm/test/misc/test_dim_order.py      | 19 ++---
 .../test/misc/test_extract_io_params_tosa.py  |  6 +-
 .../test/misc/test_non_persistent_buffers.py  | 16 ++---
 ...test_partition_decomposed_quantized_ops.py | 13 +++-
 .../arm/test/misc/test_qat_training_loop.py   | 10 ++-
 .../arm/test/misc/test_quant_custom_meta.py   | 14 ++--
 backends/arm/test/misc/test_shared_qspecs.py  | 56 ++++++++++-----
 backends/arm/test/misc/test_tosa_spec.py      |  4 +-
 .../stable_diffusion_module_test_configs.py   |  3 +-
 .../arm/test/models/test_nn_functional.py     |  1 -
 backends/arm/test/models/test_nn_modules.py   |  1 -
 .../arm/test/models/test_torch_functions.py   |  1 -
 backends/arm/test/ops/test_div_tensor_mode.py |  4 +-
 backends/arm/test/ops/test_max_pool1d.py      |  9 ++-
 backends/arm/test/ops/test_scalars.py         | 27 ++++---
 backends/arm/test/ops/test_sdpa.py            | 18 +++--
 ...st_decompose_int16_activation_conv_pass.py | 48 +++++++------
 .../passes/test_decompose_softmax_pass.py     |  5 +-
 .../quantizer/test_partial_quantization.py    |  4 +-
 .../test/quantizer/test_preserve_kwargs.py    |  4 +-
 backends/arm/test/runner_utils.py             | 38 ++++++----
 .../arm/test/test_memory_allocator_log.py     |  6 +-
 .../arm/test/tester/analyze_output_utils.py   |  6 +-
 backends/arm/test/tester/arm_tester.py        | 47 +++++++-----
 backends/arm/test/tester/test_pipeline.py     | 71 +++++++++++--------
 28 files changed, 287 insertions(+), 208 deletions(-)

diff --git a/.lintrunner.toml b/.lintrunner.toml
index 84d2305ec15..2d279b16d64 100644
--- a/.lintrunner.toml
+++ b/.lintrunner.toml
@@ -511,7 +511,6 @@ include_patterns = ['backends/arm/**/*.py']
 exclude_patterns = [
   'third-party/**',
   '**/third-party/**',
-  'backends/arm/test/**',
 ]
 command = [
   'python','-m','lintrunner_adapters','run','docformatter_linter','--config=pyproject.toml','--','@{{PATHSFILE}}'
diff --git a/backends/arm/test/common.py b/backends/arm/test/common.py
index c68d75bede0..15354c5005a 100644
--- a/backends/arm/test/common.py
+++ b/backends/arm/test/common.py
@@ -27,8 +27,8 @@
 
 
 def get_time_formatted_path(path: str, log_prefix: str) -> str:
-    """
-    Returns the log path with the current time appended to it. Used for debugging.
+    """Returns the log path with the current time appended to it. Used for
+    debugging.
 
     Args:
         path: The path to the folder where the log file will be stored.
@@ -36,6 +36,7 @@ def get_time_formatted_path(path: str, log_prefix: str) -> str:
 
     Example output:
         './my_log_folder/test_INT_artifact_28-Nov-14:14:38.log'
+
     """
     return str(
         Path(path) / f"{log_prefix}_{datetime.now().strftime('%d-%b-%H:%M:%S')}.log"
@@ -43,8 +44,7 @@ def get_time_formatted_path(path: str, log_prefix: str) -> str:
 
 
 def maybe_get_tosa_collate_path() -> str | None:
-    """
-    Checks the environment variable TOSA_TESTCASES_BASE_PATH and returns the
+    """Checks the environment variable TOSA_TESTCASES_BASE_PATH and returns the
     path to the where to store the current tests if it is set.
     """
     tosa_test_base = os.environ.get("TOSA_TESTCASES_BASE_PATH")
@@ -161,8 +161,8 @@ def get_vgf_compile_spec(
     custom_path: Optional[str] = None,
     tosa_debug_mode: VgfCompileSpec.DebugMode | None = None,
 ) -> VgfCompileSpec:
-    """Get the ArmCompileSpec for the default VGF tests, to modify
-    the compile spec before calling .build() to finalize it.
+    """Get the ArmCompileSpec for the default VGF tests, to modify the compile
+    spec before calling .build() to finalize it.
     """
 
     if not custom_path:
@@ -198,7 +198,9 @@ def get_vgf_compile_spec(
     raises=FileNotFoundError,
     reason="Did not find Corstone-300 FVP or executor_runner on path",
 )
-"""Xfails a test if Corsone300 FVP is not installed, or if the executor runner is not built"""
+"""Xfails a test if Corsone300 FVP is not installed, or if the executor runner
+is not built.
+"""
 
 XfailIfNoCorstone320 = pytest.mark.xfail(
     condition=not (
@@ -207,21 +209,23 @@ def get_vgf_compile_spec(
     raises=FileNotFoundError,
     reason="Did not find Corstone-320 FVP or executor_runner on path",
 )
-"""Xfails a test if Corsone320 FVP is not installed, or if the executor runner is not built"""
+"""Xfails a test if Corsone320 FVP is not installed, or if the executor runner
+is not built.
+"""
 
 SkipIfNoModelConverter = pytest.mark.skipif(  # type: ignore[call-arg]
     condition=not (model_converter_installed()),
     raises=FileNotFoundError,
     reason="Did not find model-converter on path",
 )
-"""Skips a test if model-converter is not installed"""
+"""Skips a test if model-converter is not installed."""
 
 XfailfNoVKMLEmulationLayer = pytest.mark.xfail(
     condition=not (vkml_emulation_layer_installed()),
     raises=TypeError,
     reason="VKML environment is not set properly or executor_runner path is misused",
 )
-"""Xfails a test if VKML Emulation Layer is not installed"""
+"""Xfails a test if VKML Emulation Layer is not installed."""
 
 xfail_type = str | tuple[str, type[Exception]]
 
@@ -238,12 +242,14 @@ def parametrize(
     strict: bool = True,
     flakies: dict[str, int] | None = None,
 ) -> Decorator:
-    """
-    Custom version of pytest.mark.parametrize with some syntatic sugar and added xfail functionality
-        - test_data is expected as a dict of (id, test_data) pairs
-        - alllows to specifiy a dict of (id, failure_reason) pairs to mark specific tests as xfail.
-          Failure_reason can be str, type[Exception], or tuple[str, type[Exception]].
-          Strings set the reason for failure, the exception type sets expected error.
+    """Custom version of pytest.mark.parametrize with some syntatic sugar and
+    added xfail functionality.
+
+    - test_data is expected as a dict of (id, test_data) pairs
+    - alllows to specifiy a dict of (id, failure_reason) pairs to mark specific tests as xfail.
+      Failure_reason can be str, type[Exception], or tuple[str, type[Exception]].
+      Strings set the reason for failure, the exception type sets expected error.
+
     """
     if xfails is None:
         xfails = {}
@@ -253,7 +259,9 @@ def parametrize(
         flakies = {}
 
     def decorator_func(func: Callable[_P, _R]) -> Callable[_P, _R]:
-        """Test data is transformed from a dict of (id, data) pairs to a list of pytest params to work with the native pytests parametrize function"""
+        """Test data is transformed from a dict of (id, data) pairs to a list of
+        pytest params to work with the native pytests parametrize function.
+        """
         pytest_testsuite = []
         for id, test_parameters in test_data.items():
             if id in flakies:
diff --git a/backends/arm/test/conftest.py b/backends/arm/test/conftest.py
index b52091b833a..351d6de7a09 100644
--- a/backends/arm/test/conftest.py
+++ b/backends/arm/test/conftest.py
@@ -64,10 +64,10 @@ def pytest_sessionfinish(session, exitstatus):
 
 @pytest.fixture(autouse=True)
 def set_random_seed():
-    """
-    Control random numbers in Arm test suite. Default behavior is to use a fixed
-    seed (0), which ensures reproducible tests. Use the env variable ARM_TEST_SEED
-    to set a custom seed, or set it to RANDOM for random seed behavior.
+    """Control random numbers in Arm test suite. Default behavior is to use a
+    fixed seed (0), which ensures reproducible tests. Use the env variable
+    ARM_TEST_SEED to set a custom seed, or set it to RANDOM for random seed
+    behavior.
 
     Examples:
     As default use fixed seed (0) for reproducible tests
@@ -76,6 +76,7 @@ def set_random_seed():
         ARM_TEST_SEED=RANDOM pytest --config-file=/dev/null --verbose -s --color=yes  backends/arm/test/ops/test_avg_pool.py -k <TESTCASE>
     Rerun with a specific seed
         ARM_TEST_SEED=3478246 pytest --config-file=/dev/null --verbose -s --color=yes  backends/arm/test/ops/test_avg_pool.py -k <TESTCASE>
+
     """
     import torch
 
@@ -100,12 +101,12 @@ def set_random_seed():
 
 
 def is_option_enabled(option: str, fail_if_not_enabled: bool = False) -> bool:
-    """
-    Returns whether an option is successfully enabled, i.e. if the flag was
+    """Returns whether an option is successfully enabled, i.e. if the flag was
     given to pytest and the necessary requirements are available.
 
-    The optional parameter 'fail_if_not_enabled' makes the function raise
-      a RuntimeError instead of returning False.
+    The optional parameter 'fail_if_not_enabled' makes the function raise a
+    RuntimeError instead of returning False.
+
     """
 
     if hasattr(pytest, "_test_options") and option in pytest._test_options and pytest._test_options[option]:  # type: ignore[attr-defined]
@@ -118,11 +119,11 @@ def is_option_enabled(option: str, fail_if_not_enabled: bool = False) -> bool:
 
 
 def get_option(option: str) -> Any | None:
-    """
-    Returns the value of an pytest option if it is set, otherwise None.
+    """Returns the value of an pytest option if it is set, otherwise None.
 
     Args:
         option (str): The option to check for.
+
     """
     if option in pytest._test_options:  # type: ignore[attr-defined]
         return pytest._test_options[option]  # type: ignore[attr-defined]
diff --git a/backends/arm/test/misc/test_dim_order.py b/backends/arm/test/misc/test_dim_order.py
index 14e12461652..4c36fcd9e89 100644
--- a/backends/arm/test/misc/test_dim_order.py
+++ b/backends/arm/test/misc/test_dim_order.py
@@ -1,4 +1,4 @@
-# Copyright 2024-2025 Arm Limited and/or its affiliates.
+# Copyright 2024-2026 Arm Limited and/or its affiliates.
 #
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
@@ -21,9 +21,8 @@
 
 
 class ChannelsLastInput(torch.nn.Module):
-    """
-    Test a complex case with (channels last, channels first) input,
-    and  (channels first, channels last) output.
+    """Test a complex case with (channels last, channels first) input, and
+    (channels first, channels last) output.
     """
 
     inputs: input_t1 = (
@@ -39,9 +38,7 @@ def forward(self, x, y):
 
 
 class ChannelsFirstOutput(torch.nn.Module):
-    """
-    Test coverting to channels_first inside the delegate.
-    """
+    """Test coverting to channels_first inside the delegate."""
 
     inputs: input_t1 = (
         torch.arange(1, 25, dtype=torch.float32)
@@ -55,9 +52,7 @@ def forward(self, x):
 
 
 class ChannelsLastOutput(torch.nn.Module):
-    """
-    Test changing of dim_order inside the delegate.
-    """
+    """Test changing of dim_order inside the delegate."""
 
     inputs: input_t1 = (torch.arange(1, 9, dtype=torch.float32).reshape((1, 2, 2, 2)),)
 
@@ -68,8 +63,8 @@ def forward(self, x):
 
 
 class ChannelsLastInsidePartition(torch.nn.Module):
-    """
-    Test dim_order changes inside the partiton, but no dim_order changes at input/output.
+    """Test dim_order changes inside the partiton, but no dim_order changes at
+    input/output.
     """
 
     inputs: input_t1 = (torch.randn((1, 2, 3, 3)),)
diff --git a/backends/arm/test/misc/test_extract_io_params_tosa.py b/backends/arm/test/misc/test_extract_io_params_tosa.py
index 229970b2be0..cd1a6e37d43 100644
--- a/backends/arm/test/misc/test_extract_io_params_tosa.py
+++ b/backends/arm/test/misc/test_extract_io_params_tosa.py
@@ -1,4 +1,4 @@
-# Copyright 2025 Arm Limited and/or its affiliates.
+# Copyright 2025-2026 Arm Limited and/or its affiliates.
 #
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
@@ -45,9 +45,7 @@ def test_roundtrip_extracts_io_params_tosa_INT(
     quantizer_cls,
     partitioner_cls,
 ):
-    """
-    Validates that IO quantization parameters round-trip for both flows.
-    """
+    """Validates that IO quantization parameters round-trip for both flows."""
     example_inputs = (
         torch.ones(1, 5),
         torch.full((1, 5), 2.0),
diff --git a/backends/arm/test/misc/test_non_persistent_buffers.py b/backends/arm/test/misc/test_non_persistent_buffers.py
index 374eb0a57d6..50648e5867d 100644
--- a/backends/arm/test/misc/test_non_persistent_buffers.py
+++ b/backends/arm/test/misc/test_non_persistent_buffers.py
@@ -1,4 +1,4 @@
-# Copyright 2025 Arm Limited and/or its affiliates.
+# Copyright 2025-2026 Arm Limited and/or its affiliates.
 #
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
@@ -14,9 +14,7 @@
 
 
 class NonPersistentBuffer(nn.Module):
-    """
-    Min code version registering a non-persistent input buffer.
-    """
+    """Min code version registering a non-persistent input buffer."""
 
     def __init__(self):
         super().__init__()
@@ -33,17 +31,15 @@ def forward(self, x):
 
 @parametrize("test_data", test_input)
 def test_non_persistent_buffer_tosa_FP(test_data: input_t):
-    """
-    Test validates Arm backend handling of non-persistent buffers
-    and ensures that there are no asserts or errors when they are used.
+    """Test validates Arm backend handling of non-persistent buffers and ensures
+    that there are no asserts or errors when they are used.
     """
     TosaPipelineFP[input_t](NonPersistentBuffer(), test_data, "").run()
 
 
 @parametrize("test_data", test_input)
 def test_non_persistent_buffer_tosa_INT(test_data: input_t):
-    """
-    Test validates Arm backend handling of non-persistent buffers
-    and ensures that there are no asserts or errors when they are used.
+    """Test validates Arm backend handling of non-persistent buffers and ensures
+    that there are no asserts or errors when they are used.
     """
     TosaPipelineINT[input_t](NonPersistentBuffer(), test_data, "").run()
diff --git a/backends/arm/test/misc/test_partition_decomposed_quantized_ops.py b/backends/arm/test/misc/test_partition_decomposed_quantized_ops.py
index 0514ad5e280..974f3dd3349 100644
--- a/backends/arm/test/misc/test_partition_decomposed_quantized_ops.py
+++ b/backends/arm/test/misc/test_partition_decomposed_quantized_ops.py
@@ -1,4 +1,4 @@
-# Copyright 2025 Arm Limited and/or its affiliates.
+# Copyright 2025-2026 Arm Limited and/or its affiliates.
 #
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
@@ -48,7 +48,11 @@
 
 
 class SoftplusModule(torch.nn.Module):
-    """Module containing an addition followed by a Softplus. Softplus is currently not supported by TosaBackend."""
+    """Module containing an addition followed by a Softplus.
+
+    Softplus is currently not supported by TosaBackend.
+
+    """
 
     def __init__(self):
         super().__init__()
@@ -59,8 +63,11 @@ def forward(self, x: torch.Tensor):
 
 
 class LinearResidualModule(torch.nn.Module):
-    """Module containing a residual and a linear layer followed by GELU and a Dropout.
+    """Module containing a residual and a linear layer followed by GELU and a
+    Dropout.
+
     GELU is currently not supported by TosaBackend nor TosaQuantizer.
+
     """
 
     def __init__(
diff --git a/backends/arm/test/misc/test_qat_training_loop.py b/backends/arm/test/misc/test_qat_training_loop.py
index 425849b8564..6a45ac09489 100644
--- a/backends/arm/test/misc/test_qat_training_loop.py
+++ b/backends/arm/test/misc/test_qat_training_loop.py
@@ -1,4 +1,4 @@
-# Copyright 2025 Arm Limited and/or its affiliates.
+# Copyright 2025-2026 Arm Limited and/or its affiliates.
 #
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
@@ -46,8 +46,12 @@ def evaluate_model(model, inputs, expected_outputs):
 
 def test_qat_training_loop_tosa_INT():
     """Test the QAT training loop with a simple MLP model.
-    This function creates a simple MLP model, prepares it for QAT, runs a training loop,
-    and evaluates the quantized model to make sure everything works as expected."""
+
+    This function creates a simple MLP model, prepares it for QAT, runs a
+    training loop, and evaluates the quantized model to make sure everything
+    works as expected.
+
+    """
 
     model = MLP()
     logger.info("Starting training loop test")
diff --git a/backends/arm/test/misc/test_quant_custom_meta.py b/backends/arm/test/misc/test_quant_custom_meta.py
index 59156c2fd57..cd9964f4511 100644
--- a/backends/arm/test/misc/test_quant_custom_meta.py
+++ b/backends/arm/test/misc/test_quant_custom_meta.py
@@ -1,4 +1,4 @@
-# Copyright 2025 Arm Limited and/or its affiliates.
+# Copyright 2025-2026 Arm Limited and/or its affiliates.
 #
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
@@ -19,11 +19,13 @@ def forward(self, x, y):
 
 @pytest.mark.parametrize("fp_extension", [True, False])
 def test_qdq_squeezed_fp_op_tosa_INT_FP(fp_extension: bool):
-    """Test that a float operation surrounded by quantize-dequantize pairs
-    is correctly handled by the partitioner and the TOSA backend.
+    """Test that a float operation surrounded by quantize-dequantize pairs is
+    correctly handled by the partitioner and the TOSA backend.
+
     Pattern:
     q -> dq -> add -> q -> dq -> sigmoid -> q -> dq -> mul -> dq -> q
                         |_____unquantized_____|
+
     """
     aten_op = "torch.ops.aten.add.Tensor"
     exir_op = "executorch_exir_dialects_edge__ops_aten_add_Tensor"
@@ -67,11 +69,13 @@ def forward(self, x, y):
 
 @pytest.mark.parametrize("fp_extension", [True, False])
 def test_quantized_to_float_transition_tosa_INT_FP(fp_extension: bool):
-    """Test that a model executing quantized ops followed by float ops
-    is correctly handled by the partitioner and the TOSA backend.
+    """Test that a model executing quantized ops followed by float ops is
+    correctly handled by the partitioner and the TOSA backend.
+
     Pattern:
     q -> dq -> mul -> q -> dq -> add -> q -> dq -> sigmoid -> conv
                                            |___unquantized___|
+
     """
     aten_op = "torch.ops.aten.add.Tensor"
     exir_op = "executorch_exir_dialects_edge__ops_aten_add_Tensor"
diff --git a/backends/arm/test/misc/test_shared_qspecs.py b/backends/arm/test/misc/test_shared_qspecs.py
index 7a27727e4a6..8324209f5b2 100644
--- a/backends/arm/test/misc/test_shared_qspecs.py
+++ b/backends/arm/test/misc/test_shared_qspecs.py
@@ -34,8 +34,8 @@ def forward(self, x, y):
 
 
 def _get_quantizer() -> TOSAQuantizer:
-    """
-    Returns a TOSAQuantizer configured for int8 quantization with SubOp unquantized.
+    """Returns a TOSAQuantizer configured for int8 quantization with SubOp
+    unquantized.
     """
     quantizer = TOSAQuantizer(TosaSpecification.create_from_string("TOSA-1.0+INT"))
     quantizer.set_global(_QUANT_CONFIG_INT8)
@@ -114,7 +114,9 @@ def forward(self, x):
 
 
 class SharedQspecInputForkNonShared(torch.nn.Module):
-    """Shared qspec cluster with an input fork with both inputs as non-shared qspecs."""
+    """Shared qspec cluster with an input fork with both inputs as non-shared
+    qspecs.
+    """
 
     qspecs = {
         "quantized_decomposed.quantize_per_tensor.default": {None: 4},
@@ -137,7 +139,9 @@ def forward(self, x, y):
 
 
 class SharedQspecInputForkShared(torch.nn.Module):
-    """Shared qspec cluster with an input fork with both inputs as shared qspecs."""
+    """Shared qspec cluster with an input fork with both inputs as shared
+    qspecs.
+    """
 
     qspecs = {
         "quantized_decomposed.quantize_per_tensor.default": {None: 5},
@@ -162,7 +166,9 @@ def forward(self, x, y):
 
 
 class SharedQspecInputForkXShared(torch.nn.Module):
-    """Shared qspec cluster with an input fork with left input as shared qspec."""
+    """Shared qspec cluster with an input fork with left input as shared
+    qspec.
+    """
 
     qspecs = {
         "quantized_decomposed.quantize_per_tensor.default": {None: 4},
@@ -186,7 +192,9 @@ def forward(self, x, y):
 
 
 class SharedQspecInputForkYShared(torch.nn.Module):
-    """Shared qspec cluster with an input fork with right input as shared qspec."""
+    """Shared qspec cluster with an input fork with right input as shared
+    qspec.
+    """
 
     qspecs = {
         "quantized_decomposed.quantize_per_tensor.default": {None: 5},
@@ -210,7 +218,9 @@ def forward(self, x, y):
 
 
 class SharedQspecInputForkXConstant(torch.nn.Module):
-    """Shared qspec cluster with an input fork with left input as global constant."""
+    """Shared qspec cluster with an input fork with left input as global
+    constant.
+    """
 
     qspecs = {
         "quantized_decomposed.quantize_per_tensor.default": {None: 2},
@@ -233,7 +243,9 @@ def forward(self, x):
 
 
 class SharedQspecInputForkYConstant(torch.nn.Module):
-    """Shared qspec cluster with an input fork with left input as local constant."""
+    """Shared qspec cluster with an input fork with left input as local
+    constant.
+    """
 
     qspecs = {
         "quantized_decomposed.quantize_per_tensor.default": {None: 2},
@@ -255,7 +267,9 @@ def forward(self, x):
 
 
 class SharedQspecOutputForkNonShared(torch.nn.Module):
-    """Shared qspec cluster with an output fork with both outputs as non-shared qspecs."""
+    """Shared qspec cluster with an output fork with both outputs as non-shared
+    qspecs.
+    """
 
     qspecs = {
         "quantized_decomposed.quantize_per_tensor.default": {None: 3},
@@ -282,7 +296,9 @@ def forward(self, x):
 
 
 class SharedQspecOutputForkShared(torch.nn.Module):
-    """Shared qspec cluster with an output fork with both outputs as shared qspecs."""
+    """Shared qspec cluster with an output fork with both outputs as shared
+    qspecs.
+    """
 
     qspecs = {
         "quantized_decomposed.quantize_per_tensor.default": {None: 4},
@@ -307,7 +323,9 @@ def forward(self, x):
 
 
 class SharedQspecManyForks(torch.nn.Module):
-    """Shared qspec cluster with a number of forks to test more complex structures."""
+    """Shared qspec cluster with a number of forks to test more complex
+    structures.
+    """
 
     qspecs = {
         "quantized_decomposed.quantize_per_tensor.default": {None: 6},
@@ -334,7 +352,9 @@ def forward(self, x):
 
 
 class SharedQspecSurroundedQuantizedOp(torch.nn.Module):
-    """An annotated int8 surrounded by a shared qspec cluster forcing input/output qparams to be equal."""
+    """An annotated int8 surrounded by a shared qspec cluster forcing
+    input/output qparams to be equal.
+    """
 
     qspecs = {
         "quantized_decomposed.quantize_per_tensor.default": {None: 4},
@@ -360,7 +380,7 @@ def forward(self, x):
 
 
 class SharedQspecSurroundedQuantizedOpConstant(torch.nn.Module):
-    """ """
+    """"""
 
     qspecs = {
         "quantized_decomposed.quantize_per_tensor.default": {None: 5},
@@ -389,7 +409,7 @@ def forward(self, x):
 
 
 class SharedQspecSub(torch.nn.Module):
-    """A shared qspec node with float input"""
+    """A shared qspec node with float input."""
 
     qspecs = {
         "quantized_decomposed.quantize_per_tensor.default": {None: 2},
@@ -416,7 +436,9 @@ def forward(self, x, y):
 
 
 class SharedQspecCompetingQspecs(torch.nn.Module):
-    """A shared qspec node with per-channel/per-tensor annotated nodes as inputs"""
+    """A shared qspec node with per-channel/per-tensor annotated nodes as
+    inputs.
+    """
 
     qspecs = {
         "quantized_decomposed.quantize_per_tensor.default": {None: 3},
@@ -456,7 +478,7 @@ def forward(self, x):
 
 
 class SharedQspecNoQspecs(torch.nn.Module):
-    """A shared qspec node with float input/outputs"""
+    """A shared qspec node with float input/outputs."""
 
     qspecs = {
         "quantized_decomposed.quantize_per_tensor.default": {None: 2},
@@ -500,7 +522,7 @@ def forward(self, x):
 
 
 class MixedMaximumInt8Int16(torch.nn.Module):
-    """A shared qspec node with int16/int8 inputs"""
+    """A shared qspec node with int16/int8 inputs."""
 
     qspecs = {
         "quantized_decomposed.quantize_per_tensor.default": {None: 6},
diff --git a/backends/arm/test/misc/test_tosa_spec.py b/backends/arm/test/misc/test_tosa_spec.py
index dafe1169394..7170c7ed4d1 100644
--- a/backends/arm/test/misc/test_tosa_spec.py
+++ b/backends/arm/test/misc/test_tosa_spec.py
@@ -71,7 +71,7 @@
 
 
 class TestTosaSpecification(unittest.TestCase):
-    """Tests the TOSA specification class"""
+    """Tests the TOSA specification class."""
 
     @parameterized.expand(test_valid_strings)  # type: ignore[misc]
     def test_version_string_no_target(self, version_string: str, expected_type):
@@ -125,7 +125,7 @@ def test_supports_new_1_1_extensions_no_target(self):
 
 
 class TestTosaSpecMapping(unittest.TestCase):
-    """Tests the TosaSpecMapping class"""
+    """Tests the TosaSpecMapping class."""
 
     def test_mapping_no_target(self):
         mapping = TosaSpecMapping()
diff --git a/backends/arm/test/models/stable_diffusion/stable_diffusion_module_test_configs.py b/backends/arm/test/models/stable_diffusion/stable_diffusion_module_test_configs.py
index 86e945311c7..89f6e7c13ed 100644
--- a/backends/arm/test/models/stable_diffusion/stable_diffusion_module_test_configs.py
+++ b/backends/arm/test/models/stable_diffusion/stable_diffusion_module_test_configs.py
@@ -1,4 +1,4 @@
-# Copyright 2025 Arm Limited and/or its affiliates.
+# Copyright 2025-2026 Arm Limited and/or its affiliates.
 #
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
@@ -18,7 +18,6 @@
 
 from transformers import CLIPTextConfig, T5Config
 
-
 """
 This file defines test configs used to initialize Stable Diffusion module tests.
 Module tests in the same directory will import these configs.
diff --git a/backends/arm/test/models/test_nn_functional.py b/backends/arm/test/models/test_nn_functional.py
index ff159e7f8ea..14201ed54e4 100644
--- a/backends/arm/test/models/test_nn_functional.py
+++ b/backends/arm/test/models/test_nn_functional.py
@@ -2,7 +2,6 @@
 
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
-
 """Tests 10 popular torch.nn.functional not tested in other ways or training
 related.
 
diff --git a/backends/arm/test/models/test_nn_modules.py b/backends/arm/test/models/test_nn_modules.py
index 733e1fc8986..c3aaa61799b 100644
--- a/backends/arm/test/models/test_nn_modules.py
+++ b/backends/arm/test/models/test_nn_modules.py
@@ -2,7 +2,6 @@
 
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
-
 """Tests 10 popular nn modules not tested in other ways or training-related.
 
 - Embedding
diff --git a/backends/arm/test/models/test_torch_functions.py b/backends/arm/test/models/test_torch_functions.py
index 03271ce1246..0ca8d3ac091 100644
--- a/backends/arm/test/models/test_torch_functions.py
+++ b/backends/arm/test/models/test_torch_functions.py
@@ -2,7 +2,6 @@
 
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
-
 """Tests 10 popular torch ops, not tested in other ways, training related or
 requiring randomness.
 
diff --git a/backends/arm/test/ops/test_div_tensor_mode.py b/backends/arm/test/ops/test_div_tensor_mode.py
index 88fe151c69f..d9d058fccc6 100644
--- a/backends/arm/test/ops/test_div_tensor_mode.py
+++ b/backends/arm/test/ops/test_div_tensor_mode.py
@@ -19,7 +19,9 @@
 
 
 class DivTensorModeFloat(torch.nn.Module):
-    """torch.div(x, y, rounding_mode=mode) with mode in {None, "floor", "trunc"}."""
+    """torch.div(x, y, rounding_mode=mode) with mode in {None, "floor",
+    "trunc"}.
+    """
 
     aten_ops = ["aten.div.Tensor_mode"]
     aten_ops_int = ["aten.mul.Tensor", "aten.reciprocal.default"]
diff --git a/backends/arm/test/ops/test_max_pool1d.py b/backends/arm/test/ops/test_max_pool1d.py
index 4d75cd8529a..4c7e9006555 100644
--- a/backends/arm/test/ops/test_max_pool1d.py
+++ b/backends/arm/test/ops/test_max_pool1d.py
@@ -4,15 +4,14 @@
 #
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
-
-"""
-Tests for the max_pool1d operation.
+"""Tests for the max_pool1d operation.
 
 In PyTorch, max_pool1d may be decomposed internally into a sequence of
 operations (e.g., unsqueeze -> max_pool2d_with_indices -> getitem -> squeeze),
 but this test focuses on ensuring that the max_pool1d aten op is correctly
-lowered/quantized and delegated to the expected edge dialect op on the
-Arm backend (U55/U85).
+lowered/quantized and delegated to the expected edge dialect op on the Arm
+backend (U55/U85).
+
 """
 
 from typing import Callable, Tuple
diff --git a/backends/arm/test/ops/test_scalars.py b/backends/arm/test/ops/test_scalars.py
index 9854772f849..d1fbefad745 100644
--- a/backends/arm/test/ops/test_scalars.py
+++ b/backends/arm/test/ops/test_scalars.py
@@ -2,20 +2,6 @@
 #
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
-
-
-from typing import Tuple
-
-import pytest
-
-import torch
-
-from executorch.backends.arm.test import common
-from executorch.backends.arm.test.tester.test_pipeline import (
-    TosaPipelineFP,
-    TosaPipelineINT,
-)
-
 """Summary of non-working cases.
 
 FP:
@@ -32,6 +18,19 @@
     Sub or inplace-sub with an integer input.
 
 """
+
+from typing import Tuple
+
+import pytest
+
+import torch
+
+from executorch.backends.arm.test import common
+from executorch.backends.arm.test.tester.test_pipeline import (
+    TosaPipelineFP,
+    TosaPipelineINT,
+)
+
 input_t1 = Tuple[torch.Tensor, torch.scalar_tensor]  # Input x, Input y
 
 
diff --git a/backends/arm/test/ops/test_sdpa.py b/backends/arm/test/ops/test_sdpa.py
index 4ed68daca04..0ffab82edbc 100644
--- a/backends/arm/test/ops/test_sdpa.py
+++ b/backends/arm/test/ops/test_sdpa.py
@@ -106,9 +106,13 @@ def test_sdpa_vgf_quant(test_case: test_case_t):
 
 @common.parametrize("test_case", test_suite)
 def test_sdpa_u55_INT(test_case: test_case_t):
-    """Verify SDPA compiles on U55. _safe_softmax from SDPA is skipped by
-    DecomposeSoftmaxPass (skip_safe_softmax=True for U55) and runs on CPU,
-    avoiding REDUCE_MAX which fails Vela compilation."""
+    """Verify SDPA compiles on U55.
+
+    _safe_softmax from SDPA is skipped by DecomposeSoftmaxPass
+    (skip_safe_softmax=True for U55) and runs on CPU, avoiding REDUCE_MAX which
+    fails Vela compilation.
+
+    """
     model, test_input = test_case()
     pipeline = EthosU55PipelineINT[input_t](model, test_input, [], [])
     pipeline.pop_stage("check.quant_nodes")
@@ -120,8 +124,12 @@ def test_sdpa_u55_INT(test_case: test_case_t):
 @common.parametrize("test_case", test_suite)
 @common.XfailIfNoCorstone320
 def test_sdpa_u85_INT(test_case: test_case_t):
-    """Verify SDPA compiles on U85. _safe_softmax is decomposed with stable
-    softmax (including amax/REDUCE_MAX) which is supported on U85."""
+    """Verify SDPA compiles on U85.
+
+    _safe_softmax is decomposed with stable softmax (including amax/REDUCE_MAX)
+    which is supported on U85.
+
+    """
     model, test_input = test_case()
     pipeline = EthosU85PipelineINT[input_t](model, test_input, [], [])
     pipeline.pop_stage("check.quant_nodes")
diff --git a/backends/arm/test/passes/test_decompose_int16_activation_conv_pass.py b/backends/arm/test/passes/test_decompose_int16_activation_conv_pass.py
index 834bc7f7663..dd3f742cf84 100644
--- a/backends/arm/test/passes/test_decompose_int16_activation_conv_pass.py
+++ b/backends/arm/test/passes/test_decompose_int16_activation_conv_pass.py
@@ -1,16 +1,15 @@
-# Copyright 2025 Arm Limited and/or its affiliates.
+# Copyright 2025-2026 Arm Limited and/or its affiliates.
 #
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
-
-"""
-Tests for DecomposeConvWithInt16ActivationPass.
+"""Tests for DecomposeConvWithInt16ActivationPass.
 
 This pass decomposes convolution with int16 activation and bias into:
 - A convolution without bias
 - A rescale to int32
 - An add with the reshaped bias
 - A rescale back to the output dtype
+
 """
 
 from typing import Tuple
@@ -107,9 +106,8 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
         return x
 
 
-def test_decompose_int16_conv_pass_fp32_no_decomposition() -> None:
-    """
-    Test that DecomposeConvWithInt16ActivationPass does NOT decompose
+def test_decompose_conv_with_int16_activation_no_target_fp32_no_decomposition() -> None:
+    """Test that DecomposeConvWithInt16ActivationPass does NOT decompose
     convolution when using FP32 (no quantization).
     """
     module = Conv2dWithBias()
@@ -137,11 +135,12 @@ def test_decompose_int16_conv_pass_fp32_no_decomposition() -> None:
 exir_op = "executorch_exir_dialects_edge__ops_aten_convolution_default"
 
 
-def test_conv2d_int16_e2e_tosa_single_conv() -> None:
-    """
-    End-to-end test for conv2d with INT16 quantization using TOSA pipeline.
-    This validates the full lowering path including the decomposition pass
-    for a single convolution with bias.
+def test_decompose_conv_with_int16_activation_tosa_INT_single_conv() -> None:
+    """End-to-end test for conv2d with INT16 quantization using TOSA pipeline.
+
+    This validates the full lowering path including the decomposition pass for a
+    single convolution with bias.
+
     """
     module = Conv2dWithBias()
     pipeline = TosaPipelineINT[input_t](
@@ -154,11 +153,12 @@ def test_conv2d_int16_e2e_tosa_single_conv() -> None:
     pipeline.run()
 
 
-def test_conv2d_int16_e2e_tosa_multiple_convs() -> None:
-    """
-    End-to-end test for conv2d with INT16 quantization using TOSA pipeline.
-    This validates the full lowering path including the decomposition pass
-    for multiple convolutions with bias.
+def test_decompose_conv_with_int16_activation_tosa_INT_multiple_convs() -> None:
+    """End-to-end test for conv2d with INT16 quantization using TOSA pipeline.
+
+    This validates the full lowering path including the decomposition pass for
+    multiple convolutions with bias.
+
     """
     module = Conv2dMultipleConvs()
     pipeline = TosaPipelineINT[input_t](
@@ -171,10 +171,11 @@ def test_conv2d_int16_e2e_tosa_multiple_convs() -> None:
     pipeline.run()
 
 
-def test_conv2d_int16_e2e_tosa_without_bias() -> None:
-    """
-    End-to-end test for conv2d without bias with INT16 quantization.
+def test_decompose_conv_with_int16_activation_tosa_INT_without_bias() -> None:
+    """End-to-end test for conv2d without bias with INT16 quantization.
+
     This validates that convolutions without bias don't get decomposed.
+
     """
     module = Conv2dWithoutBias()
     pipeline = TosaPipelineINT[input_t](
@@ -187,10 +188,11 @@ def test_conv2d_int16_e2e_tosa_without_bias() -> None:
     pipeline.run()
 
 
-def test_conv2d_int8_e2e_tosa() -> None:
-    """
-    End-to-end test for conv2d with INT8 quantization using TOSA pipeline.
+def test_decompose_conv_with_int16_activation_tosa_INT_int8() -> None:
+    """End-to-end test for conv2d with INT8 quantization using TOSA pipeline.
+
     This validates that INT8 activations don't trigger the decomposition.
+
     """
     module = Conv2dWithBias()
     pipeline = TosaPipelineINT[input_t](
diff --git a/backends/arm/test/passes/test_decompose_softmax_pass.py b/backends/arm/test/passes/test_decompose_softmax_pass.py
index 8e6b52d78bc..f208f07b6fd 100644
--- a/backends/arm/test/passes/test_decompose_softmax_pass.py
+++ b/backends/arm/test/passes/test_decompose_softmax_pass.py
@@ -107,8 +107,9 @@ def __init__(self, **kwargs):
 
 
 def test_decompose_softmax_tosa_FP_skip_safe_softmax():
-    """Verify skip_safe_softmax=True still decomposes regular softmax
-    using the stable algorithm (with amax and sub)."""
+    """Verify skip_safe_softmax=True still decomposes regular softmax using the
+    stable algorithm (with amax and sub).
+    """
     module = Softmax()
     pipeline = PassPipeline[input_t](
         module,
diff --git a/backends/arm/test/quantizer/test_partial_quantization.py b/backends/arm/test/quantizer/test_partial_quantization.py
index 5fe53d375ce..f8ba1d8d8d5 100644
--- a/backends/arm/test/quantizer/test_partial_quantization.py
+++ b/backends/arm/test/quantizer/test_partial_quantization.py
@@ -158,8 +158,8 @@ def test_disallow_tfa_for_two_skipped_modules_no_target():
 
 def test_disallow_tfa_with_global_none_and_one_quantized_module_no_target():
     """Ensure that with a global None quantization config, only the linear
-    module (with its own quantization config) is quantized, and that the
-    other nodes have `disallow_tfa` set.
+    module (with its own quantization config) is quantized, and that the other
+    nodes have `disallow_tfa` set.
     """
 
     graph_after_quant_stage = _run_quantization_pipeline(
diff --git a/backends/arm/test/quantizer/test_preserve_kwargs.py b/backends/arm/test/quantizer/test_preserve_kwargs.py
index 8bfa912720f..f2ba835a481 100644
--- a/backends/arm/test/quantizer/test_preserve_kwargs.py
+++ b/backends/arm/test/quantizer/test_preserve_kwargs.py
@@ -17,7 +17,9 @@
 
 
 class FullLike(torch.nn.Module):
-    """Since full_like is replaced with full, we only need to test on reference model, not FVP."""
+    """Since full_like is replaced with full, we only need to test on reference
+    model, not FVP.
+    """
 
     test_parameters = {
         "full_like_int_val": lambda: (torch.randn(2, 2, 2, 2) * 50, 3),
diff --git a/backends/arm/test/runner_utils.py b/backends/arm/test/runner_utils.py
index 733783f2a09..273c35de56d 100644
--- a/backends/arm/test/runner_utils.py
+++ b/backends/arm/test/runner_utils.py
@@ -91,13 +91,13 @@ def __init__(
 
 
 def get_input_names(program: ExportedProgram) -> list[str]:
-    """
-    Get a list[str] with the names of the inputs to this model.
+    """Get a list[str] with the names of the inputs to this model.
 
     Args:
         program (ExportedProgram): The program to get input names from.
     Returns:
         A list of strings with the names of the model input.
+
     """
     return [spec.arg.name for spec in program.graph_signature.input_specs]
 
@@ -105,12 +105,14 @@ def get_input_names(program: ExportedProgram) -> list[str]:
 def get_input_quantization_params(
     program: ExportedProgram,
 ) -> list[QuantizationParams]:
-    """
-    Get input QuantizationParams in a program, maximum one per input to the program.
+    """Get input QuantizationParams in a program, maximum one per input to the
+    program.
+
     Args:
         program (ExportedProgram): The program to get input quantization parameters from.
     Returns:
         list[QuantizationParams]: The found quantization parameters.
+
     """
 
     quant_params = []
@@ -142,8 +144,8 @@ def get_input_quantization_params(
 def get_output_quantization_params(
     output_node: Node,
 ) -> dict[Node, QuantizationParams | None]:
-    """
-    Get output QuantizationParams from a program.
+    """Get output QuantizationParams from a program.
+
     Args:
         output_nodes (list(Node)): A list of output nodes to get output quantization parameters from.
     Returns:
@@ -151,6 +153,7 @@ def get_output_quantization_params(
         If no quantization parameters were found, the entry is None.
     Raises:
         RuntimeError if no output quantization parameters are found.
+
     """
     quant_params: dict[Node, QuantizationParams | None] = {}
     for node in output_node.args[0]:  # type: ignore[union-attr]
@@ -207,7 +210,9 @@ def numpy_to_torch_tensor(array: np.ndarray, output_node: Node) -> torch.Tensor:
 
 
 class TosaReferenceModelDispatch(TorchFunctionMode):
-    """A context manager for executing call_delegate nodes using the reference model"""
+    """A context manager for executing call_delegate nodes using the reference
+    model.
+    """
 
     def __init__(self):
         self.ran_tosa_dispatch = False
@@ -376,6 +381,7 @@ def run_corstone(
     timeout: int = 120,  # s
 ) -> list[torch.Tensor]:
     """Executes an inference of the exported_program on FVP.
+
     Returns a list of tensors with the output.
     Args:
         `executorch_program_manager`: The executorch program to run.
@@ -392,6 +398,7 @@ def run_corstone(
         Relies on the output tensors from the exported program
         to figure out the shape and dtype of the buffer that was
         output from the FVP.
+
     """
     exported_program = executorch_program_manager.exported_program()
     intermediate_path = Path(intermediate_path)
@@ -552,7 +559,8 @@ def save_bytes(
     input_name: str,
     quant_param: Optional[QuantizationParams] = None,
 ) -> str:
-    """Serializes and saves 'data' in byte format, possibly quantizing it before.
+    """Serializes and saves 'data' in byte format, possibly quantizing it
+    before.
 
     Parameters:
         path: the directory where to save the data.
@@ -561,6 +569,7 @@ def save_bytes(
         quant_param: the parameters to use for quantization.
     Returns:
         the full file path of the output.
+
     """
     data_np = prep_data_for_save(data, input_name, quant_param)
     file_path = os.path.join(path, input_name + ".bin")
@@ -572,11 +581,11 @@ def save_bytes(
 
 
 def _run_cmd(cmd: List[str], check=True) -> subprocess.CompletedProcess[bytes]:
-    """
-    Run a command and check for errors.
+    """Run a command and check for errors.
 
     Args:
     cmd (List[str]): The command to run as a list.
+
     """
     try:
         result = subprocess.run(  # nosec B603 - cmd constructed from trusted inputs
@@ -599,6 +608,7 @@ def _run_flatc(args: List[str]) -> None:
 
     If a resource matching _FLATC_RESOURCE_NAME exists, uses that executable.
     Otherwise, expects the `flatc` tool to be available on the system path.
+
     """
     flatc_resource = _resources.files(arm_test_package).joinpath(_FLATC_RESOURCE_NAME)
     if flatc_resource.is_file():
@@ -623,9 +633,11 @@ def _run_flatc(args: List[str]) -> None:
 
 
 def dbg_tosa_fb_to_json(tosa_fb: bytes) -> Dict:
-    """
-    This function is used to dump the TOSA flatbuffer to a human readable
-    format, using flatc. It is used for debugging purposes.
+    """This function is used to dump the TOSA flatbuffer to a human readable
+    format, using flatc.
+
+    It is used for debugging purposes.
+
     """
 
     tmp = tempfile.mkdtemp()
diff --git a/backends/arm/test/test_memory_allocator_log.py b/backends/arm/test/test_memory_allocator_log.py
index 3853b60b7f6..87b32391806 100644
--- a/backends/arm/test/test_memory_allocator_log.py
+++ b/backends/arm/test/test_memory_allocator_log.py
@@ -1,15 +1,15 @@
-# Copyright 2025 Arm Limited and/or its affiliates.
+# Copyright 2025-2026 Arm Limited and/or its affiliates.
 #
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
-"""
-Check log files for memory metrics and compare them against thresholds.
+"""Check log files for memory metrics and compare them against thresholds.
 
 Usage example:
   python3 test_memory_allocator_log.py \
     --log path/to/log.txt \
     --require "Total SRAM used" "<= 310 KiB" \
     --require "method_allocator_input" "<= 4 B"
+
 """
 
 import argparse
diff --git a/backends/arm/test/tester/analyze_output_utils.py b/backends/arm/test/tester/analyze_output_utils.py
index 6ba08fd4785..67b6a2cf19f 100644
--- a/backends/arm/test/tester/analyze_output_utils.py
+++ b/backends/arm/test/tester/analyze_output_utils.py
@@ -301,8 +301,8 @@ def dump_error_output(
     rtol: float = 1e-03,
     qtol: float = 0,
 ) -> None:
-    """
-    Prints Quantization info and error tolerances, and saves the differing tensors to disc.
+    """Prints Quantization info and error tolerances, and saves the differing
+    tensors to disc.
     """
     # Capture assertion error and print more info
     banner = "=" * 40 + "TOSA debug info" + "=" * 40
@@ -336,7 +336,7 @@ def dump_error_output(
 
 
 if __name__ == "__main__":
-    """This is expected to produce the example output of print_diff"""
+    """This is expected to produce the example output of print_diff."""
     torch.manual_seed(0)
     a = torch.rand(3, 3, 2, 2) * 0.01
     b = a.clone().detach()
diff --git a/backends/arm/test/tester/arm_tester.py b/backends/arm/test/tester/arm_tester.py
index 2336ccc6233..cbd682eefae 100644
--- a/backends/arm/test/tester/arm_tester.py
+++ b/backends/arm/test/tester/arm_tester.py
@@ -482,10 +482,10 @@ def run_method_and_compare_outputs(
         error_callbacks: Optional[Sequence[Callable[..., None]]] = None,
         run_eager_mode: bool = False,
     ):
-        """
-        Compares the run_artifact output of 'stage' with the output of a reference stage.
-        If the model is quantized, the reference stage is the Quantize stage output.
-        Otherwise, the reference stage is the initial pytorch module.
+        """Compares the run_artifact output of 'stage' with the output of a
+        reference stage. If the model is quantized, the reference stage is the
+        Quantize stage output. Otherwise, the reference stage is the initial
+        pytorch module.
 
         Asserts that the outputs are equal (within tolerances).
         Returns self to allow the function to be run in a test chain.
@@ -495,6 +495,7 @@ def run_method_and_compare_outputs(
                 The default is the latest run stage.
             inputs (Optional[Tuple[torch.Tensor]]): Allows you to input custom input data.
                 The default is random data.
+
         """
 
         # backward-compatible ordering (accept inputs as the first positional argument)
@@ -730,8 +731,7 @@ def check_quantization_annotation(
         input_qspecs: Optional[Dict[QuantizationSpec | None, int]] = None,
         output_qspecs: Optional[Dict[QuantizationSpec | None, int]] = None,
     ):
-        """
-        Check the quantization annotations in the graph of a quantized model.
+        """Check the quantization annotations in the graph of a quantized model.
 
         Args:
             quantization_annotations: A dictionary mapping operator names to a dictionary of
@@ -743,6 +743,7 @@ def check_quantization_annotation(
                 If None, the check is skipped.
 
         Returns self for daisy-chaining.
+
         """
         if not self.is_quantized():
             raise RuntimeError(
@@ -783,13 +784,13 @@ def dump_operator_distribution(
         print_table: bool = True,
         include_dtypes: bool = True,
     ):
-        """Dump the distribution of operators in the current stage.
-        In the partition stage, additional information is included such as the number of
-        delegates and the distribution of TOSA operators.
-        Set parameter print_table to False to dump in a parseable format.
-
+        """Dump the distribution of operators in the current stage. In the
+        partition stage, additional information is included such as the number
+        of delegates and the distribution of TOSA operators. Set parameter
+        print_table to False to dump in a parseable format.
 
         Returns self for daisy-chaining.
+
         """
         line = "#" * 10
         to_print = f"\n{line} {self.cur} Operator Distribution {line}\n"
@@ -868,10 +869,12 @@ def dump_operator_distribution(
     def dump_dtype_distribution(
         self, path_to_dump: Optional[str] = None, print_table: bool = True
     ):
-        """Dump a the distributions of dtypes of nodes and placeholders in the current stage.
-        Set parameter print_table to False to dump in a parseable format.
+        """Dump a the distributions of dtypes of nodes and placeholders in the
+        current stage. Set parameter print_table to False to dump in a parseable
+        format.
 
         Returns self for daisy-chaining.
+
         """
 
         line = "#" * 10
@@ -915,11 +918,12 @@ def run_transform_for_annotation_pipeline(
         """Run transform_for_annotation_pipeline on exported program to ensure
         passes do not break the initial model before quantization.
 
-        There are caveats to this however. As we register buffers to the graph modules
-        the resulting exported graph can fail. Use this only to compare numerical correctness
-        in eager mode.
+        There are caveats to this however. As we register buffers to the graph
+        modules the resulting exported graph can fail. Use this only to compare
+        numerical correctness in eager mode.
 
         Returns exported program with passes applied.
+
         """
 
         if stage is None:
@@ -1038,7 +1042,10 @@ def _get_dtype_distribution(
     graph: Graph, tosa_spec: TosaSpecification
 ) -> tuple[Counter[str], Counter[str]]:
     """Counts the occurences of placeholder and call_function dtypes in a graph.
-    The result is a tuple of Counters (placeholder_distribution, call_function_distribution)
+
+    The result is a tuple of Counters (placeholder_distribution,
+    call_function_distribution)
+
     """
     placeholder_dtypes: list[str] = []
     call_function_dtypes: list[str] = []
@@ -1054,7 +1061,9 @@ def _get_dtype_distribution(
 
 def _get_operator_distribution(graph: Graph) -> List[Tuple[str, int]]:
     """Counts the occurences of operator names in a graph.
+
     The result is a sorted list [('operator name':'number of nodes')]
+
     """
     return sorted(
         Counter(
@@ -1069,7 +1078,9 @@ def _get_operator_distribution(graph: Graph) -> List[Tuple[str, int]]:
 
 def _get_operator_dtype_distribution(graph: Graph) -> List[Tuple[Tuple[str, str], int]]:
     """Counts the occurences of operator names and dtype pairs in a graph.
+
     The result is a sorted list[(('operator name','dtype'),'number of nodes')]
+
     """
     target_dtype_pairs = []
     for node in graph.nodes:
@@ -1104,7 +1115,9 @@ def _get_tosa_operator_distribution(
 ) -> list[Tuple[str, int]] | list[Tuple[Tuple[str, str], int]]:
     """Counts the occurences of operator names of all lowered modules containing
     a TOSA flatbuffer.
+
     The result is a string with the operator distribution or an error message.
+
     """
     id = 0
     unknown_dtype_str = "UNKNOWN"
diff --git a/backends/arm/test/tester/test_pipeline.py b/backends/arm/test/tester/test_pipeline.py
index 5420875bbda..4e060919738 100644
--- a/backends/arm/test/tester/test_pipeline.py
+++ b/backends/arm/test/tester/test_pipeline.py
@@ -51,7 +51,11 @@
 
 logger = logging.getLogger(__name__)
 T = TypeVar("T", bound=Tuple[Any, ...])
-""" Generic type used for test data in the pipeline. Depends on which type the operator expects."""
+"""Generic type used for test data in the pipeline.
+
+Depends on which type the operator expects.
+
+"""
 
 
 def _has_quantizable_inputs(test_data: T) -> bool:
@@ -87,10 +91,10 @@ def update(self, *args, **kwargs):
 
 
 class BasePipeline(Generic[T]):
-    """
-    The BasePipeline defines a list of stages to be applied to a torch.nn.module for lowering it
-    in the Arm backend. To be inherited and adjusted for particular targets. Importantly, the
-    pipeline list can be modified before running the pipeline to support various pipeline extensions
+    """The BasePipeline defines a list of stages to be applied to a
+    torch.nn.module for lowering it in the Arm backend. To be inherited and
+    adjusted for particular targets. Importantly, the pipeline list can be
+    modified before running the pipeline to support various pipeline extensions
     and debugging usecases.
 
     Attributes:
@@ -104,6 +108,7 @@ class BasePipeline(Generic[T]):
                 tester.to_edge_transform_and_lower()
             or
                 tester.to_edge().check(exir_ops).partition()
+
     """
 
     @staticmethod
@@ -157,14 +162,15 @@ def __init__(
         self.add_stage(self.tester.to_executorch)
 
     def add_stage(self, func: Callable, *args, **kwargs):
-        """
-        Adds a stage defined by a function with args and kwargs. By default appends to the pipeline.
-        For stages which may be added multiple times to a pipeline, s.a. checks and debug stages,
-        a suffix is appended with a dot to make sure every id is unique, e.g. check becomes check.0
+        """Adds a stage defined by a function with args and kwargs. By default
+        appends to the pipeline. For stages which may be added multiple times to
+        a pipeline, s.a. checks and debug stages, a suffix is appended with a
+        dot to make sure every id is unique, e.g. check becomes check.0.
 
         Special kwargs:
             pos : specifies position in pipeline to add stage at.
             suffix : specifies a custom suffix to identify non unique stages, instead of a number.
+
         """
         pipeline_length = len(self._stages)
 
@@ -238,7 +244,7 @@ def quantizer(self) -> TOSAQuantizer:
             )
 
     def pop_stage(self, identifier: int | str):
-        """Removes and returns the stage at postion pos"""
+        """Removes and returns the stage at postion pos."""
         if isinstance(identifier, int):
             stage = self._stages.pop(identifier)
         elif isinstance(identifier, str):
@@ -369,8 +375,8 @@ def run(self):
 
 
 class TosaPipelineINT(TOSAPipeline, Generic[T]):
-    """
-    Lowers a graph to INT TOSA spec (with quantization) and tests it with the TOSA reference model.
+    """Lowers a graph to INT TOSA spec (with quantization) and tests it with the
+    TOSA reference model.
 
     Attributes:
        module: The module which the pipeline is applied to.
@@ -395,6 +401,7 @@ class TosaPipelineINT(TOSAPipeline, Generic[T]):
        tosa_version: TOSA version string to target.
        tosa_extensions: Optional list of TOSA extensions.
        epsilon: Epsilon used in quantization configuration.
+
     """
 
     def __init__(
@@ -515,8 +522,8 @@ def __init__(
 
 
 class TosaPipelineFP(TOSAPipeline, Generic[T]):
-    """
-    Lowers a graph to FP TOSA spec and tests it with the TOSA reference model.
+    """Lowers a graph to FP TOSA spec and tests it with the TOSA reference
+    model.
 
     Attributes:
        module: The module which the pipeline is applied to.
@@ -532,6 +539,7 @@ class TosaPipelineFP(TOSAPipeline, Generic[T]):
                      options.
        use_edge_to_transform_and_lower: Selects betweeen two possible ways of lowering the module.
        custom_path : Path to dump intermediate artifacts such as tosa and pte to.
+
     """
 
     def __init__(
@@ -679,8 +687,8 @@ def __init__(
 
 
 class EthosU55PipelineINT(EthosUPipelineINTBase, Generic[T]):
-    """
-    Lowers a graph to u55 INT TOSA spec and tests it on the Corstone300 FVP, if run_on_fvp is true.
+    """Lowers a graph to u55 INT TOSA spec and tests it on the Corstone300 FVP,
+    if run_on_fvp is true.
 
     Attributes:
        module: The module which the pipeline is applied to.
@@ -692,6 +700,7 @@ class EthosU55PipelineINT(EthosUPipelineINTBase, Generic[T]):
        run_on_fvp: Set to true to test the pte file on a fvp simulator.
        use_edge_to_transform_and_lower: Selects between two possible ways of lowering the module.
        custom_path : Path to dump intermediate artifacts such as tosa and pte to.
+
     """
 
     def __init__(
@@ -735,8 +744,8 @@ def __init__(
 
 
 class EthosU85PipelineINT(EthosUPipelineINTBase, Generic[T]):
-    """
-    Lowers a graph to u85 INT TOSA spec and tests it on the Corstone320 FVP, if run_on_fvp is true.
+    """Lowers a graph to u85 INT TOSA spec and tests it on the Corstone320 FVP,
+    if run_on_fvp is true.
 
     Attributes:
        module: The module which the pipeline is applied to.
@@ -748,6 +757,7 @@ class EthosU85PipelineINT(EthosUPipelineINTBase, Generic[T]):
        run_on_fvp: Set to true to test the pte file on a fvp simulator.
        use_edge_to_transform_and_lower: Selects between two possible ways of lowering the module.
        custom_path : Path to dump intermediate artifacts such as tosa and pte to.
+
     """
 
     def __init__(
@@ -791,8 +801,8 @@ def __init__(
 
 
 class PassPipeline(TOSAPipeline, Generic[T]):
-    """
-    Runs single passes directly on an edge_program and checks operators before/after.
+    """Runs single passes directly on an edge_program and checks operators
+    before/after.
 
     Attributes:
         module: The module which the pipeline is applied to.
@@ -811,6 +821,7 @@ class PassPipeline(TOSAPipeline, Generic[T]):
 
     Passes are run in order pass_list -> pass_functions -> passes_with_exported_program.
     See arm_tester.RunPasses() for more information.
+
     """
 
     def __init__(
@@ -889,8 +900,8 @@ def run(self):
 
 
 class TransformAnnotationPassPipeline(TOSAPipeline, Generic[T]):
-    """
-    Runs transform_for_annotation_pipeline passes directly on an exported program and checks output.
+    """Runs transform_for_annotation_pipeline passes directly on an exported
+    program and checks output.
 
     Attributes:
         module: The module which the pipeline is applied to.
@@ -945,9 +956,8 @@ def __init__(
 
 
 class QuantizationPipeline(TOSAPipeline, Generic[T]):
-    """
-    Runs quantization and checks that appropriate nodes are annotated with an expected
-    quantization-spec.
+    """Runs quantization and checks that appropriate nodes are annotated with an
+    expected quantization-spec.
 
     Attributes:
         module: The module which the pipeline is applied to.
@@ -1002,9 +1012,8 @@ def __init__(
 
 
 class OpNotSupportedPipeline(TOSAPipeline, Generic[T]):
-    """
-    Runs the partitioner on a module and checks that ops are not delegated to test
-    SupportedTOSAOperatorChecks.
+    """Runs the partitioner on a module and checks that ops are not delegated to
+    test SupportedTOSAOperatorChecks.
 
     Attributes:
         module: The module which the pipeline is applied to.
@@ -1014,6 +1023,7 @@ class OpNotSupportedPipeline(TOSAPipeline, Generic[T]):
         non_delegated_ops : Exir ops expected not to be delegated.
         n_expected_delegates : Number of delegate calls (0 in the usual case).
         custom_path : Path to dump intermediate artifacts such as tosa and pte to.
+
     """
 
     def __init__(
@@ -1069,8 +1079,8 @@ def __init__(
 
 
 class VgfPipeline(BasePipeline, Generic[T]):
-    """
-    Lowers a graph based on TOSA spec (with or without quantization) and converts TOSA to VFG.
+    """Lowers a graph based on TOSA spec (with or without quantization) and
+    converts TOSA to VFG.
 
     Attributes:
        module: The module which the pipeline is applied to.
@@ -1089,6 +1099,7 @@ class VgfPipeline(BasePipeline, Generic[T]):
 
        use_edge_to_transform_and_lower: Selects betweeen two possible ways of lowering the module.
        custom_path : Path to dump intermediate artifacts such as tosa and pte to.
+
     """
 
     def __init__(