#14983: Update unary backward docs (#15292)

### Ticket
Link to Github Issue #14983 

Problem description

Update docs for unary backward ops

### What's changed
Updated docs for the unary backward ops 


### Checklist
- [x] All Post commit CI

tests/sweep_framework/sweeps/eltwise/unary_backward/acos_bw/acos_bw.py

@@ -6,7 +6,6 @@
 from functools import partial
 
 import torch
-import random
 import ttnn
 
 from tests.sweep_framework.sweep_utils.utils import gen_shapes, sanitize_shape_rm
@@ -15,11 +14,6 @@
 from tests.ttnn.utils_for_testing import check_with_pcc, start_measuring_time, stop_measuring_time
 from models.utility_functions import torch_random
 
-# Override the default timeout in seconds for hang detection.
-TIMEOUT = 30
-
-random.seed(0)
-
 
 # Parameters provided to the test vector generator are defined here.
 # They are defined as dict-type suites that contain the arguments to the run function as keys, and lists of possible inputs as values.
@@ -80,8 +74,7 @@ def run(
     *,
     device,
 ) -> list:
-    data_seed = random.randint(0, 20000000)
-    torch.manual_seed(data_seed)
+    torch.manual_seed(0)
 
     if input_layout == ttnn.ROW_MAJOR_LAYOUT:
         input_shape = sanitize_shape_rm(input_shape)

tests/sweep_framework/sweeps/eltwise/unary_backward/acosh_bw/acosh_bw.py

@@ -6,19 +6,13 @@
 from functools import partial
 
 import torch
-import random
 import ttnn
 from tests.sweep_framework.sweep_utils.utils import gen_shapes, sanitize_shape_rm
 from tests.tt_eager.python_api_testing.sweep_tests.generation_funcs import gen_func_with_cast_tt
 
 from tests.ttnn.utils_for_testing import check_with_pcc, start_measuring_time, stop_measuring_time
 from models.utility_functions import torch_random
 
-# Override the default timeout in seconds for hang detection.
-TIMEOUT = 30
-
-random.seed(0)
-
 
 # Parameters provided to the test vector generator are defined here.
 # They are defined as dict-type suites that contain the arguments to the run function as keys, and lists of possible inputs as values.
@@ -79,8 +73,7 @@ def run(
     *,
     device,
 ) -> list:
-    data_seed = random.randint(0, 20000000)
-    torch.manual_seed(data_seed)
+    torch.manual_seed(0)
 
     if input_layout == ttnn.ROW_MAJOR_LAYOUT:
         input_shape = sanitize_shape_rm(input_shape)

tests/sweep_framework/sweeps/eltwise/unary_backward/cos_bw/cos_bw.py

@@ -6,7 +6,6 @@
 from functools import partial
 
 import torch
-import random
 import ttnn
 
 from tests.sweep_framework.sweep_utils.utils import gen_shapes, sanitize_shape_rm
@@ -15,11 +14,6 @@
 from tests.ttnn.utils_for_testing import check_with_pcc, start_measuring_time, stop_measuring_time
 from models.utility_functions import torch_random
 
-# Override the default timeout in seconds for hang detection.
-TIMEOUT = 30
-
-random.seed(0)
-
 
 # Parameters provided to the test vector generator are defined here.
 # They are defined as dict-type suites that contain the arguments to the run function as keys, and lists of possible inputs as values.
@@ -67,8 +61,7 @@ def run(
     *,
     device,
 ) -> list:
-    data_seed = random.randint(0, 20000000)
-    torch.manual_seed(data_seed)
+    torch.manual_seed(0)
 
     if input_layout == ttnn.ROW_MAJOR_LAYOUT:
         input_shape = sanitize_shape_rm(input_shape)

tests/sweep_framework/sweeps/eltwise/unary_backward/fill_zero_bw.py

@@ -6,19 +6,13 @@
 from functools import partial
 
 import torch
-import random
 import ttnn
 from tests.sweep_framework.sweep_utils.utils import gen_shapes
 from tests.tt_eager.python_api_testing.sweep_tests.generation_funcs import gen_func_with_cast_tt
 
 from tests.ttnn.utils_for_testing import check_with_pcc, start_measuring_time, stop_measuring_time
 from models.utility_functions import torch_random
 
-# Override the default timeout in seconds for hang detection.
-TIMEOUT = 30
-
-random.seed(0)
-
 
 # Parameters provided to the test vector generator are defined here.
 # They are defined as dict-type suites that contain the arguments to the run function as keys, and lists of possible inputs as values.
@@ -56,8 +50,7 @@ def run(
     *,
     device,
 ) -> list:
-    data_seed = random.randint(0, 20000000)
-    torch.manual_seed(data_seed)
+    torch.manual_seed(0)
 
     torch_grad_tensor = gen_func_with_cast_tt(partial(torch_random, low=-10, high=10, dtype=torch.float32), grad_dtype)(
         input_shape

tests/sweep_framework/sweeps/eltwise/unary_backward/floor_bw/floor_bw.py

@@ -5,20 +5,13 @@
 from functools import partial
 
 import torch
-import random
 import ttnn
 from tests.sweep_framework.sweep_utils.utils import gen_shapes, sanitize_shape_rm
 from tests.tt_eager.python_api_testing.sweep_tests.generation_funcs import gen_func_with_cast_tt
 
 from tests.ttnn.utils_for_testing import check_with_pcc, start_measuring_time, stop_measuring_time
 from models.utility_functions import torch_random
 
-# Override the default timeout in seconds for hang detection.
-TIMEOUT = 30
-
-random.seed(0)
-
-
 # Parameters provided to the test vector generator are defined here.
 # They are defined as dict-type suites that contain the arguments to the run function as keys, and lists of possible inputs as values.
 # Each suite has a key name (in this case "suite_1" and "suite_2") which will associate the test vectors to this specific suite of inputs.
@@ -55,8 +48,7 @@ def run(
     *,
     device,
 ) -> list:
-    data_seed = random.randint(0, 20000000)
-    torch.manual_seed(data_seed)
+    torch.manual_seed(0)
 
     torch_grad_tensor = gen_func_with_cast_tt(
         partial(torch_random, low=-100, high=100, dtype=torch.float32), grad_dtype

tests/sweep_framework/sweeps/eltwise/unary_backward/frac_bw/frac_bw.py

@@ -6,7 +6,6 @@
 from functools import partial
 
 import torch
-import random
 import ttnn
 
 from tests.sweep_framework.sweep_utils.utils import gen_shapes, sanitize_shape_rm
@@ -15,11 +14,6 @@
 from tests.ttnn.utils_for_testing import check_with_pcc, start_measuring_time, stop_measuring_time
 from models.utility_functions import torch_random
 
-# Override the default timeout in seconds for hang detection.
-TIMEOUT = 30
-
-random.seed(0)
-
 
 # Parameters provided to the test vector generator are defined here.
 # They are defined as dict-type suites that contain the arguments to the run function as keys, and lists of possible inputs as values.
@@ -67,8 +61,7 @@ def run(
     *,
     device,
 ) -> list:
-    data_seed = random.randint(0, 20000000)
-    torch.manual_seed(data_seed)
+    torch.manual_seed(0)
 
     if input_layout == ttnn.ROW_MAJOR_LAYOUT:
         input_shape = sanitize_shape_rm(input_shape)

tests/sweep_framework/sweeps/eltwise/unary_backward/hardsigmoid_bw/hardsigmoid_bw.py

@@ -6,7 +6,6 @@
 from functools import partial
 
 import torch
-import random
 import ttnn
 
 from tests.sweep_framework.sweep_utils.utils import gen_shapes, sanitize_shape_rm
@@ -15,11 +14,6 @@
 from tests.ttnn.utils_for_testing import check_with_pcc, start_measuring_time, stop_measuring_time
 from models.utility_functions import torch_random
 
-# Override the default timeout in seconds for hang detection.
-TIMEOUT = 30
-
-random.seed(0)
-
 
 # Parameters provided to the test vector generator are defined here.
 # They are defined as dict-type suites that contain the arguments to the run function as keys, and lists of possible inputs as values.
@@ -70,8 +64,7 @@ def run(
     *,
     device,
 ) -> list:
-    data_seed = random.randint(0, 20000000)
-    torch.manual_seed(data_seed)
+    torch.manual_seed(0)
 
     if input_layout == ttnn.ROW_MAJOR_LAYOUT:
         input_shape = sanitize_shape_rm(input_shape)

tests/sweep_framework/sweeps/eltwise/unary_backward/hardswish_bw/hardswish_bw.py

@@ -5,19 +5,13 @@
 from functools import partial
 
 import torch
-import random
 import ttnn
 from tests.sweep_framework.sweep_utils.utils import gen_shapes, sanitize_shape_rm
 from tests.tt_eager.python_api_testing.sweep_tests.generation_funcs import gen_func_with_cast_tt
 
 from tests.ttnn.utils_for_testing import check_with_pcc, start_measuring_time, stop_measuring_time
 from models.utility_functions import torch_random
 
-# Override the default timeout in seconds for hang detection.
-TIMEOUT = 30
-
-random.seed(0)
-
 
 # Parameters provided to the test vector generator are defined here.
 # They are defined as dict-type suites that contain the arguments to the run function as keys, and lists of possible inputs as values.
@@ -55,8 +49,7 @@ def run(
     *,
     device,
 ) -> list:
-    data_seed = random.randint(0, 20000000)
-    torch.manual_seed(data_seed)
+    torch.manual_seed(0)
 
     torch_grad_tensor = gen_func_with_cast_tt(
         partial(torch_random, low=-100, high=100, dtype=torch.float32), grad_dtype

tests/sweep_framework/sweeps/eltwise/unary_backward/i0_bw/i0_bw.py

@@ -6,7 +6,6 @@
 from functools import partial
 
 import torch
-import random
 import ttnn
 
 from tests.sweep_framework.sweep_utils.utils import gen_shapes, sanitize_shape_rm
@@ -15,11 +14,6 @@
 from tests.ttnn.utils_for_testing import check_with_pcc, start_measuring_time, stop_measuring_time
 from models.utility_functions import torch_random
 
-# Override the default timeout in seconds for hang detection.
-TIMEOUT = 30
-
-random.seed(0)
-
 
 # Parameters provided to the test vector generator are defined here.
 # They are defined as dict-type suites that contain the arguments to the run function as keys, and lists of possible inputs as values.
@@ -69,8 +63,7 @@ def run(
     *,
     device,
 ) -> list:
-    data_seed = random.randint(0, 20000000)
-    torch.manual_seed(data_seed)
+    torch.manual_seed(0)
 
     if input_layout == ttnn.ROW_MAJOR_LAYOUT:
         input_shape = sanitize_shape_rm(input_shape)

tests/sweep_framework/sweeps/eltwise/unary_backward/lgamma_bw/lgamma_bw.py

@@ -6,7 +6,6 @@
 from functools import partial
 
 import torch
-import random
 import ttnn
 
 from tests.sweep_framework.sweep_utils.utils import gen_shapes, sanitize_shape_rm
@@ -15,11 +14,6 @@
 from tests.ttnn.utils_for_testing import check_with_pcc, start_measuring_time, stop_measuring_time
 from models.utility_functions import torch_random
 
-# Override the default timeout in seconds for hang detection.
-TIMEOUT = 30
-
-random.seed(0)
-
 
 # Parameters provided to the test vector generator are defined here.
 # They are defined as dict-type suites that contain the arguments to the run function as keys, and lists of possible inputs as values.
@@ -82,8 +76,7 @@ def run(
     *,
     device,
 ) -> list:
-    data_seed = random.randint(0, 20000000)
-    torch.manual_seed(data_seed)
+    torch.manual_seed(0)
 
     if input_layout == ttnn.ROW_MAJOR_LAYOUT:
         input_shape = sanitize_shape_rm(input_shape)

tests/sweep_framework/sweeps/eltwise/unary_backward/log10_bw/log10_bw.py

@@ -6,7 +6,6 @@
 from functools import partial
 
 import torch
-import random
 import ttnn
 
 from tests.sweep_framework.sweep_utils.utils import gen_shapes, sanitize_shape_rm
@@ -15,11 +14,6 @@
 from tests.ttnn.utils_for_testing import check_with_pcc, start_measuring_time, stop_measuring_time
 from models.utility_functions import torch_random
 
-# Override the default timeout in seconds for hang detection.
-TIMEOUT = 30
-
-random.seed(0)
-
 
 # Parameters provided to the test vector generator are defined here.
 # They are defined as dict-type suites that contain the arguments to the run function as keys, and lists of possible inputs as values.
@@ -79,8 +73,7 @@ def run(
     *,
     device,
 ) -> list:
-    data_seed = random.randint(0, 20000000)
-    torch.manual_seed(data_seed)
+    torch.manual_seed(0)
 
     if input_layout == ttnn.ROW_MAJOR_LAYOUT:
         input_shape = sanitize_shape_rm(input_shape)

tests/sweep_framework/sweeps/eltwise/unary_backward/log_bw/log_bw.py

@@ -6,7 +6,6 @@
 from functools import partial
 
 import torch
-import random
 import ttnn
 
 from tests.sweep_framework.sweep_utils.utils import gen_shapes, sanitize_shape_rm
@@ -15,11 +14,6 @@
 from tests.ttnn.utils_for_testing import check_with_pcc, start_measuring_time, stop_measuring_time
 from models.utility_functions import torch_random
 
-# Override the default timeout in seconds for hang detection.
-TIMEOUT = 30
-
-random.seed(0)
-
 
 # Parameters provided to the test vector generator are defined here.
 # They are defined as dict-type suites that contain the arguments to the run function as keys, and lists of possible inputs as values.
@@ -70,8 +64,7 @@ def run(
     *,
     device,
 ) -> list:
-    data_seed = random.randint(0, 20000000)
-    torch.manual_seed(data_seed)
+    torch.manual_seed(0)
 
     if input_layout == ttnn.ROW_MAJOR_LAYOUT:
         input_shape = sanitize_shape_rm(input_shape)

tests/sweep_framework/sweeps/eltwise/unary_backward/log_sigmoid_bw.py

@@ -6,19 +6,13 @@
 from functools import partial
 
 import torch
-import random
 import ttnn
 from tests.sweep_framework.sweep_utils.utils import gen_shapes
 from tests.tt_eager.python_api_testing.sweep_tests.generation_funcs import gen_func_with_cast_tt
 
 from tests.ttnn.utils_for_testing import check_with_pcc, start_measuring_time, stop_measuring_time
 from models.utility_functions import torch_random
 
-# Override the default timeout in seconds for hang detection.
-TIMEOUT = 30
-
-random.seed(0)
-
 
 # Parameters provided to the test vector generator are defined here.
 # They are defined as dict-type suites that contain the arguments to the run function as keys, and lists of possible inputs as values.
@@ -56,8 +50,7 @@ def run(
     *,
     device,
 ) -> list:
-    data_seed = random.randint(0, 20000000)
-    torch.manual_seed(data_seed)
+    torch.manual_seed(0)
 
     torch_grad_tensor = gen_func_with_cast_tt(partial(torch_random, low=-10, high=10, dtype=torch.float32), grad_dtype)(
         input_shape