[DOCS][FIXES +++ ]

docs/zeta/nn/modules/simple_feedback.md

@@ -112,6 +112,3 @@ This particular sequence ensures that the neural network can learn a rich repres
 
 ---
 
-**Notes**:
-
-Remember to replace `"from zeta.nn.modules import SimpleFeedForward"` with the actual import statement depending on where the `SimpleFeedForward` function resides in your project structure. The above examples assume it's placed in a module named `your_module`.

docs/zeta/structs/encoderdecoder.md

@@ -99,7 +99,7 @@ This method executes the forward pass of the module.
 ```python
 # Imports
 import torch
-from _your_module_ import Encoder, Decoder, EncoderDecoder
+from zeta.structs import Encoder, Decoder, EncoderDecoder
 
 # Arguments
 args = argparse.Namespace(

docs/zeta/training/parallel_wrapper.md

@@ -56,7 +56,7 @@ This method redirects attribute access to the internal model to allow direct acc
 
 ```python
 import torch.nn as nn
-from zeta.training import ParallelWrapper  # assuming the class is in your_module.py
+from zeta.training import ParallelWrapper  
 
 # Define a model
 model = nn.Linear(512, 512)
@@ -74,7 +74,7 @@ output = model(input)
 
 ```python
 import torch.nn as nn
-from zeta.training import ParallelWrapper  # assuming the class is in your_module.py
+from zeta.training import ParallelWrapper  
 
 # Define a model
 model = nn.Linear(512, 512)
@@ -92,7 +92,7 @@ output = model(input)
 
 ```python
 import torch.nn as nn
-from zeta.training import ParallelWrapper  # assuming the class is in your_module.py
+from zeta.training import ParallelWrapper  
 
 # Define a model
 model = nn.Linear(512, 512)

tests/nn/modules/test_test_conv_lang.py

@@ -90,9 +90,3 @@ def test_invalid_activation_raises_error():
         )
 
 
-# 6. Test Coverage (requires pytest-cov)
-def test_coverage():
-    pytest.main(["--cov=your_module", "test_your_module.py"])
-
-
-# Add more tests as needed...

tests/nn/modules/test_test_h3_layer.py

@@ -54,10 +54,3 @@ def test_invalid_dimension_raises_error():
     with pytest.raises(ValueError):
         H3Layer(0)
 
-
-# 6. Test Coverage (requires pytest-cov)
-def test_coverage():
-    pytest.main(["--cov=your_module", "test_your_module.py"])
-
-
-# Add more tests as needed...

tests/ops/test_mos.py

@@ -3,7 +3,7 @@
 from torch import nn
 from zeta.ops.mos import (
     MixtureOfSoftmaxes,
-)  # Replace 'your_module' with your actual module
+)  
 
 
 # Create a fixture for initializing the model

tests/rl/test_prioritizedreplybuffer.py

@@ -2,7 +2,7 @@
 import torch
 from zeta.rl.priortized_replay_buffer import (
     PrioritizedReplayBuffer,
-)  # Replace 'your_module' with the actual module where classes are defined
+)  
 
 
 @pytest.fixture

tests/rl/test_prioritizedsequencereplybuffer.py

@@ -2,7 +2,7 @@
 import torch
 from zeta.rl.priortized_rps import (
     PrioritizedSequenceReplayBuffer,
-)  # Replace 'your_module' with the actual module where classes are defined
+)  
 
 
 @pytest.fixture

tests/rl/test_sumtree.py

@@ -1,7 +1,7 @@
 import pytest
 from zeta.rl.sumtree import (
     SumTree,
-)  # Replace 'your_module' with the actual module where SumTree is defined
+)  
 
 
 # Fixture for initializing SumTree instances with a given size

tests/training/test_parallel_wrapper.py

@@ -3,7 +3,7 @@
 import torch.nn as nn
 
 from zeta.training.parallel_wrapper import (
-    ParallelWrapper,  # assuming the class is in your_module.py
+    ParallelWrapper,  
 )
 
 

zeta/nn/modules/test_dense_connect.py

@@ -2,7 +2,7 @@
 import torch.nn as nn
 import unittest
 
-from your_module import DenseBlock
+from zeta.nn.modules.dense_connect import DenseBlock
 
 
 class DenseBlockTestCase(unittest.TestCase):