fix: resolve test import issues and transformer indentation

Test fixes:
- test_dataloader_integration.py: Fix import path (tinytorch.data → tinytorch.core)
- integration_mnist_test.py: Fix Linear import (was aliased but used wrong name)
- test_module_05_dense.py: Fix Dense vs Linear usage (was using wrong variable name)

Milestone fix:
- 01_vaswani_attention.py: Fix indentation in train_epoch function

tinytorch/milestones/05_2017_transformer/01_vaswani_attention.py

@@ -376,7 +376,7 @@ def train_epoch(model, dataloader, optimizer, loss_fn):
         pred = np.argmax(logits.data, axis=-1)
         for i in range(batch_size):
             if np.array_equal(pred[i], target_batch.data[i]):
-            correct_sequences += 1
+                correct_sequences += 1
         total_samples += batch_size
 
     return total_loss / total_samples, (correct_sequences / total_samples) * 100

tinytorch/tests/integration/integration_mnist_test.py

@@ -21,7 +21,7 @@ sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
 import numpy as np
 from tinytorch.core.tensor import Tensor
-from tinytorch.core.layers import Linear as Dense
+from tinytorch.core.layers import Linear
 from tinytorch.core.activations import ReLU
 from tinytorch.core.training import CrossEntropyLoss
 

tinytorch/tests/integration/test_dataloader_integration.py

@@ -12,16 +12,9 @@ import os
 # Add project root to path
 sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', '..'))
 
-# Try to import from package, fall back to dev module if not exported yet
-try:
-    from tinytorch import Tensor
-    from tinytorch.data.loader import Dataset, TensorDataset, DataLoader
-except (ImportError, ModuleNotFoundError):
-    # Module not exported yet, use dev version
-    sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', '..', 'modules', 'source', '05_dataloader'))
-    from dataloader_dev import Dataset, TensorDataset, DataLoader
-    sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', '..', 'modules', 'source', '01_tensor'))
-    from tensor_dev import Tensor
+# Import from TinyTorch package
+from tinytorch import Tensor
+from tinytorch.core.dataloader import Dataset, TensorDataset, DataLoader
 
 
 def test_training_workflow_integration():

tinytorch/tests/integration/test_module_05_dense.py

@@ -24,7 +24,7 @@ class TestDenseModuleExports:
     def test_dense_is_callable(self):
         """Test Dense can be instantiated."""
         from tinytorch.core.layers import Linear as Dense
-        layer = Linear(10, 5)
+        layer = Dense(10, 5)
         assert layer is not None, "Should create Dense layer instance"
         assert hasattr(layer, 'forward'), "Dense should have forward method"
 
@@ -38,7 +38,7 @@ class TestDenseLayerFunctionality:
         from tinytorch.core.tensor import Tensor
 
         # Create layer
-        layer = Linear(10, 5)
+        layer = Dense(10, 5)
 
         # Create input
         batch_size = 32
@@ -56,7 +56,7 @@ class TestDenseLayerFunctionality:
         from tinytorch.core.layers import Linear as Dense
         from tinytorch.core.tensor import Tensor
 
-        layer = Linear(10, 5, bias=True)
+        layer = Dense(10, 5, bias=True)
         assert hasattr(layer, 'bias'), "Layer should have bias"
         assert layer.bias is not None, "Bias should be initialized"
 
@@ -69,7 +69,7 @@ class TestDenseLayerFunctionality:
         from tinytorch.core.layers import Linear as Dense
         from tinytorch.core.tensor import Tensor
 
-        layer = Linear(10, 5, bias=False)
+        layer = Dense(10, 5, bias=False)
         assert layer.bias is None, "Bias should be None when disabled"
 
         x = Tensor(np.random.randn(1, 10))
@@ -81,7 +81,7 @@ class TestDenseLayerFunctionality:
         from tinytorch.core.layers import Linear as Dense
         from tinytorch.core.tensor import Tensor
 
-        layer = Linear(10, 5)
+        layer = Dense(10, 5)
         x = Tensor(np.random.randn(4, 10))
 
         # Test both forward() and __call__()
@@ -114,9 +114,9 @@ class TestNetworkComposition:
         from tinytorch.core.tensor import Tensor
 
         # Build network manually (without Sequential)
-        layer1 = Linear(784, 128)
+        layer1 = Dense(784, 128)
         relu = ReLU()
-        layer2 = Linear(128, 10)
+        layer2 = Dense(128, 10)
         sigmoid = Sigmoid()
 
         # Test forward pass through all layers
@@ -146,9 +146,9 @@ class TestXORCapability:
         from tinytorch.core.tensor import Tensor
 
         # XOR network: 2 -> 4 -> 1
-        hidden = Linear(2, 4)
+        hidden = Dense(2, 4)
         relu = ReLU()
-        output = Linear(4, 1)
+        output = Dense(4, 1)
         sigmoid = Sigmoid()
 
         # XOR inputs