Type the data/model/training batch contracts with NamedTuples (issues.md Issue 7)

build_features (transforms.py) now returns StepFeatures and
StreamingStepsDataset (dataset.py) now yields StepBatch, both NamedTuples
with the same field order as the tuples they replace, so ty can catch a
dropped/added field at every consuming call site instead of a silent
positional-tuple mismatch. Converted the unreadable throwaway-heavy unpacks
in cli.py, pipeline.py, validate.py, and dataset.py to named attribute
access; gave the WGAN path's derived 5-element batch its own
_Stage2RealFakeBatch NamedTuple; updated the two test batch-construction
helpers to build real StepBatchs.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

tests/test_validate.py

@@ -2,6 +2,7 @@ import numpy as np
 import torch
 
 from giant.constants import COND_DIM, SEC_SLOT_DIM, X_DIM
+from giant.data.dataset import StepBatch
 from giant.model.network import Stage1Model, Stage2OneShot, stage2_trunk_sec_dim
 from giant.validate import validate_marginals
 
@@ -46,8 +47,7 @@ def _tiny_models(particle_type_cfg: dict | None = None):
 
 
 def _loader(B: int = 4, n_batches: int = 2, n_sec_value: int = 0, n_classes: int = 8):
-    """A val_loader matching StreamingStepsDataset's 7-tuple batch shape:
-    (cond_cont, cond_cat, target_s1, n_sec, sec_cont, proc_idx, sec_type_idx)."""
+    """A val_loader matching StreamingStepsDataset's StepBatch shape."""
     batches = []
     for _ in range(n_batches):
         cond_cont = torch.randn(B, COND_DIM)
@@ -57,7 +57,7 @@ def _loader(B: int = 4, n_batches: int = 2, n_sec_value: int = 0, n_classes: int
         sec_cont = torch.randn(B, _K_MAX, SEC_SLOT_DIM)
         proc_idx = torch.zeros(B, dtype=torch.long)
         sec_type_idx = torch.randint(0, n_classes, (B, _K_MAX), dtype=torch.long)
-        batches.append((cond_cont, cond_cat, x1, n_sec, sec_cont, proc_idx, sec_type_idx))
+        batches.append(StepBatch(cond_cont, cond_cat, x1, n_sec, sec_cont, proc_idx, sec_type_idx))
     return batches