[fix bug] load atomic_*.npy for tf tensor model (#4538)

Fix bug mentioned in
#4536

<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->

## Summary by CodeRabbit

- **Bug Fixes**
- Updated atomic property and weight label naming conventions across the
machine learning training and loss components to ensure consistent
terminology.
- Corrected placeholder key references in the training process to match
updated label names.

<!-- end of auto-generated comment: release notes by coderabbit.ai -->

deepmd/tf/loss/tensor.py

@@ -155,7 +155,7 @@ def label_requirement(self) -> list[DataRequirementItem]:
         # data required
         data_requirements.append(
             DataRequirementItem(
-                "atom_" + self.label_name,
+                "atomic_" + self.label_name,
                 self.tensor_size,
                 atomic=True,
                 must=False,
@@ -176,7 +176,7 @@ def label_requirement(self) -> list[DataRequirementItem]:
         if self.enable_atomic_weight:
             data_requirements.append(
                 DataRequirementItem(
-                    "atom_weight",
+                    "atomic_weight",
                     1,
                     atomic=True,
                     must=False,

deepmd/tf/train/trainer.py

@@ -281,6 +281,13 @@ def _build_network(self, data, suffix="") -> None:
             tf.int32, [None], name="t_mesh"
         )
         self.place_holders["is_training"] = tf.placeholder(tf.bool)
+        # update "atomic_" in self.place_holders.keys() with "atom_"
+        for kk in list(self.place_holders.keys()):
+            if "atomic_" in kk:
+                self.place_holders[kk.replace("atomic_", "atom_")] = (
+                    self.place_holders.pop(kk)
+                )
+
         self.model_pred = self.model.build(
             self.place_holders["coord"],
             self.place_holders["type"],