Set the dataset format used by `test_trainer` to float32 (#28920)

Co-authored-by: unit_test <test@unit.com>

tests/trainer/test_trainer.py

@@ -176,8 +176,8 @@ class DynamicShapesDataset:
         np.random.seed(seed)
         sizes = np.random.randint(1, 20, (length // batch_size,))
         # For easy batching, we make every batch_size consecutive samples the same size.
-        self.xs = [np.random.normal(size=(s,)) for s in sizes.repeat(batch_size)]
-        self.ys = [np.random.normal(size=(s,)) for s in sizes.repeat(batch_size)]
+        self.xs = [np.random.normal(size=(s,)).astype(np.float32) for s in sizes.repeat(batch_size)]
+        self.ys = [np.random.normal(size=(s,)).astype(np.float32) for s in sizes.repeat(batch_size)]
 
     def __len__(self):
         return self.length
@@ -547,7 +547,7 @@ class TrainerIntegrationPrerunTest(TestCasePlus, TrainerIntegrationCommon):
 
         np.random.seed(42)
         x = np.random.normal(size=(64,)).astype(np.float32)
-        y = 2.0 * x + 3.0 + np.random.normal(scale=0.1, size=(64,))
+        y = 2.0 * x + 3.0 + np.random.normal(scale=0.1, size=(64,)).astype(np.float32)
         train_dataset = datasets.Dataset.from_dict({"input_x": x, "label": y})
 
         # Base training. Should have the same results as test_reproducible_training