diff --git a/tests/attention_test.py b/tests/attention_test.py
index 3ce54e8251..c5e7c1bbab 100644
--- a/tests/attention_test.py
+++ b/tests/attention_test.py
@@ -23,27 +23,22 @@
 
 from absl.testing import parameterized
 from flax import nnx
-from flax.linen import partitioning as nn_partitioning
 import jax
 import jax.numpy as jnp
-from jax.sharding import AxisType, Mesh, NamedSharding, PartitionSpec as P
-from MaxText import max_utils
+from jax.sharding import AxisType, Mesh
 from MaxText import maxtext_utils
 from MaxText import pyconfig
 from MaxText.common_types import (
     AttentionType,
     DECODING_ACTIVE_SEQUENCE_INDICATOR,
-    EP_AS_CONTEXT,
     MODEL_MODE_AUTOREGRESSIVE,
     MODEL_MODE_PREFILL,
     MODEL_MODE_TRAIN,
-    ShardMode,
 )
 from MaxText.globals import MAXTEXT_PKG_DIR
 from MaxText.layers.attention_mla import MLA
 from MaxText.layers.attention_op import ChunkedCausalMask, _generate_chunk_attention_mask, _make_bidirectional_block_mask
 from MaxText.layers.attentions import Attention
-from MaxText.sharding import maybe_shard_with_name
 import numpy as np
 import pytest
 
@@ -693,15 +688,13 @@ def test_tpu_flash_attention_context_parallel(
     )
     nnx.update(attention_as_mha_flash_cp, generic_state)
 
-    mha_generic_flash_cp_output = (
-        attention_test_util.forward_with_context_expert_parallelism(
-            cfg_cp,
-            mesh_cp,
-            attention_as_mha_flash_cp,
-            lnx,
-            decoder_segment_ids,
-            decoder_positions,
-        )
+    mha_generic_flash_cp_output = attention_test_util.forward_with_context_expert_parallelism(
+        cfg_cp,
+        mesh_cp,
+        attention_as_mha_flash_cp,
+        lnx,
+        decoder_segment_ids,
+        decoder_positions,
     )
 
     # This removes all sharding information and makes them standard NumPy arrays.
@@ -1479,15 +1472,13 @@ def test_tpu_flash_attention_context_parallel(
         rngs=self.nnx_rng,
     )
     nnx.update(attention_as_mla_flash_cp, generic_state)
-    mla_generic_flash_cp_output = (
-        attention_test_util.forward_with_context_expert_parallelism(
-            cfg_cp,
-            mesh_cp,
-            attention_as_mla_flash_cp,
-            lnx,
-            decoder_segment_ids,
-            decoder_positions,
-        )
+    mla_generic_flash_cp_output = attention_test_util.forward_with_context_expert_parallelism(
+        cfg_cp,
+        mesh_cp,
+        attention_as_mla_flash_cp,
+        lnx,
+        decoder_segment_ids,
+        decoder_positions,
     )
 
     # This removes all sharding information and makes them standard NumPy arrays.
diff --git a/tests/integration_tests/train_tests.py b/tests/integration_tests/train_tests.py
index d29338e502..cc81a8e063 100644
--- a/tests/integration_tests/train_tests.py
+++ b/tests/integration_tests/train_tests.py
@@ -417,6 +417,7 @@ def test_gpu_synthetic_model_ag_once(self):
 
   @pytest.mark.integration_test
   @pytest.mark.gpu_only
+  @pytest.mark.scheduled_only
   def test_gpu_zero1_gradient_accumulation(self):
     os.environ["NVTE_FUSED_ATTN"] = "1"  # Enable fused attention
     zero1_ga = [  # tests Zero-1 optimizer sharding with gradient accumulation