diff --git a/torch_xla/distributed/spmd/test_debugging.py b/torch_xla/distributed/spmd/test_debugging.py
deleted file mode 100644
index 9d04bd7ea62f..000000000000
--- a/torch_xla/distributed/spmd/test_debugging.py
+++ /dev/null
@@ -1,67 +0,0 @@
-import torch
-import torch_xla
-from torch_xla.distributed.spmd.debugging import visualize_tensor_sharding
-
-import numpy as np
-import torch
-import torch_xla.core.xla_model as xm
-import torch_xla.runtime as xr
-import torch_xla.experimental.xla_sharding as xs
-from torch_xla.experimental.xla_sharded_tensor import XLAShardedTensor
-from torch_xla.experimental.xla_sharding import Mesh
-
-
-def test_single_host_tiled():
-  xr.use_spmd()
-  num_devices = xr.global_runtime_device_count()
-  mesh_shape = (2, num_devices // 2)
-  device_ids = np.array(range(num_devices))
-  mesh = Mesh(device_ids, mesh_shape, ('x', 'y'))
-  t = torch.randn(8, 4).to(xm.xla_device())
-  partition_spec = (0, 1)
-  m1_sharded = xs.mark_sharding(t, mesh, partition_spec)
-  sharding = torch_xla._XLAC._get_xla_sharding_spec(t)
-  print("sharding is:")
-  print(sharding)
-  print("then print:")
-  visualize_tensor_sharding(t)
-
-
-def test_single_host_partial_replication():
-  xr.use_spmd()
-  num_devices = xr.global_runtime_device_count()
-  mesh_shape = (2, num_devices // 2)
-  device_ids = np.array(range(num_devices))
-  mesh = Mesh(device_ids, mesh_shape, ('x', 'y'))
-
-  partition_spec = (0, None)
-  t = torch.randn(8, 32).to(xm.xla_device())
-  xs.mark_sharding(t, mesh, (0, None))
-  sharding = torch_xla._XLAC._get_xla_sharding_spec(t)
-  print("sharding is: ")
-  print(sharding)
-  print("then print: ")
-  visualize_tensor_sharding(t)
-
-
-def test_single_host_replicated():
-  xr.use_spmd()
-  num_devices = xr.global_runtime_device_count()
-  mesh_shape = (2, num_devices // 2)
-  device_ids = np.array(range(num_devices))
-  mesh = Mesh(device_ids, mesh_shape, ('x', 'y'))
-
-  partition_spec_replicated = (None, None)
-  t = torch.randn(8, 32).to(xm.xla_device())
-  # xs.mark_sharding(t, mesh, (0, None))
-  sharded = xs.mark_sharding(t, mesh, partition_spec_replicated)
-  sharding = torch_xla._XLAC._get_xla_sharding_spec(t)
-  print("sharding is: ")
-  print(sharding)
-  print("then print: ")
-  visualize_tensor_sharding(t)
-
-
-test_single_host_tiled()
-test_single_host_partial_replication()
-test_single_host_replicated()