diff --git a/TrainingExtensions/torch/src/python/aimet_torch/elementwise_ops.py b/TrainingExtensions/torch/src/python/aimet_torch/elementwise_ops.py
index d5dc3f55b7b..16ddb8502a9 100644
--- a/TrainingExtensions/torch/src/python/aimet_torch/elementwise_ops.py
+++ b/TrainingExtensions/torch/src/python/aimet_torch/elementwise_ops.py
@@ -37,7 +37,7 @@
 
 """ Custom modules for functional operations defined under torch and torch.nn.functional packages """
 
-from typing import Callable, Any, Tuple, Union
+from typing import Callable, Any, Tuple, Union, List
 import itertools
 import torchvision
 import torch
@@ -278,6 +278,24 @@ def forward(data: torch.Tensor, indices: torch.Tensor, axis: int = 0) -> torch.T
         return torch.index_select(data, axis, indices.flatten()).reshape(target_shape)
 
 
+class DepthToSpaceCRDMode(torch.nn.Module):
+    """ Depthtospace op implementation in CRD mode """
+
+    def __init__(self, block_size: List):
+        super().__init__()
+        self.block_size_h = block_size[0]
+        self.block_size_w = block_size[1]
+
+    def forward(self, x: torch.Tensor) -> Any:
+        """
+        Forward-pass routine for DepthToSpace op in CRD mode
+        """
+        b, c, h, w = x.shape
+        tmp = torch.reshape(x, (b, c // (self.block_size_h * self.block_size_w), self.block_size_h, self.block_size_w, h, w))
+        tmp = torch.permute(tmp, (0, 1, 4, 2, 5, 3))
+        out = torch.reshape(tmp, (b, c // (self.block_size_h * self.block_size_w), h * self.block_size_h, w * self.block_size_w))
+        return out
+
 class DepthToSpaceDCRMode(torch.nn.Module):
     """ Depthtospace op implementation in DCR mode """