diff --git a/onnxscript/function_libs/torch_lib/ops/core.py b/onnxscript/function_libs/torch_lib/ops/core.py
index 05e2cd9258..e74d68c386 100644
--- a/onnxscript/function_libs/torch_lib/ops/core.py
+++ b/onnxscript/function_libs/torch_lib/ops/core.py
@@ -3538,10 +3538,51 @@ def aten_feature_alpha_dropout(input: TensorType, p: float, train: bool) -> Tens
     raise NotImplementedError()
 
 
-def aten_feature_dropout(input: TensorType, p: float, train: bool) -> TensorType:
+@torch_op("aten::feature_dropout", trace_only=True)
+def aten_feature_dropout(input: TFloat, p: FLOAT, train: BOOL) -> TFloat:
     """feature_dropout(Tensor input, float p, bool train) -> Tensor"""
 
-    raise NotImplementedError()
+    # Feature dropout applies dropout to entire feature maps/channels
+    # rather than individual elements
+    
+    if p == 0 or not train:
+        return input
+    
+    # Get input dimensions
+    ndim = op.Size(op.Shape(input))
+    
+    # Create mask shape for feature dropout
+    # For 2D tensors [N, C]: mask shape is [N, C]
+    # For higher dim tensors [N, C, ...]: mask shape is [N, C, 1, 1, ...]
+    batch_size = op.Shape(input, start=0, end=1)
+    channel_size = op.Shape(input, start=1, end=2)
+    
+    # Create the appropriate mask shape based on tensor dimensions
+    is_2d = op.Equal(ndim, 2)
+    
+    # For 2D case, mask_shape = [N, C]
+    mask_shape_2d = op.Concat(batch_size, channel_size, axis=0)
+    
+    # For higher dimensions, mask_shape = [N, C, 1, 1, ...]
+    spatial_dims_count = op.Sub(ndim, 2)
+    ones_for_spatial = op.ConstantOfShape(
+        op.Reshape(spatial_dims_count, [1]), 
+        value=1
+    )
+    mask_shape_nd = op.Concat(batch_size, channel_size, ones_for_spatial, axis=0)
+    
+    # Select appropriate mask shape
+    mask_shape = op.Where(is_2d, mask_shape_2d, mask_shape_nd)
+    
+    # Create a dummy tensor of ones with the mask shape and apply dropout to it
+    # This leverages op.Dropout to handle training mode, scaling, and random generation
+    dummy_tensor = op.ConstantOfShape(mask_shape, value=1.0)
+    mask, _ = op.Dropout(dummy_tensor, p, train)
+    
+    # Apply mask to input (broadcasting will handle different shapes)
+    result = op.Mul(input, mask)
+    
+    return result
 
 
 @torch_op(("aten::fill.Tensor", "aten::fill.Scalar"))