diff --git a/torch_npu/contrib/__init__.py b/torch_npu/contrib/__init__.py
index 5bc4472adf03bf732c5d0fd9a9880a2ad90cfe74..6a531eb9136a48c9227ffcb52c0e2fef72db8582 100644
--- a/torch_npu/contrib/__init__.py
+++ b/torch_npu/contrib/__init__.py
@@ -5,7 +5,7 @@ from .function import npu_iou, npu_ptiou, npu_giou, npu_diou, npu_ciou, npu_mult
 from .module import ChannelShuffle, Prefetcher, LabelSmoothingCrossEntropy, ROIAlign, DCNv2, \
      ModulatedDeformConv, Mish, BiLSTM, PSROIPool, SiLU, Swish, NpuFairseqDropout, NpuCachedDropout, \
      MultiheadAttention, FusedColorJitter, NpuDropPath, Focus, FastBatchNorm1d, FastBatchNorm2d, \
-     FastBatchNorm3d, FastSyncBatchNorm, LinearA8W8Quant, LinearWeightQuant
+     FastBatchNorm3d, FastSyncBatchNorm, LinearA8W8Quant, LinearWeightQuant, QuantConv2d
 
 __all__ = [
     # from function
@@ -47,4 +47,5 @@ __all__ = [
     "LinearA8W8Quant",
     "FusedColorJitter",
     "LinearWeightQuant",
+    "QuantConv2d",
 ]
diff --git a/torch_npu/contrib/module/__init__.py b/torch_npu/contrib/module/__init__.py
index f9dd427f441bd19bd6488fa802a4b74fcffb0e66..c91e18cbe30c33ac8b0ea8c606a6b0731371c083 100644
--- a/torch_npu/contrib/module/__init__.py
+++ b/torch_npu/contrib/module/__init__.py
@@ -17,6 +17,7 @@ from .batchnorm_with_int32_count import FastBatchNorm1d, \
 from .linear_a8w8_quant import LinearA8W8Quant
 from .linear_weight_quant import LinearWeightQuant
 from .npu_modules import DropoutWithByteMask
+from .quant_conv2d import QuantConv2d
 
 __all__ = [
     "ChannelShuffle",
@@ -40,4 +41,5 @@ __all__ = [
     "LinearA8W8Quant",
     "LinearWeightQuant",
     "DropoutWithByteMask",
+    "QuantConv2d",
 ]
diff --git a/torch_npu/contrib/module/quant_conv2d.py b/torch_npu/contrib/module/quant_conv2d.py
index 1aa59bce434fea66bb4749a319e77ccbe673610b..70eba9e371e968492ec006bbf6bf19238c3785e4 100644
--- a/torch_npu/contrib/module/quant_conv2d.py
+++ b/torch_npu/contrib/module/quant_conv2d.py
@@ -59,21 +59,19 @@ class QuantConv2d(nn.Module):
         offset (Tensor): Requant calculation parameter of shape (out_channels)
 
     Examples::
-        >>> quant_conv2d_input = torch.randint(-1, 1, (1, 1, 4, 4), dtype=torch.int8)
-        >>> weight = torch.randint(-1, 1, (1, 1, 3, 3), dtype=torch.int8)
-        >>> scale = torch.randint(-1, 1, (1,), dtype=torch.int64)
-        >>> bias = torch.randint(-1, 1, (1,), dtype=torch.int32)
-        >>> model = QuantConv2d(in_channels, out_channels, k_size, output_dtype)
+        >>> quant_conv2d_input = torch.randint(-1, 1, (1, 1, 4, 4), dtype=torch.int8).npu()
+        >>> weight = torch.randint(-1, 1, (1, 1, 3, 3), dtype=torch.int8).npu()
+        >>> scale = torch.randint(-1, 1, (1,), dtype=torch.int64).npu()
+        >>> bias = torch.randint(-1, 1, (1,), dtype=torch.int32).npu()
+        >>> model = QuantConv2d(in_channels=1, out_channels=1, kernel_size=(3, 3), output_dtype=torch.float16).npu()
         >>> model = model.npu()
         >>> model.weight.data = weight
         >>> model.scale.data = scale
         >>> model.bias.data = bias
-        >>> config = CompilerConfig()
-        >>> npu_backend = tng.get_npu_backend(compiler_config=config)
-        >>> static_graph_model = torch.compile(model, backend=npu_backend, dynamic=False)
+        >>> static_graph_model = torch.compile(model, backend="npu", dynamic=False)
         >>> output = static_graph_model(quant_conv2d_input)
         >>> print(output.size())
-        torch.Size(1, 1, 2, 2)
+        torch.Size([1, 1, 2, 2])
     """
     in_channels: int
     out_channels: int