docs(imperative): fix multiple target warning when cross-reference

GitOrigin-RevId: c5b4884c8ab98e5986ce8e9006c841df9149fb6f

imperative/python/megengine/functional/nn.py

@@ -367,7 +367,7 @@ def conv_transpose2d(
 ) -> Tensor:
     r"""2D transposed convolution operation.
 
-    Refer to :class:`~.ConvTranspose2d` for more information.
+    Refer to :class:`~.module.conv.ConvTranspose2d` for more information.
 
     Args:
         inp: feature map of the convolution operation.
@@ -1519,7 +1519,7 @@ def dropout(inp: Tensor, drop_prob: float, training: bool = True) -> Tensor:
         inp: input tensor.
         drop_prob: probability to drop (set to zero) a single element.
         training: the default behavior of ``dropout`` during training is to rescale the output,
-            then it can be replaced by an :class:`~.Identity` during inference. Default: True
+            then it can be replaced by an :class:`~.module.identify.Identity` during inference. Default: True
     Returns:
         the ouput tensor
 
@@ -1669,7 +1669,7 @@ def sliding_window(
 ) -> Tensor:
     r"""Extracts sliding local blocks from a batched input tensor.
 
-    Refer to :class:`~.SlidingWindow` for more information.
+    Refer to :class:`~.module.sliding_window.SlidingWindow` for more information.
 
     Args:
         inp: input tensor.
@@ -1707,7 +1707,7 @@ def sliding_window_transpose(
 ) -> Tensor:
     r"""Sum over the sliding windows on the corresponding input location.
 
-    Refer to :class:`~.SlidingWindowTranspose` for more information.
+    Refer to :class:`~.module.sliding_window.SlidingWindowTranspose` for more information.
 
     Args:
         inp: input tensor.

imperative/python/megengine/module/dropout.py

@@ -14,7 +14,7 @@ class Dropout(Module):
     r"""Randomly sets input elements to zeros with the probability :math:`drop\_prob` during training.
     Commonly used in large networks to prevent overfitting.
     Note that we perform dropout only during training, we also rescale(multiply) the output tensor
-    by :math:`\frac{1}{1 - drop\_prob}`. During inference :class:`~.Dropout` is equal to :class:`~.Identity`.
+    by :math:`\frac{1}{1 - drop\_prob}`. During inference :class:`~.Dropout` is equal to :class:`~.module.identity.Identity`.
 
     Args:
         drop_prob: The probability to drop (set to zero) each single element

imperative/python/megengine/module/qat/concat.py

@@ -14,7 +14,7 @@ from .module import QATModule
 
 class Concat(Float.Concat, QATModule):
     r"""A :class:`~.QATModule` to do functional :func:`~.concat` with QAT support.
-    Could be applied with :class:`~.Observer` and :class:`~.FakeQuantize`.
+    Could be applied with :class:`~.Observer` and :class:`~.quantization.fake_quant.FakeQuantize`.
     """
 
     def forward(self, inps: Iterable[Tensor], axis: int = 0):

imperative/python/megengine/module/qat/conv.py

@@ -12,7 +12,7 @@ from .module import QATModule
 
 class Conv2d(Float.Conv2d, QATModule):
     r"""A :class:`~.QATModule` :class:`~.module.Conv2d` with QAT support.
-    Could be applied with :class:`~.Observer` and :class:`~.FakeQuantize`.
+    Could be applied with :class:`~.Observer` and :class:`~.quantization.fake_quant.FakeQuantize`.
     """
 
     def calc_conv_qat(self, inp):
@@ -50,7 +50,7 @@ class Conv2d(Float.Conv2d, QATModule):
 
 class ConvRelu2d(Conv2d):
     r"""A :class:`~.QATModule` include :class:`~.module.Conv2d` and :func:`~.relu` with QAT support.
-    Could be applied with :class:`~.Observer` and :class:`~.FakeQuantize`.
+    Could be applied with :class:`~.Observer` and :class:`~.quantization.fake_quant.FakeQuantize`.
     """
 
     def forward(self, inp):
@@ -59,7 +59,7 @@ class ConvRelu2d(Conv2d):
 
 class ConvTranspose2d(Float.ConvTranspose2d, QATModule):
     r"""A :class:`~.QATModule` :class:`~.module.ConvTranspose2d` with QAT support.
-    Could be applied with :class:`~.Observer` and :class:`~.FakeQuantize`.
+    Could be applied with :class:`~.Observer` and :class:`~.quantization.fake_quant.FakeQuantize`.
     """
 
     def calc_conv_transpose2d_qat(self, inp):

imperative/python/megengine/module/qat/conv_bn.py

@@ -157,7 +157,7 @@ class _ConvBnActivation2d(Float._ConvBnActivation2d, QATModule):
 
 class ConvBn2d(_ConvBnActivation2d):
     r"""A fused :class:`~.QATModule` including :class:`~.module.Conv2d` and :class:`~.module.BatchNorm2d` with QAT support.
-    Could be applied with :class:`~.Observer` and :class:`~.FakeQuantize`.
+    Could be applied with :class:`~.Observer` and :class:`~.quantization.fake_quant.FakeQuantize`.
     """
 
     def forward(self, inp):
@@ -166,7 +166,7 @@ class ConvBn2d(_ConvBnActivation2d):
 
 class ConvBnRelu2d(_ConvBnActivation2d):
     r"""A fused :class:`~.QATModule` including :class:`~.module.Conv2d`, :class:`~.module.BatchNorm2d` and :func:`~.relu` with QAT support.
-    Could be applied with :class:`~.Observer` and :class:`~.FakeQuantize`.
+    Could be applied with :class:`~.Observer` and :class:`~.quantization.fake_quant.FakeQuantize`.
     """
 
     def forward(self, inp):

imperative/python/megengine/module/qat/elemwise.py

@@ -11,7 +11,7 @@ from .module import QATModule
 
 class Elemwise(Float.Elemwise, QATModule):
     r"""A :class:`~.QATModule` to do :mod:`~.functional.elemwise` operator with QAT support.
-    Could be applied with :class:`~.Observer` and :class:`~.FakeQuantize`.
+    Could be applied with :class:`~.Observer` and :class:`~.quantization.fake_quant.FakeQuantize`.
     """
 
     with_weight = False

imperative/python/megengine/module/qat/linear.py

@@ -11,7 +11,7 @@ from .module import QATModule
 
 class Linear(Float.Linear, QATModule):
     r"""A :class:`~.QATModule` version of :class:`~.module.Linear`.
-    Could be applied with :class:`~.Observer` and :class:`~.FakeQuantize`.
+    Could be applied with :class:`~.Observer` and :class:`~.quantization.fake_quant.FakeQuantize`.
 
     Args:
         in_features: size of each input sample.

imperative/python/megengine/quantization/qconfig.py

@@ -34,7 +34,7 @@ class QConfig(
         weight_observer: interface to instantiate an :class:`~.Observer` indicating
             how to collect scales and zero_point of wegiht.
         act_observer: similar to ``weight_observer`` but toward activation.
-        weight_fake_quant: interface to instantiate a :class:`~.FakeQuantize` indicating
+        weight_fake_quant: interface to instantiate a :class:`~.quantization.fake_quant.FakeQuantize` indicating
             how to do fake_quant calculation.
         act_observer: similar to ``weight_fake_quant`` but toward activation.
     

imperative/python/megengine/utils/network.py

@@ -532,13 +532,13 @@ def set_symbolic_shape(option: bool):
 
 
 def as_varnode(obj):
-    r"""convert a :class:`.VarNode` compatible object to :class:`.VarNode`.
+    r"""convert a :class:`.utils.network_node.VarNode` compatible object to :class:`.utils.network_node.VarNode`.
 
     Args:
         obj: it must be one of the following:
 
-            1. a :class:`.VarNode` object
-            2. a :class:`.OpNode` object that has unique output
+            1. a :class:`.utils.network_node.VarNode` object
+            2. a :class:`.utils.network_node.OpNode` object that has unique output
             3. an iterable that produces either type 1 or 2, with length 1
 
     """
@@ -568,8 +568,8 @@ def as_varnode(obj):
 
 
 def as_oprnode(obj):
-    r"""convert a :class:`.OpNode` compatible object to
-    :class:`.OpNode`; it works like :func:`as_varnode`.i
+    r"""convert a :class:`.utils.network_node.OpNode` compatible object to
+    :class:`.utils.network_node.OpNode`; it works like :func:`as_varnode`.
     """
     if type(obj) is VarNode:
         return obj.owner