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+IfOp should have only one branch. An IfOp operator takes a `cond` variable whose value must be a vector of N boolean elements. Its return value has M (M<=N) instances, each corresponds to a true element in `cond`.
+
+```python
+import paddle as pd
+
+x = var()
+y = var()
+cond = var()
+
+b = pd.create_ifop(inputs=[x], output_num=1)
+with b.true_block():
+    x = b.inputs(0)
+    z = operator.add(x, y)
+    b.set_output(0, operator.softmax(z))
+
+out = b(cond)
+```
+
+If we want the output still has N instances, we can use IfElseOp with a default value, whose minibatch size must be N:
+
+```python
+import paddle as pd
+
+x = var()
+y = var()
+cond = var()
+default_value = var()
+b = pd.create_ifelseop(inputs=[x], output_num=1)
+with b.true_block():
+    x = b.inputs(0)
+    z = operator.add(x, y)
+    b.set_output(0, operator.softmax(z))
+
+with b.false_block():
+    x = b.inputs(0)
+    z = layer.fc(x)
+    b.set_output(0, operator.softmax(z))
+
+out = b(cond)
+```
+
+If only true_block is set in an IfElseOp, we can have a default value for false as:
+```python
+import paddle as pd
+
+x = var()
+y = var()
+cond = var()
+default_value = var()
+b = pd.create_ifelseop(inputs=[x], output_num=1, default_value)
+
+with b.true_block():
+    x = b.inputs(0)
+    z = operator.add(x, y)
+    b.set_output(0, operator.softmax(z))
+
+out = b(cond)
+```
+where default_value is a list of vars for `cond` == False.