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 N instances. If cond[i] == True, input instance input[i] will go through true_block() and generate output[i]; otherwise it will produce output from false_bloack().
# The `IfElse` Operator
```python
PaddlePaddle's `IfElse` operator differs from TensorFlow's:
importpaddleaspd
x=var()
- the TensorFlow version takes a scalar boolean value as the condition so that the whole mini-batch goes to either the true or the false branch, whereas
y=var()
- the PaddlePaddle version takes a vector of boolean value as the condition, and instances corresponding to true values go to the true branch, those corresponding to false values go to the false branch.
cond=var()
default_value=var()
## Example
b=pd.create_ifelseop(inputs=[x],output_num=1)
withb.true_block():
The following PaddlePaddle program shows the usage of the IfElse operator:
x=b.inputs(0)
z=operator.add(x,y)
b.set_output(0,operator.softmax(z))
withb.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, a special case is that we can have a default value for false as:
A challenge to implement the `IfElse` operator is to infer those variables to be split, or, say, to identify the variable of the mini-batch or those derived from the mini-batch.
An equivalent C++ program is as follows:
```c++
namespacepd=paddle;
intx=10;
inty=1;
intz=10;
boolcond=false;
into1,o2;
if(cond){
intd=x+y;
o1=z;
o2=pd::layer::softmax(z);
}else{
intd=pd::layer::fc(z);
o1=d;
o2=d+1;
}
```
```
where default_value is a list of vars for `cond` == False.