Just like [scope](https://en.wikipedia.org/wiki/Scope_(computer_science)) in programming languages, `Scope` in the neural network can also be a local scope. There are two attributes about local scope.
1. We can create local variables in a local scope. When that local scope are destroyed, all local variables should also be destroyed.
2. Variables in a parent scope can be retrieved from local scopes of that parent scope, i.e., when user get a variable from a scope, it will try to search this variable in current scope. If there is no such variable in the local scope, `scope` will keep searching from its parent, until the variable is found or there is no parent.
In `Scope` class, there is a private data member called `parent_`. `parent_` is a smart pointer to its parent scope. When user `Get` a variable by its `name`, the `name` will be searched inside the current scope. If the variable cannot be found locally and parent scope is not a `nullptr`, the variable will be searched inside that parent scope. `parent_` pointer's default value is `nullptr`. It means that the scope is a global scope when `parent_` is nullptr.
A local scope is very useful when we implement Recurrent Neural Network. Each timestep of an RNN should be a `Net`. Each `Net` of timestep (`StepNet` for short) should use an independent local scope. Just like variables in a while loop is inside a local scope in programming languages. By using a single `StepNet` and changing local scope, we can implement an RNN easily.