## caffe2op-do

The `caffe2op-do` crate defines an operator used
in DSP and machine learning computations that
executes a subnet in a separate workspace.

**Note: This crate is currently being translated from C++ to Rust, and some function bodies may still be in the process of translation.**

### Subnet Execution

The `DoOp` operator executes a subnet, which is
a collection of operators, in a separate
workspace. This means that any computations
performed by the subnet are isolated from the main
workspace, allowing for greater flexibility and
control over the computations being performed.

### Workspace Management

The `DoOp` operator utilizes a workspace stack to
manage the separate workspaces used for subnet
execution. When executing the subnet, a new
workspace is pushed onto the stack, and when the
subnet execution is complete, the workspace is
popped from the stack. This allows for efficient
management of the workspaces and ensures that
there are no conflicts between computations
performed in the main workspace and those
performed in the separate workspace.

### Reuse and Reusability

The `DoOp` operator also provides options for
reusing previously allocated workspace memory,
allowing for greater efficiency and reduced memory
usage. By utilizing workspace reuse, the `DoOp`
operator can reduce the amount of memory required
for subnet execution, improving performance and
reducing the overall memory footprint of the
computation.

### Example Usage

One example of the `DoOp` operator in use is in
the training of neural networks. During the
forward pass, the neural network computes a set of
outputs given a set of inputs. During the backward
pass, gradients are computed and used to adjust
the weights of the neural network. The `DoOp`
operator can be used to isolate the computations
performed during the forward pass, allowing for
efficient memory management and reducing the
memory footprint of the computation.

Overall, the `caffe2op-do` crate provides
a powerful operator for executing subnets in
separate workspaces, allowing for greater
flexibility, control, and efficiency in machine
learning and DSP computations.