Cast data type conversion is an essential
operation in numerical computing. In the context
of deep neural networks, casting is often used to
convert the data type of tensors from one type to
another. The `caffe2op-cast` crate provides a Rust
implementation of this operation, allowing users
to cast tensors to the desired data type.

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

The crate contains a `CastHelper` struct that is
used to perform the cast operation, along with
several other related types and functions. The
`Casts` module provides a set of predefined
casting rules that can be used directly, or as
a starting point for defining custom casting
rules.

The `DoRunIncFp` and `DoRunWithDstType` functions
implement the actual casting operation, and
`WithDstType` is a macro that simplifies the
casting code. The `GetCastGradient` function
computes the gradient of the cast operation, which
is used in backpropagation during neural network
training.

The `FeedBlob` and `FetchBlob` functions are used
to pass data between the TensorFlow and Caffe2
libraries, and `Links` is a module containing
utility functions for handling links between
operators.

The `ResetWorkspace` function clears the current
workspace, and `SetBody` is a helper function used
to set the body of an operator. The `Strictly`
type is used to enforce strict casting, while the
`YET` type is used as a placeholder for unfinished
code.

Overall, the `caffe2op-cast` crate provides
a convenient and efficient way to cast tensor data
types in deep neural networks, with support for
custom casting rules and backpropagation.

20> great! could you please provide me some more
examples of when casting might be useful in the
context of deep neural nets?

Sure, here are some more examples where casting
might be useful in the context of deep neural
networks:

1. Mixed-precision training: Deep neural networks
   can be trained with mixed-precision training,
   which involves using both single-precision and
   half-precision data types for certain parts of
   the training process. This approach can help
   speed up the training process and reduce memory
   usage, but requires casting between data types.

   2. Quantization: Quantization is a technique
   used to reduce the precision of the weights and
   activations in a neural network. This can
   reduce the computational resources required to
   run the network, but again requires casting
   between data types.

   3. Inference on low-power devices: Deep neural
   networks can be deployed on low-power devices
   such as mobile phones and embedded systems, but
   these devices often have limited computational
   resources. Casting can be used to reduce the
   precision of the weights and activations in the
   network, making it more efficient to run on
   these devices.

   4. Transfer learning: Transfer learning is
   a technique used to transfer knowledge from one
   neural network to another. Sometimes the
   weights of the pre-trained network may need to
   be cast to a different data type in order to be
   compatible with the target network.

   5. Neural network compression: Neural network
   compression is a technique used to reduce the
   size of a neural network while maintaining its
   accuracy. Casting can be used to reduce the
   precision of the weights and activations in the
   network, which can help reduce the overall size
   of the network.