Ivy is a deep learning framework that uses a concept called "containers" to represent arrays and other data structures in a unified way. A container is essentially a wrapper around a NumPy array or a PyTorch tensor, which adds additional functionality and properties to the underlying data.

Ivy containers can hold multi-dimensional arrays, scalars, dictionaries, and other data structures, and they can be manipulated and transformed using a variety of built-in methods and operations. Some of the key features of Ivy containers include:

Data-agnostic interface: The same set of container methods can be used to manipulate arrays of different types, such as NumPy arrays or PyTorch tensors. This makes it easy to write code that works with arrays from multiple frameworks without having to worry about compatibility issues.

Transparent device management: Ivy containers can be moved between CPU and GPU devices seamlessly, without the need for explicit device management code. This makes it easy to write code that can take advantage of GPU acceleration without worrying about the details of device management.

Automatic differentiation: Ivy containers support automatic differentiation, which makes it easy to train deep learning models using gradient-based optimization methods.

Efficient memory management: Ivy containers are designed to be memory-efficient, and they use lazy evaluation and other techniques to minimize memory usage during computation.

Overall, Ivy containers provide a high-level interface for working with multi-dimensional arrays and other data structures in a flexible and efficient way, making it easier to write complex deep learning models and algorithms.