S.No 
Name 
Description 
1. 
Torch 
The torch package includes data structure for multidimensional tensors and mathematical operation over these are defined. 
2. 
torch.Tensor 
This package is a multidimensional matrix which contains an element of a single data type. 
3. 
Tensor Attributes 

a) torch.dtype 
It is an object which represents the datatype of thetorch.Tensor. 
b) torch.device 
It is an object that represents the device on which torch.Tensor will be allocated. 
c) torch.layout 
It is an object which represents a memory layout of a toch.Tensor. 
4. 
Type Info 
The numerical properties of a torch.dtype will be accessed through either the torch.iinfo or the torch.finfo. 
1) torch.finfo 
It is an object which represents the numerical properties of a floatingpoint torch.dtype. 
2) torch.iinfo 
It is an object which represents the numerical properties of an integer torch.dtype. 
5. 
torch.sparse 
Torch supports sparse tensors in COO (rdinate) format, which will efficiently store and process tensors for which the majority of elements are zero. 
6. 
torch.cuda 
Torch supports for CUDA tensor types which implement the same function as CPU tensors, but for computation they utilize GPUs. 
7. 
torch.Storage 
A torch.Storage is a contiguous, onedimensional array of a single data type. 
8. 
torch.nn 
This package provides us many more classes and modules to implement and train the neural network. 
9. 
torch.nn.functional 
This package has functional classes which are similar to torch.nn. 
10. 
torch.optim 
This package is used to implement various optimization algorithm. 
11. 
torch.autogard 
This package provides classes and functions to implement automatic differentiation of arbitrary scalar value functions. 
12. 
torch.distributed 
This package supports three backends and each one is with different capabilities. 
13. 
torch.distribution 
This package allows us to construct the stochastic computation graphs, and stochastic gradient estimators for optimization 
14. 
torch.hub 
It is a pretrained model repository which is designed to facilitate research reproducibility. 
15. 
torch.multiprocessing 
It is a wrapper around the native multiprocessing module. 
16. 
torch.utils.bottleneck 
It is a tool which can be used as an initial step for debugging bottlenecks in our program. 
17. 
torch.utils.checkpoint 
It is used to create checkpoint in our source program. 
18. 
torch.tils.cpp_extension 
It is used to create the extension of C++, CUDA, and other languages. 
19. 
torch.utils.data 
This package is mainly used for creating the dataset. 
20. 
torch.utils.dlpack 
It will use to decode the Dlpack into tensor. 
21. 
torch.onnx 
The ONNX exporter is a tracebased exporter, which means that it operates by executing your model once and exporting the operators which were actually run during this run 