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Here I will discuss about 5 different functions of torch with examples as a part of Assignment of pytorch tutorial by freecodecamp and jovian. The 5 different fuctions discussed here are:

  1. torch.eye(n, m=None, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False)
  2. torch.complex(real, imag, *, out=None)
  3. torch.reshape(input, shape)
  4. torch.rand(*size, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False)
  5. torch.narrow(input, dim, start, length)
# Uncomment and run the appropriate command for your operating system, if required

# Linux / Binder
# !pip install numpy torch==1.7.0+cpu torchvision==0.8.1+cpu torchaudio==0.7.0 -f https://download.pytorch.org/whl/torch_stable.html

# Windows
!pip install numpy torch==1.7.0+cpu torchvision==0.8.1+cpu torchaudio==0.7.0 -f https://download.pytorch.org/whl/torch_stable.html

# MacOS
# !pip install numpy torch torchvision torchaudio
Looking in links: https://download.pytorch.org/whl/torch_stable.html Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (1.18.5) Requirement already satisfied: torch==1.7.0+cpu in /usr/local/lib/python3.6/dist-packages (1.7.0+cpu) Requirement already satisfied: torchvision==0.8.1+cpu in /usr/local/lib/python3.6/dist-packages (0.8.1+cpu) Requirement already satisfied: torchaudio==0.7.0 in /usr/local/lib/python3.6/dist-packages (0.7.0) Requirement already satisfied: dataclasses in /usr/local/lib/python3.6/dist-packages (from torch==1.7.0+cpu) (0.8) Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from torch==1.7.0+cpu) (0.16.0) Requirement already satisfied: typing-extensions in /usr/local/lib/python3.6/dist-packages (from torch==1.7.0+cpu) (3.7.4.3) Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.6/dist-packages (from torchvision==0.8.1+cpu) (7.0.0) 
# Import torch and other required modules
import torch

Function 1: torch.eye():

Returns a 2-D tensor with ones on the diagonal and zeros elsewhere.

#Example 1- Working
torch.eye(3)
tensor([[1., 0., 0.],
        [0., 1., 0.],
        [0., 0., 1.]])