Basic functions on tensors

import torch
import numpy
singleton = torch.tensor(8)
print(singleton.item())
#random_image_size_tensor = torch.rand(size=(224, 224, 3))
#print(random_image_size_tensor.shape)
#print(random_image_size_tensor.ndim)
# zero = torch.zeros(size=(5, 5))
# ones = torch.ones(size=(5, 5))
# similarZeros = torch.zeros_like(zero)
# toTen = torch.range(0, 10)
# print(toTen)
# toTen2 = torch.arange(start=0, end=100, step=3)
# print(toTen2)
tensor = torch.tensor([[1, 2, 3], [6, 7, 8]], dtype=torch.int32)
print(f"Shape of the tensor is: {tensor.shape}")
print(f"Dimension of the tensor is: {tensor.ndim}")
print(f"Datatype of the tensor is: {tensor.dtype}")
datatypecustom = torch.tensor([7, 8, 9], dtype=torch.float64)
print(f"Custom datatype set to float64: {datatypecustom.dtype}")
sample = torch.arange(start=0, end=10, step=2)
print(sample)
# sample = sample + 1
# sample = sample * sample
# Matrix Multiplication
#new = sample @ sample
#sample = torch.matmul(sample, sample)
#print(sample)
#for i in range(len(sample)):
#    sample = torch.mul(sample, i)
#Transpose Matrix
matrix = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print(matrix)
print(matrix.T)
matrix = torch.matmul(matrix, matrix.T)
print(matrix)
mat = torch.randn(3,3)
det = torch.linalg.det(mat)
print(det)
print(mat)