One approach is very similar to selecting elements from a Numpy array. You check the tensor for elements greater than a given value. It returns a tensor of True/False values. Then you use the tensor of True/False to find the elements in the original tensor which are greater than the given value.
>>> import torch
>>> a=torch.randn(6,4)
>>> a
tensor([[-0.0457, -0.4924, -0.7026, 0.0567],
[-0.5104, -0.1395, -0.3003, 0.8491],
[ 2.2846, 0.5619, -0.1806, 0.9625],
[ 0.7884, 1.1767, 2.0025, -0.0589],
[-0.1579, 0.8199, -0.5279, 0.2966],
[ 0.0946, -0.7405, 0.4907, 1.3673]])
>>> a>1
tensor([[False, False, False, False],
[False, False, False, False],
[ True, False, False, False],
[False, True, True, False],
[False, False, False, False],
[False, False, False, True]])
>>> a[a>1]
tensor([2.2846, 1.1767, 2.0025, 1.3673])
>>> torch.masked_select(a, a>1)
tensor([2.2846, 1.1767, 2.0025, 1.3673])
>>> torch.masked_select(a, torch.ge(a,1))
tensor([2.2846, 1.1767, 2.0025, 1.3673])