Thank You for the reply. I’ve used your code snippets.
from torch.utils.data import TensorDataset, DataLoader
t = torch.arange(100)
ds = TensorDataset(t)

# Setting Seed explicitly in dataloader
dl = DataLoader(ds, batch_size=16, shuffle=True, generator=torch.manual_seed(0))

print("first run through dl")
for i, yy in enumerate(dl):
    if i < 3:
        print(yy[0][:5])

print("second run through dl")
for i, yy in enumerate(dl):
    if i < 3:
        print(yy[0][:5])

first run through dl
tensor([33, 70, 17, 63, 71])
tensor([90, 64, 11, 30, 91])
tensor([43, 31, 92, 94, 19])
second run through dl
tensor([15,  9, 50, 34, 51])
tensor([82, 70, 73, 13, 57])
tensor([89, 23, 36, 55, 84])

# Setting Seed "globally"
torch.manual_seed(0)
dl = DataLoader(ds, batch_size=16, shuffle=True)
print("first run through dl")
for i, yy in enumerate(dl):
    if i < 3:
        print(yy[0][:5])

print("second run through dl")
for i, yy in enumerate(dl):
    if i < 3:
        print(yy[0][:5])

first run through dl
tensor([63, 70, 43, 75, 77])
tensor([78,  6, 23, 66, 44])
tensor([31, 84, 24, 73, 54])
second run through dl
tensor([71, 54, 40, 70, 80])
tensor([29, 90, 96, 56, 89])
tensor([92, 53, 41, 60, 78])

Both results are reproducible.If I understand correctly, the results happen to be different because the samples drawn, although use the same seed, are different instances and these instances are reproducible.
Edit:
Played around a bit. That explains it. Thank You.
torch.manual_seed(0)
x = torch.randn(5)
print(x)
print(torch.randn(5))
print(torch.randn(5))

# Reproducible
tensor([ 1.5410, -0.2934, -2.1788,  0.5684, -1.0845])
tensor([-1.3986,  0.4033,  0.8380, -0.7193, -0.4033])
tensor([-0.5966,  0.1820, -0.8567,  1.1006, -1.0712])

torch.manual_seed(0)
x = torch.randn(5)
print(x)

torch.manual_seed(0)
print(torch.randn(5))

torch.manual_seed(0)
print(torch.randn(5))

# Reproducible
tensor([ 1.5410, -0.2934, -2.1788,  0.5684, -1.0845])
tensor([ 1.5410, -0.2934, -2.1788,  0.5684, -1.0845])
tensor([ 1.5410, -0.2934, -2.1788,  0.5684, -1.0845])