Assuming that each image tensor has the shape [channels, height, width], you could use expand or repeat to create a tensor with the shape [20, channels, height, width]:
x = torch.randn(3, 224, 224)

x = x.unsqueeze(0)
x1 = x.expand(20, -1, -1, -1)
print(x1.shape)
> torch.Size([20, 3, 224, 224])

# or
x2 = x.repeat(20, 1, 1, 1)
print(x2.shape)
> torch.Size([20, 3, 224, 224])

The former case would only manipulate the meta data (stride and shape), while the latter would trigger a copy.