I am trying to understand usage of nn.Identity , but unable to understand what pytorch document referring with following example nn.Identity 2
seed =4
m = nn.Identity()
input = torch.randn(4,4)
output = m(input)
print(output)
print(output.size())

What nn.Identity is doing here ?
tensor([[-0.2381, -0.0488,  1.2883, -2.0334],
        [-0.7297, -0.8721,  0.7086,  0.3899],
        [ 0.6550,  1.4832,  0.3744, -1.2825],
        [ 2.3327,  0.5004, -0.8785,  0.5100]])
torch.Size([4, 4])

Without identiy
seed =4
input = torch.randn(4,4)
output = input
print(output)
print(output.size())

tensor([[-0.0255, -0.5771, -0.5268, -0.6201],
        [ 1.0814,  0.4274,  0.6822,  0.2369],
        [ 0.0251, -1.1992, -0.9557,  0.9640],
        [-0.2789, -2.4326, -2.4736,  0.2777]])
torch.Size([4, 4])


seed =4
m = nn.Identity()
input = torch.randn(4,4)
output = m(input)
print(f'input {input}')
print(f'output {output}')


input tensor([[-1.3704, -0.4062, -0.5499,  1.8572],
        [ 0.3674,  0.4486, -0.3362, -1.4035],
        [ 0.1322,  0.2116, -0.1157,  1.0715],
        [ 0.8215,  0.1549,  0.8935, -0.1930]])
output tensor([[-1.3704, -0.4062, -0.5499,  1.8572],
        [ 0.3674,  0.4486, -0.3362, -1.4035],
        [ 0.1322,  0.2116, -0.1157,  1.0715],
        [ 0.8215,  0.1549,  0.8935, -0.1930]])