Initializing convolutions in that effect with out_channels=1 would mean that random values would be set up per in_channel row. In other words, we would see that different randomized filters are being convolved with different input channels.
Like Filter A is used in channel A and Filter B is used in channel B and all of them are summed up. On the other hand, having out_channels as 1 means that we are using a single filter for all channels and are adding them up channel-wise.
If i use:
out_channels=1
in_channels=2
height=3
width=3
print(torch.rand([out_channels,in_channels,height,width]))

This gives me
tensor([[[[0.8728, 0.5557, 0.4790],
          [0.8028, 0.7029, 0.3034],
          [0.6854, 0.5710, 0.0458]],

         [[0.7219, 0.7814, 0.6355],
          [0.2254, 0.4197, 0.9144],
          [0.7522, 0.8480, 0.2152]]]])

As you see two filters are generated and they will be used in the two channels. But as out_channels=1 we should have a single filter which is convolved with the two channels and the outputs of those convolutions are added to create a single filter.
This however seems to give the required filter:
print(torch.rand([out_channels,height,width]).unsqueeze(1).repeat(1, in_channels, 1,1))

The output:
tensor([[[[0.4394, 0.8586, 0.6954],
          [0.2975, 0.1665, 0.0886],
          [0.5199, 0.8727, 0.7965]],

         [[0.4394, 0.8586, 0.6954],
          [0.2975, 0.1665, 0.0886],
          [0.5199, 0.8727, 0.7965]]]])