Can features that are the same in every sample contribute to learning?

For simplicity, let’s say that I am monitoring 4 sensors for an ongoing metric.

The first column is the sensor ID and the second column is the sensor type.

[
  [
    [0, 0, 0.123],
    [1, 0, 0.456],
    [2, 1, 0.789],
    [3, 1, 0.555]
  ],
  [
    [0, 0, 0.987],
    [1, 0, 0.654],
    [2, 1, 0.321],
    [3, 1, 0.666]     
  ],
  [
    [0, 0, 0.591],
    [1, 0, 0.824],
    [2, 1, 0.760],
    [3, 1, 0.888]      
  ]
]

If the first two columns are always the same values, will a CNN or an LSTM be able to learn from these columns or are they just redundant?

In my mind, the sensor ID could correspond with a postion on the map where different metrics are observed. Or the sensor type could correspond with some sort of sensitivity in the metric. But am I just kidding myself if they are the same in every sample?

I don’t want to provide unnecessary dimensionality to the model.