torch.randn（1，3，8，9）

torch.randn（1，3，8，9）

input = torch.randn(1, 3, 8, 9)
print(input)

输出：1这个张量，其中是3个通道，8行，9列的数据（从外面往里面数）

tensor([[[[-1.3645,  1.3841,  0.9907,  0.3150, -0.2379, -0.3170, -0.0550,
            1.1550, -0.3213],
          [-0.1501,  1.7308,  1.8265, -1.2117, -0.4732,  0.9322,  0.0305,
            4.1326,  0.2020],
          [ 0.5548,  0.2435,  0.2562,  0.6831,  0.3219, -0.7511,  0.7655,
           -0.2759,  0.5383],
          [-0.2694, -0.0915, -0.5576,  0.4837,  0.3901, -1.2744, -0.4583,
            0.0563, -0.9379],
          [-0.7810, -1.4409,  0.5581,  2.0812, -0.7531,  0.6109, -0.3434,
            0.5256,  0.7634],
          [ 0.8005,  1.4034, -1.1069,  0.2152, -1.4043, -0.5372,  1.7089,
           -0.6352,  1.1726],
          [-0.2337,  1.5157, -0.9249, -0.7676, -0.5558,  0.5163,  0.7303,
            0.2412,  1.3088],
          [ 0.9068,  0.6180, -0.0107, -0.8221, -0.0535, -0.2342, -0.2962,
           -0.7616, -0.3691]],

         [[-1.0224, -0.5053,  0.6644, -1.1911,  0.1454, -0.9308, -0.4813,
            0.6776, -1.1368],
          [ 0.3080, -1.3522, -1.6679, -0.8663,  0.6496,  1.6452,  0.3038,
           -1.7191,  1.7693],
          [ 1.1772, -0.4583, -0.3896, -1.5648, -0.4779, -0.5874, -0.9368,
           -1.0590, -0.7477],
          [-0.1314,  1.0419, -0.2534, -0.3769,  0.0934,  1.1365, -0.5016,
            0.9439, -0.1448],
          [ 1.0333, -1.9576, -0.8608,  1.2146,  0.9373, -0.4464,  1.0232,
           -0.3030,  0.5389],
          [-0.5502, -1.3761, -1.7243, -0.4608, -0.3664, -0.1555,  0.0889,
            1.5637, -2.3157],
          [ 0.0188,  1.1967,  0.6780, -0.3910,  0.0085,  1.9097,  0.5758,
            1.4236, -0.5228],
          [-0.4159, -1.1071, -0.0392,  0.0440, -1.9346, -0.6395, -1.8250,
           -0.6693,  0.1131]],

         [[-0.1653,  1.9642,  0.0252,  0.2953, -0.6793, -0.4985, -0.8535,
            2.4142, -0.2819],
          [ 0.6813,  1.8921, -0.5795,  0.9541, -0.4565,  0.3152,  0.4868,
           -0.1828,  0.3407],
          [ 1.5413, -1.0101, -0.2164, -0.1154,  0.6204,  1.3020,  0.8447,
            0.1371,  0.5912],
          [-1.6656, -0.5710, -0.1446,  0.1783, -1.4103, -2.0123, -0.0323,
           -1.9840, -0.4228],
          [-0.6837, -0.4137, -0.5354,  0.2577,  0.5738, -0.2938,  0.6559,
           -0.6664, -0.7363],
          [ 0.9420,  0.7781, -1.2198, -0.8467, -1.0400, -0.3809, -0.8529,
           -0.1051,  1.1124],
          [-0.2055,  1.2309, -0.3316, -0.9748,  0.0092,  1.5463, -0.7017,
           -0.3630,  0.9377],
          [ 1.4363,  0.3101, -0.2334, -0.3299,  1.7171,  0.2468, -1.0415,
            0.5179, -0.2107]]]])