Finding several means in a large tensor
I have a large tensor (~10k). Here's a sampler with 200 values:
sample_tensor = tensor([ 0.6676,  0.0917,  0.6083,  0.4536,  1.1882,  0.6672,  0.6058, -0.1615,
         0.5254,  1.1642,  0.1994, -0.2274,  0.0511,  0.3707,  0.3675, -0.1629,
        -0.0638, -0.0118,  0.2668,  0.8586,  0.7027,  0.3018, -0.2930,  1.2613,
         0.9374,  0.3154,  1.0396, -0.0263,  0.2012,  1.5710, -0.4640, -0.1657,
        -0.2670,  0.5783,  0.7420,  0.1886, -1.1255,  0.3682,  0.2597,  0.3697,
         0.1404, -0.0289,  0.5903,  0.0461,  0.2288, -0.0414,  0.9736,  0.4891,
        -0.0593,  0.1694,  0.2426, -0.0339,  0.1683,  0.2374,  0.1349,  0.1672,
         0.4174,  0.8038,  1.4121, -0.1046,  0.1169,  0.6447, -0.1168,  0.7392,
         0.0578, -0.1398,  0.8974,  1.0977,  0.7102,  1.4012,  0.8541,  0.3314,
        -0.2045,  0.1540,  0.2779, -0.3912,  0.4068, -0.1868,  0.1796,  0.0318,
         0.1354, -0.9689,  0.3460,  0.3762,  0.8637, -0.4735,  0.8413,  0.5261,
         0.8362, -0.2226, -0.2772, -0.2757,  0.2079,  0.0895,  0.4352,  0.8868,
         0.3707,  0.8412,  0.3026,  0.1568,  0.4442,  0.0789,  0.5050,  0.0102,
         0.6944,  0.1852,  0.5215, -0.7028, -0.7591,  0.2139,  0.7411,  0.3830,
         0.8048, -0.7532,  0.7710,  0.8526,  1.1322,  0.0939, -0.3318,  1.1003,
         0.3066,  1.6501,  1.1300,  0.0062,  0.2600,  0.2605, -0.2236,  0.2516,
         0.4460,  0.6813,  0.1876, -0.4710, -0.5939,  0.4144,  0.0783,  0.4282,
         0.1744,  0.0569,  0.1043,  0.3329,  0.3561,  0.1618, -0.1184,  0.4183,
         0.5722, -0.4459,  0.3354,  0.3373,  0.2290,  1.0164, -0.5191,  0.0992,
         0.9188, -0.3634,  1.2128,  0.0457,  0.1028, -0.2206,  0.9355,  0.6074,
         0.3834,  0.0802,  0.7016,  0.8777,  0.2769, -0.7512,  0.8667, -0.1056,
         0.5435,  1.4568, -0.3943,  0.5740,  0.6328,  0.4063, -0.7712,  0.5113,
         0.1578,  0.4571,  1.0314,  0.2863, -0.1470,  1.0763, -0.0019,  0.9103,
         1.0114, -0.1229, -0.3118,  0.5383,  0.5566,  0.2280,  0.9320,  0.6770,
         0.0908,  0.5056,  0.0445, -0.0810,  0.2611,  0.1223, -0.0108,  0.0611])

I also have an input value that corresponds to how many means I need to get from this tensor:
sampler_number_of_means = 10

What is an efficient way to get a tensor of 10 means from this tensor, where each mean is a different  set of values with size of len(sample_tensor)/sampler_number_of_means. That is, in this example the first mean will be the first 20 values, the second mean the next 20 values, etc.
I'm currently iterating through the tensor and breaking it into equal size lists, then iterating through each list to get the mean. But it's quite slow with large tensors.

You can reshape the tensor, then take the mean.
import torch
sample_tensor = torch.tensor([ 0.6676,  0.0917,  0.6083,  0.4536,  1.1882,  0.6672,  0.6058, -0.1615,
         0.5254,  1.1642,  0.1994, -0.2274,  0.0511,  0.3707,  0.3675, -0.1629,
        -0.0638, -0.0118,  0.2668,  0.8586,  0.7027,  0.3018, -0.2930,  1.2613,
         0.9374,  0.3154,  1.0396, -0.0263,  0.2012,  1.5710, -0.4640, -0.1657,
        -0.2670,  0.5783,  0.7420,  0.1886, -1.1255,  0.3682,  0.2597,  0.3697,
         0.1404, -0.0289,  0.5903,  0.0461,  0.2288, -0.0414,  0.9736,  0.4891,
        -0.0593,  0.1694,  0.2426, -0.0339,  0.1683,  0.2374,  0.1349,  0.1672,
         0.4174,  0.8038,  1.4121, -0.1046,  0.1169,  0.6447, -0.1168,  0.7392,
         0.0578, -0.1398,  0.8974,  1.0977,  0.7102,  1.4012,  0.8541,  0.3314,
        -0.2045,  0.1540,  0.2779, -0.3912,  0.4068, -0.1868,  0.1796,  0.0318,
         0.1354, -0.9689,  0.3460,  0.3762,  0.8637, -0.4735,  0.8413,  0.5261,
         0.8362, -0.2226, -0.2772, -0.2757,  0.2079,  0.0895,  0.4352,  0.8868,
         0.3707,  0.8412,  0.3026,  0.1568,  0.4442,  0.0789,  0.5050,  0.0102,
         0.6944,  0.1852,  0.5215, -0.7028, -0.7591,  0.2139,  0.7411,  0.3830,
         0.8048, -0.7532,  0.7710,  0.8526,  1.1322,  0.0939, -0.3318,  1.1003,
         0.3066,  1.6501,  1.1300,  0.0062,  0.2600,  0.2605, -0.2236,  0.2516,
         0.4460,  0.6813,  0.1876, -0.4710, -0.5939,  0.4144,  0.0783,  0.4282,
         0.1744,  0.0569,  0.1043,  0.3329,  0.3561,  0.1618, -0.1184,  0.4183,
         0.5722, -0.4459,  0.3354,  0.3373,  0.2290,  1.0164, -0.5191,  0.0992,
         0.9188, -0.3634,  1.2128,  0.0457,  0.1028, -0.2206,  0.9355,  0.6074,
         0.3834,  0.0802,  0.7016,  0.8777,  0.2769, -0.7512,  0.8667, -0.1056,
         0.5435,  1.4568, -0.3943,  0.5740,  0.6328,  0.4063, -0.7712,  0.5113,
         0.1578,  0.4571,  1.0314,  0.2863, -0.1470,  1.0763, -0.0019,  0.9103,
         1.0114, -0.1229, -0.3118,  0.5383,  0.5566,  0.2280,  0.9320,  0.6770,
         0.0908,  0.5056,  0.0445, -0.0810,  0.2611,  0.1223, -0.0108,  0.0611])


sampler_number_of_means = 10
sample_tensor.reshape((sampler_number_of_means,int(sample_tensor.shape[0]/sampler_number_of_means))).mean(1)

Output
tensor([0.3729, 0.3248, 0.2977, 0.3431, 0.2499, 0.2993, 0.2740, 0.2841, 0.3611,
        0.3170])

