Is dropout in pytorch a pseudo-random operation?
For example, ‘out’ is a tensor with [n,c,h,w]
out=self.lrelu(self.conv_1(out))        
out1 = F.dropout(out, 0.1, training=True)
out2 = F.dropout(out, 0.1, training=True)
out3 = F.dropout(out, 0.1, training=True)

print(out1)
print(out2)
print(out3)

When I run the above code many times. As I wish, out1 out2 out3 are not the same every time.
But out1 in the first time is the same as out1 in the second time. out2 and out3 have the same phenomenon.
Is this correct？
Thank you