PyTorch 中的正方形

import torch# 一维张量my_tensor = torch.tensor([-3, 1, -2, 3, 5, -5, 0, -4])result = torch.square(input=my_tensor)  # 使用 torch.square() 函数print(result)  # tensor([9, 1, 4, 9, 25, 25, 0, 16])print(my_tensor.square()) # 直接调用张量对象的square()方法# 二维张量my_tensor = torch.tensor([[-3, 1, -2, 3], [5, -5, 0, -4]])result = torch.square(input=my_tensor)print(result)# tensor([[9, 1, 4, 9],#         [25, 25, 0, 16]])# 三维张量my_tensor = torch.tensor([[[-3., 1.], [-2., 3.]], [[5., -5.], [0., -4.]]])result = torch.square(input=my_tensor)print(result)# tensor([[[9., 1.], [4., 9.]],#         [[25., 25.], [0., 16.]]])# 复数张量my_tensor = torch.tensor([[[-3.+0.j, 1.+0.j], [-2.+0.j, 3.+0.j]], [[5.+0.j, -5.+0.j], [0.+0.j, -4.+0.j]]])result = torch.square(input=my_tensor)print(result)# tensor([[[9.-0.j, 1.+0.j], [4.-0.j, 9.+0.j]],#         [[25.+0.j, 25.-0.j], [0.+0.j, 16.-0.j]]])# 布尔张量my_tensor = torch.tensor([[[True, False], [True, False]], [[False, True], [False, True]]])result = torch.square(input=my_tensor)print(result)# tensor([[[1, 0], [1, 0]],#         [[0, 1], [0, 1]]])# 使用out参数out_tensor = torch.zeros_like(my_tensor)torch.square(input=my_tensor, out=out_tensor)print(out_tensor)