>>> import torch
>>> 
>>> # 定義一個 33x55 張量
>>> a = torch.randn(33, 55)
>>> a.size()
torch.Size([33, 55])
>>> 
>>> # 下面開始嘗試 repeat 函數(shù)在不同參數(shù)情況下的效果
>>> a.repeat(1,1).size()     # 原始值：torch.Size([33, 55])
torch.Size([33, 55])
>>> 
>>> a.repeat(2,1).size()     # 原始值：torch.Size([33, 55])
torch.Size([66, 55])
>>> 
>>> a.repeat(1,2).size()     # 原始值：torch.Size([33, 55])
torch.Size([33, 110])
>>>
>>> a.repeat(1,1,1).size()   # 原始值：torch.Size([33, 55])
torch.Size([1, 33, 55])
>>>
>>> a.repeat(2,1,1).size()   # 原始值：torch.Size([33, 55])
torch.Size([2, 33, 55])
>>>
>>> a.repeat(1,2,1).size()   # 原始值：torch.Size([33, 55])
torch.Size([1, 66, 55])
>>>
>>> a.repeat(1,1,2).size()   # 原始值：torch.Size([33, 55])
torch.Size([1, 33, 110])
>>>
>>> a.repeat(1,1,1,1).size() # 原始值：torch.Size([33, 55])
torch.Size([1, 1, 33, 55])
>>> 
>>> # ------------------ 割割 ------------------
>>> # repeat()的參數(shù)的個數(shù)，不能少于被操作的張量的維度的個數(shù)，
>>> # 下面是一些錯誤示例
>>> a.repeat(2).size()  # 1D < 2D, error
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
RuntimeError: Number of dimensions of repeat dims can not be smaller than number of dimensions of tensor
>>>
>>> # 定義一個3維的張量，然后展示前面提到的那個錯誤
>>> b = torch.randn(5,6,7)
>>> b.size() # 3D
torch.Size([5, 6, 7])
>>> 
>>> b.repeat(2).size() # 1D < 3D, error
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
RuntimeError: Number of dimensions of repeat dims can not be smaller than number of dimensions of tensor
>>>
>>> b.repeat(2,1).size() # 2D < 3D, error
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
RuntimeError: Number of dimensions of repeat dims can not be smaller than number of dimensions of tensor
>>>
>>> b.repeat(2,1,1).size() # 3D = 3D, okay
torch.Size([10, 6, 7])
>>>

import torch

x = torch.randn(1, 3, 224, 224)
y = x.repeat(3, 1, 1, 1)
print(x.shape)
print(y.shape)