Applies a 2D convolution over an input signal composed of several input planes.
In the simplest case, the output value of the layer with input size  and output  can be precisely described as:
where  is the valid 2D [cross-correlation](https://en.wikipedia.org/wiki/Cross-correlation) operator,  is a batch size,  denotes a number of channels,  is a height of input planes in pixels, and  is width in pixels.
这里的符号实际上是一个二维互相关（[cross-correlation](https://en.wikipedia.org/wiki/Cross-correlation)） 操作符（大家可以自己查一下互相关和真卷积的区别，互相关因为实现起来很简单，所以一般的深度学习框架都是用互相关操作取代真卷积）,  is a batch size,  代表通道的数量,  是输入的二维数据的像素高度， 是输入的二维数据的像素宽度。
*`stride` controls the stride for the cross-correlation, a single number or a tuple.
*`padding` controls the amount of implicit zero-paddings on both sides for `padding` number of points for each dimension.
*`dilation` controls the spacing between the kernel points; also known as the à trous algorithm. It is harder to describe, but this [link](https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md) has a nice visualization of what `dilation` does.
*`padding` 参数控制了要在二维卷积核的输入信号的上下左右各边填补的0的个数。
*`groups` controls the connections between inputs and outputs. `in_channels` and `out_channels` must both be divisible by `groups`. For example,
> * At groups=1, all inputs are convolved to all outputs.
> * At groups=2, the operation becomes equivalent to having two conv layers side by side, each seeing half the input channels, and producing half the output channels, and both subsequently concatenated.
> * At groups= `in_channels`, each input channel is convolved with its own set of filters, of size: .
