Pytorch Convolutions are no different from Tensorflow Convolutions. It is just a notation difference
Consider an image Ci x H x W, Where Ci is referred as input channels in the image, H and W are input spatial dimensions(width and height).
Lets say you want to convolve this image with a kernel of size K x K (lets keep stride =1  and padding = 0) to produce Co (output channels) Feature maps.
Thus the size of Convolution Kernel will be Co x Ci x K x K.
The operation produces Co x Ho x Wo, where Ho = ( H - K + 1), Wo = ( W - K + 1)
Co refers the number of feature maps (output channels) and Ho,Wo refer to the output spatial dimension, calculated using the same formula.
The choices we have are Co, K, stride, padding, dilation while defining a Conv Layer. The remaining information is either calculated(Wo, Ho) or fixed in the input (Ci, H, W)