Thanks for answering, Im afraid I still dont understand enough, the docs are not detailed enough for me.
I looked in the NN tutorial:
def __init__(self):
        super(Net, self).__init__()
        # 1 input image channel, 6 output channels, 3x3 square convolution
        # kernel
        self.conv1 = nn.Conv2d(1, 6, 3)
        self.conv2 = nn.Conv2d(6, 16, 3)
        # an affine operation: y = Wx + b
        self.fc1 = nn.Linear(16 * 6 * 6, 120)  # 6*6 from image dimension
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

Im still not sure what Conv2d does. From the docs:

Applies a 2D convolution over an input signal composed of several input planes.

Conv2d takes high dimension and put it in a 2D matrix? but why we want do it? and why the choosed the input to be (6, 16, 3) in the second line of  self.conv2 = nn.Conv2d(6, 16, 3). In addition what output channel means for Conv2d? Why we need to define here output channel while we already do it in the nn.Linear
In this code lines:
        self.fc1 = nn.Linear(16 * 6 * 6, 120)  # 6*6 from image dimension
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

Why they choose the hidden layer units to be with 120 and 84 units?
In the forward function:
def forward(self, x):
        # Max pooling over a (2, 2) window
        x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
        # If the size is a square you can only specify a single number
        x = F.max_pool2d(F.relu(self.conv2(x)), 2)
        x = x.view(-1, self.num_flat_features(x))
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

Im not sure why we need the max_pool2d function, from the docs:

Applies a 2D max pooling over an input signal composed of several input planes.

What is it max pooling and why do we need it?
Thanks.