The input to nn.Conv2d is expected to have the shape [batch_size, channels, height, width], while you are trying to pass a tensor in the shape [10000, 4761] to the model.
You could reshape the input tensor via:
tranining_tensor = tranining_tensor.view(-1, 1, 69, 69)

before passing it to the model.
However, since your batch size seems to be 10000, you might run out of memory quickly and might want to use a Dataset and DataLoader as described in this tutorial 7.
Also, the in_features on self.fc1 seem to be wrong, since you are downsampling the activation with the pooling layers, so you might need to change it to self.fc1 = nn.Linear(64*17*17, 100).
You might also want to add an activation function between self.fc1 and self.fc2.