You can simply concatenate multiple examples together with torch.cat, like in this small example:
    x = torch.rand(size=(1, 3000), dtype=torch.float32)
    y = torch.rand(size=(1, 3000), dtype=torch.float32)
    z = torch.cat((x, y), dim=0)

I do not know how you load your data, but if you are not using PyTorch’s torch.utils.data.Dataset and torch.utils.data.DataLoader classes, you could as it does the concatenation of multiple examples into a batched tensor for you (see tutorial 13).
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