import numpy as np

def fit(epochs, [model](https://docs.pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential "torch.nn.Sequential"), loss_func, [opt](https://docs.pytorch.org/docs/stable/generated/torch.optim.SGD.html#torch.optim.SGD "torch.optim.SGD"), train_dl, valid_dl):
    for epoch in range(epochs):
        [model.train](https://docs.pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.train "torch.nn.Module.train")()
        for [xb](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "torch.Tensor"), [yb](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "torch.Tensor") in train_dl:
            loss_batch([model](https://docs.pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential "torch.nn.Sequential"), loss_func, [xb](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "torch.Tensor"), [yb](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "torch.Tensor"), [opt](https://docs.pytorch.org/docs/stable/generated/torch.optim.SGD.html#torch.optim.SGD "torch.optim.SGD"))

        [model.eval](https://docs.pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.eval "torch.nn.Module.eval")()
        with [torch.no_grad](https://docs.pytorch.org/docs/stable/generated/torch.no_grad.html#torch.no_grad "torch.no_grad")():
            losses, nums = zip(
                *[loss_batch([model](https://docs.pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential "torch.nn.Sequential"), loss_func, [xb](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "torch.Tensor"), [yb](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "torch.Tensor")) for [xb](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "torch.Tensor"), [yb](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "torch.Tensor") in valid_dl]
            )
        val_loss = np.sum(np.multiply(losses, nums)) / np.sum(nums)

        print(epoch, val_loss)