ignite/engine/__init__.py [91:105]:
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    if gradient_accumulation_steps <= 0:
        raise ValueError(
            "Gradient_accumulation_steps must be strictly positive. "
            "No gradient accumulation if the value set to one (default)."
        )

    def update(engine: Engine, batch: Sequence[torch.Tensor]) -> Union[Any, Tuple[torch.Tensor]]:
        model.train()
        x, y = prepare_batch(batch, device=device, non_blocking=non_blocking)
        y_pred = model(x)
        loss = loss_fn(y_pred, y)
        if gradient_accumulation_steps > 1:
            loss = loss / gradient_accumulation_steps
        loss.backward()
        if engine.state.iteration % gradient_accumulation_steps == 0:
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ignite/engine/__init__.py [327:341]:
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    if gradient_accumulation_steps <= 0:
        raise ValueError(
            "Gradient_accumulation_steps must be strictly positive. "
            "No gradient accumulation if the value set to one (default)."
        )

    def update(engine: Engine, batch: Sequence[torch.Tensor]) -> Union[Any, Tuple[torch.Tensor]]:
        model.train()
        x, y = prepare_batch(batch, device=device, non_blocking=non_blocking)
        y_pred = model(x)
        loss = loss_fn(y_pred, y)
        if gradient_accumulation_steps > 1:
            loss = loss / gradient_accumulation_steps
        loss.backward()
        if engine.state.iteration % gradient_accumulation_steps == 0:
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