automl21/accel/neural_rec.py [145:176]:
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            rec_n_hidden=rec_n_hidden, rec_n_layers=rec_n_layers,
            init_hidden_depth=init_hidden_depth,
            init_hidden_n_hidden=init_hidden_n_hidden,
            init_act=init_act,
            init_hidden_weight_scale=init_hidden_weight_scale,
            enc_hidden_depth=enc_hidden_depth, enc_n_hidden=enc_n_hidden,
            enc_act=enc_act, enc_weight_scale=enc_weight_scale,
            dec_hidden_depth=dec_hidden_depth, dec_n_hidden=dec_n_hidden,
            dec_act=dec_act, dec_weight_scale=dec_weight_scale,
            output_delta_weight=output_delta_weight,
            learn_init_iterate=learn_init_iterate,
            learn_init_hidden=learn_init_hidden,
            learn_init_iterate_delta=learn_init_iterate_delta,
            center_iterates=center_iterates,
        )

    def init_instance(self, init_x, context):
        single = init_x.dim() == 1
        if single:
            init_x = init_x.unsqueeze(0)
            if context is not None:
                context = context.unsqueeze(0)

        assert init_x.dim() == 2
        if context is not None:
            assert context.dim() == 2
            assert init_x.size(0) == context.size(0)

        n_batch = init_x.size(0)

        h = torch.zeros(n_batch, self.rec_n_layers * self.rec_n_hidden,
                        dtype=init_x.dtype, device=init_x.device)
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automl21/accel/neural_rec.py [251:282]:
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            rec_n_hidden=rec_n_hidden, rec_n_layers=rec_n_layers,
            init_hidden_depth=init_hidden_depth,
            init_hidden_n_hidden=init_hidden_n_hidden,
            init_act=init_act,
            init_hidden_weight_scale=init_hidden_weight_scale,
            enc_hidden_depth=enc_hidden_depth, enc_n_hidden=enc_n_hidden,
            enc_act=enc_act, enc_weight_scale=enc_weight_scale,
            dec_hidden_depth=dec_hidden_depth, dec_n_hidden=dec_n_hidden,
            dec_act=dec_act, dec_weight_scale=dec_weight_scale,
            output_delta_weight=output_delta_weight,
            learn_init_iterate=learn_init_iterate,
            learn_init_hidden=learn_init_hidden,
            learn_init_iterate_delta=learn_init_iterate_delta,
            center_iterates=center_iterates,
        )

    def init_instance(self, init_x, context):
        single = init_x.dim() == 1
        if single:
            init_x = init_x.unsqueeze(0)
            if context is not None:
                context = context.unsqueeze(0)

        assert init_x.dim() == 2
        if context is not None:
            assert context.dim() == 2
            assert init_x.size(0) == context.size(0)

        n_batch = init_x.size(0)

        h = torch.zeros(n_batch, self.rec_n_layers * self.rec_n_hidden,
                        dtype=init_x.dtype, device=init_x.device)
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