complex_shift_autoencoder.py [216:248]:
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    def plot_x1_reconstructions(
        self, indices=[10, 2092, 10299, 13290], train_set=False, save_name=None
    ):
        """Plots x1 autoencoder reconstruction from z1.

            Args:
                pairs (datasets.Pairs): contains x1, x2, and params.
                model (function): callable f(x1) = x1_reconstruction
                indices (list of ints): indices for samples to plot
                train_set (bool): if true title is plotted with train otherwise test.
                save_name (str): indicates path where images should be saved. 
            """
        pairs = self.data.X_train if train_set else self.data.X_test
        plot.plot_x1_reconstructions(
            pairs, self.reconstruct_x1, indices, train_set, save_name
        )

    def plot_x2_reconstructions(
        self, indices=[10, 2092, 10299, 13290], train_set=False, save_name=None
    ):
        """Plots x1, x2 and x2 autoencoder reconstruction from z1 rotated.

        Args:
            pairs (datasets.Pairs): contains x1, x2, and params.
            model (function): callable f(x1) = x1_reconstruction
            indices (list of ints): indices for samples to plot
            train_set (bool): if true title is plotted with train otherwise test.
            save_name (str): indicates path where images should be saved. 
        """
        pairs = self.data.X_train if train_set else self.data.X_test
        plot.plot_x2_reconstructions(
            pairs, self.reconstruct_x2, indices, train_set, save_name
        )
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weakly_complex_shift_autoencoder.py [227:259]:
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    def plot_x1_reconstructions(
        self, indices=[10, 2092, 10299, 13290], train_set=False, save_name=None
    ):
        """Plots x1 autoencoder reconstruction from z1.

            Args:
                pairs (datasets.Pairs): contains x1, x2, and params.
                model (function): callable f(x1) = x1_reconstruction
                indices (list of ints): indices for samples to plot
                train_set (bool): if true title is plotted with train otherwise test.
                save_name (str): indicates path where images should be saved. 
            """
        pairs = self.data.X_train if train_set else self.data.X_test
        plot.plot_x1_reconstructions(
            pairs, self.reconstruct_x1, indices, train_set, save_name
        )

    def plot_x2_reconstructions(
        self, indices=[10, 2092, 10299, 13290], train_set=False, save_name=None
    ):
        """Plots x1, x2 and x2 autoencoder reconstruction from z1 rotated.

        Args:
            pairs (datasets.Pairs): contains x1, x2, and params.
            model (function): callable f(x1) = x1_reconstruction
            indices (list of ints): indices for samples to plot
            train_set (bool): if true title is plotted with train otherwise test.
            save_name (str): indicates path where images should be saved. 
        """
        pairs = self.data.X_train if train_set else self.data.X_test
        plot.plot_x2_reconstructions(
            pairs, self.reconstruct_x2, indices, train_set, save_name
        )
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