b5453ae0c6700e0d9e12de02876f6a07c58b45ec,kymatio/scattering1d/frontend/tensorflow_frontend.py,ScatteringTensorFlow1D,scattering,#ScatteringTensorFlow1D#Any#,23

Before Change

                          "out_type="list" for equivalent functionality.",
                          DeprecationWarning)

        batch_shape = tuple(x.shape[:-1])
        signal_shape = tuple(x.shape[-1:])

        x = tf.reshape(x, (-1, 1) + signal_shape)

        // get the arguments before calling the scattering
        // treat the arguments
        if self.vectorize:
            size_scattering = precompute_size_scattering(
                self.J, self.Q, max_order=self.max_order, detail=True)
        else:
            size_scattering = 0

        S = scattering1d(x, self.backend.pad, self.backend.unpad, self.backend, self.J, self.psi1_f, self.psi2_f,
                         self.phi_f, max_order=self.max_order, average=self.average, pad_left=self.pad_left,
                         pad_right=self.pad_right, ind_start=self.ind_start, ind_end=self.ind_end,
                         oversampling=self.oversampling,
                         vectorize=self.vectorize,
                         size_scattering=size_scattering,
                         out_type=self.out_type)

        if self.out_type == "array" and self.vectorize:
            scattering_shape = tuple(S.shape[-2:])
            new_shape = batch_shape + scattering_shape

            S = tf.reshape(S, new_shape)
        elif self.out_type == "array" and not self.vectorize:
            for k, v in S.items():

After Change

                          "out_type="list" for equivalent functionality.",
                          DeprecationWarning)

        batch_shape = tf.shape(x)[:-1]
        signal_shape = tf.shape(x)[-1:]

        x = tf.reshape(x, tf.concat(((-1, 1), signal_shape), 0))

        // get the arguments before calling the scattering
        // treat the arguments
        if self.vectorize:
            size_scattering = precompute_size_scattering(
                self.J, self.Q, max_order=self.max_order, detail=True)
        else:
            size_scattering = 0

        S = scattering1d(x, self.backend.pad, self.backend.unpad, self.backend, self.J, self.psi1_f, self.psi2_f,
                         self.phi_f, max_order=self.max_order, average=self.average, pad_left=self.pad_left,
                         pad_right=self.pad_right, ind_start=self.ind_start, ind_end=self.ind_end,
                         oversampling=self.oversampling,
                         vectorize=self.vectorize,
                         size_scattering=size_scattering,
                         out_type=self.out_type)

        if self.out_type == "array" and self.vectorize:
            scattering_shape = tf.shape(S)[-2:]
            new_shape = tf.concat((batch_shape, scattering_shape), 0)

            S = tf.reshape(S, new_shape)
        elif self.out_type == "array" and not self.vectorize:
            for k, v in S.items():

Instances