_interventions:tensor([[1., 0., 0., 0.],
        [0., 1., 0., 0.],
        [0., 0., 1., 0.],
        [0., 0., 0., 1.]])
dummy:tensor([0.])
scm._manifold_thickness:9.999999717180685e-10
scm.graph.adjacency_matrix:tensor([[False,  True, False, False],
        [False, False, False, False],
        [False, False, False,  True],
        [False, False, False, False]])
scm.structure_transforms.0.log_scale:tensor([0.])
scm.structure_transforms.0.param_net.weight:tensor([[0.7477, 0.9128, 0.9556, 0.7220]])
scm.structure_transforms.0.param_net.bias:tensor([0.])
scm.structure_transforms.1.log_scale:tensor([0.])
scm.structure_transforms.1.param_net.weight:tensor([[-0.9548,  1.0326, -0.9659,  0.5813]])
scm.structure_transforms.1.param_net.bias:tensor([0.])
scm.structure_transforms.2.log_scale:tensor([0.])
scm.structure_transforms.2.param_net.weight:tensor([[-1.2040, -0.8834, -0.8460,  0.9622]])
scm.structure_transforms.2.param_net.bias:tensor([0.])
scm.structure_transforms.3.log_scale:tensor([0.])
scm.structure_transforms.3.param_net.weight:tensor([[-1.0696,  1.3524,  0.5810,  1.2699]])
scm.structure_transforms.3.param_net.bias:tensor([0.])
scm.intervention_transforms.0.log_scale:tensor([0.])
scm.intervention_transforms.0.param_net.weight:tensor([[2.0893, 2.0598, 1.6464, 2.1822]])
scm.intervention_transforms.0.param_net.bias:tensor([0.])
scm.intervention_transforms.1.log_scale:tensor([0.])
scm.intervention_transforms.1.param_net.weight:tensor([[2.1687, 1.8260, 2.4826, 1.7912]])
scm.intervention_transforms.1.param_net.bias:tensor([0.])
scm.intervention_transforms.2.log_scale:tensor([0.])
scm.intervention_transforms.2.param_net.weight:tensor([[1.7585, 2.1272, 2.0602, 2.3028]])
scm.intervention_transforms.2.param_net.bias:tensor([0.])
scm.intervention_transforms.3.log_scale:tensor([0.])
scm.intervention_transforms.3.param_net.weight:tensor([[1.1277, 1.7970, 1.5157, 1.7658]])
scm.intervention_transforms.3.param_net.bias:tensor([0.])
intervention_prior._masks:tensor([[ True, False, False, False],
        [False,  True, False, False],
        [False, False,  True, False],
        [False, False, False,  True]])
intervention_prior._permutation:tensor([0, 1, 2, 3], dtype=torch.int32)
intervention_prior._inverse_permutation:tensor([0, 1, 2, 3], dtype=torch.int32)
encoder.coeffs:tensor([ 0.0609,  1.3255, -1.0692, -0.5364, -1.3446, -0.8330])
decoder.base_model.coeffs:tensor([ 0.0609,  1.3255, -1.0692, -0.5364, -1.3446, -0.8330])
