_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, False,  True,  True],
        [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([[ 0.7607,  1.8722, -1.3915,  0.7189]])
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([[ 0.7827,  1.2007, -1.0437,  0.9642]])
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([[-1.1985, -0.4966, -0.8636,  1.4692]])
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([[-1.0056, -1.0556,  1.3376, -1.3920]])
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([[1.4576, 1.5466, 1.8949, 2.4834]])
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([[2.0293, 1.4672, 1.9171, 1.7192]])
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([[1.9496, 1.7487, 1.9462, 1.7080]])
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([[2.3954, 2.2449, 2.2282, 2.0082]])
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([ 1.5722,  0.5626, -1.6730, -0.2517,  0.0818,  0.4442])
decoder.base_model.coeffs:tensor([ 1.5722,  0.5626, -1.6730, -0.2517,  0.0818,  0.4442])
