_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,  True, False],
        [False, False, False,  True],
        [False, False, False,  True],
        [False, False, False, False]])
scm.structure_transforms.0.log_scale:tensor([0.])
scm.structure_transforms.0.param_net.weight:tensor([[1.4565, 0.7219, 0.9616, 1.5670]])
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([[-0.3805,  1.1739,  0.4697,  1.1926]])
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([[0.5565, 0.4067, 1.2480, 0.8196]])
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([[-0.8018, -0.7004, -1.4445, -0.6753]])
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([[-1.5607,  2.0125,  2.2786,  2.0168]])
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([[2.2642, 1.8311, 1.8015, 2.1129]])
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([[1.9671, 2.2892, 1.8614, 1.9414]])
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([[2.0544, 1.8153, 2.2925, 2.0872]])
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([-1.1947, -0.7185, -0.3509, -1.9502,  1.4475,  0.5872])
decoder.base_model.coeffs:tensor([-1.1947, -0.7185, -0.3509, -1.9502,  1.4475,  0.5872])
