_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,  True],
        [False, False, False, False],
        [False, False, False, False]])
scm.structure_transforms.0.log_scale:tensor([0.])
scm.structure_transforms.0.param_net.weight:tensor([[ 0.4240, -1.0087,  1.0119, -0.8656]])
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([[-1.3883, -0.4925, -0.9417,  0.7801]])
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([[-1.3438, -0.6233, -1.0685,  1.2668]])
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([[ 0.9644,  1.1339, -1.3862, -0.9507]])
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([[1.7849, 1.8966, 1.8324, 1.6157]])
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([[1.5054, 1.9030, 2.3303, 1.8391]])
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([[1.7317, 2.3605, 1.4936, 1.9938]])
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([[1.8192, 1.4942, 1.8918, 1.8815]])
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([ 0.5232, -0.6038,  2.3885, -0.3897, -1.6344,  0.1412])
decoder.base_model.coeffs:tensor([ 0.5232, -0.6038,  2.3885, -0.3897, -1.6344,  0.1412])
