_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,  True],
        [False, False,  True,  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([[ 0.7049,  0.6252, -0.9435, -1.0848]])
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([[ 0.4318,  1.3017, -0.7915, -1.2719]])
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([[ 0.4970,  0.7826,  1.2890, -1.0403]])
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([[ 1.5495,  0.8339, -1.3118, -0.6220]])
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([[1.6555, 1.6265, 1.8778, 2.2317]])
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([[1.8523, 2.1041, 1.6399, 2.6510]])
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([[2.3515, 1.7434, 1.9070, 0.9038]])
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([[2.0081, 2.0903, 1.7141, 2.0722]])
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([-0.0798,  0.9674, -0.7761,  0.5552,  0.2274, -2.1934])
decoder.base_model.coeffs:tensor([-0.0798,  0.9674, -0.7761,  0.5552,  0.2274, -2.1934])
