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getattr_1 = None add = getitem + (32,); getitem = None reshape = x.reshape(-1, 1); x = None getattr_2 = reshape.shape getitem_1 = getattr_2[0]; getattr_2 = None reshape_1 = w.reshape(-1, 32); w = None reshape_2 = reshape.reshape(getitem_1, 1, 1); reshape = None reshape_3 = reshape_1.reshape((1, 32)); reshape_1 = None tensordot = torch.functional.tensordot(reshape_2, reshape_3, ([1], [0]), out = None); reshape_2 = reshape_3 = None permute = tensordot.permute([0, 2, 1]); tensordot = None reshape_4 = permute.reshape(getitem_1, 32); permute = getitem_1 = None reshape_5 = reshape_4.reshape(add); reshape_4 = add = None return reshape_5 q]X _tracer_clsq^Nubq_ubq`ubqaXlayer0_convnetqbc__torch__.nequip.nn._convnetlayer ConvNetLayer qc)}(hhNXfeature_irreps_hiddenqd(K KKK KKK KJK KJtXresnetqeX num_layersqfKXequivariant_nonlinqgc__torch__.e3nn.nn._gate Gate qh)}(hhNXirreps_scalarsqiK KKX irreps_gatesqjK KKX irreps_gatedqkK KJX _irreps_inqlK@KKK KJX _irreps_outqmK KKK KJXscqnc__torch__.e3nn.nn._gate _Sortcut qo)}(hhNX irreps_outsqpK KKK KKK KJhK@KKK KJXcutqqc__torch__.e3nn.nn._extract Extract qr)}(hhNhK KKK KKK KJhpK KKK KKK KJhUh](KKKeXList[Tuple[int]]qsRX_compiled_forwardqtc__torch__.torch.fx.graph_module.___torch_mangle_3 GraphModule qu)}(hhNh\XB def forward(self, x : torch.Tensor) -> typing_Tuple[torch.Tensor,torch.Tensor,torch.Tensor]: getattr_1 = x.shape getitem = getattr_1[-1]; getattr_1 = None eq = getitem == 160; getitem = None _assert = torch._assert(eq, 'invalid input shape'); eq = None getattr_2 = x.shape getitem_1 = getattr_2[slice(None, -1, None)]; getattr_2 = None add = getitem_1 + (32,); getitem_1 = None new_zeros = x.new_zeros(add); add = None getattr_3 = x.shape getitem_2 = getattr_3[slice(None, -1, None)]; getattr_3 = None add_1 = getitem_2 + (32,); getitem_2 = None new_zeros_1 = x.new_zeros(add_1); add_1 = None getattr_4 = x.shape getitem_3 = getattr_4[slice(None, -1, None)]; getattr_4 = None add_2 = getitem_3 + (96,); getitem_3 = None new_zeros_2 = x.new_zeros(add_2); add_2 = None narrow = new_zeros.narrow(-1, 0, 32) narrow_1 = x.narrow(-1, 0, 32) copy_ = narrow.copy_(narrow_1); narrow = narrow_1 = None narrow_2 = new_zeros_1.narrow(-1, 0, 32) narrow_3 = x.narrow(-1, 32, 32) copy__1 = narrow_2.copy_(narrow_3); narrow_2 = narrow_3 = None narrow_4 = new_zeros_2.narrow(-1, 0, 96) narrow_5 = x.narrow(-1, 64, 96); x = None copy__2 = narrow_4.copy_(narrow_5); narrow_4 = narrow_5 = None return (new_zeros, new_zeros_1, new_zeros_2) qvh^NubqwubqxubqyX act_scalarsqzc__torch__.e3nn.nn._activation Activation q{)}(hhNXactsq|c__torch__.torch.nn.modules.container ModuleList q})}(hhNh c__torch__.e3nn.math._normalize_activation normalize2mom q~)}(hhNubqubqubqX act_gatesqc__torch__.e3nn.nn._activation.___torch_mangle_37 Activation q)}(hhNh|c__torch__.torch.nn.modules.container.___torch_mangle_36 ModuleList q)}(hhNh c__torch__.e3nn.math._normalize_activation.___torch_mangle_34 normalize2mom q)}(hhNubqubqubqXmulqc__torch__.e3nn.o3._tensor_product._sub ElementwiseTensorProduct q)}(hJh((hhX6qh KtQqK(Kt(Kth )RtRhPh((hhX7qh K`tQqK(K`t(Kth )RtRhhNX irreps_in1qK KJX irreps_in2qK KKhK KJhUh]((KKKXuuuqG?zXLK teX List[Any]qRX_in1_dimqK`X_in2_dimqK hShTX_specialized_codeqhWhXKX_profiling_strqXGElementwiseTensorProduct(32x1o x 32x0e -> 32x1o | 32 paths | 0 weights)qX_compiled_main_left_rightqc__torch__.torch.fx.graph_module.___torch_mangle_4 GraphModule q)}(hhNh\X~ def forward(self, x1 : torch.Tensor, x2 : torch.Tensor, w : torch.Tensor) -> torch.Tensor: empty = torch.empty((), device = 'cpu') getattr_1 = x1.shape getitem = getattr_1[slice(None, -1, None)]; getattr_1 = None expand = empty.expand(getitem); getitem = None getattr_2 = x2.shape getitem_1 = getattr_2[slice(None, -1, None)]; getattr_2 = None expand_1 = empty.expand(getitem_1); empty = getitem_1 = None broadcast_tensors = torch.functional.broadcast_tensors(expand, expand_1); expand = expand_1 = None getitem_2 = broadcast_tensors[0]; broadcast_tensors = None getattr_3 = getitem_2.shape; getitem_2 = None add = getattr_3 + (-1,) broadcast_to = x1.broadcast_to(add); x1 = add = None add_1 = getattr_3 + (-1,) broadcast_to_1 = x2.broadcast_to(add_1); x2 = add_1 = None add_2 = getattr_3 + (96,); getattr_3 = None reshape = broadcast_to.reshape(-1, 96); broadcast_to = None reshape_1 = broadcast_to_1.reshape(-1, 32); broadcast_to_1 = None getattr_4 = reshape.shape getitem_3 = getattr_4[0]; getattr_4 = None reshape_2 = reshape.reshape(getitem_3, 32, 3); reshape = None reshape_3 = reshape_1.reshape(getitem_3, 32, 1); reshape_1 = None einsum = torch.functional.einsum('dcb,dca->dcab', reshape_3, reshape_2) reshape_4 = reshape_3.reshape(getitem_3, 32); reshape_3 = None einsum_1 = torch.functional.einsum('cb,cba->cba', reshape_4, reshape_2); reshape_4 = reshape_2 = None reshape_5 = einsum_1.reshape(getitem_3, 96); einsum_1 = getitem_3 = None reshape_6 = reshape_5.reshape(add_2); reshape_5 = add_2 = None return reshape_6 qh^NubqX_compiled_main_rightqc__torch__.torch.fx.graph_module tp_forward q)}(hhNh\X def forward(self, x2 : torch.Tensor, w : torch.Tensor): _assert = torch._assert(False, '`right` method is not compiled, set `compile_right` to True when creating the TensorProduct') qh^NubqubqubqXconvqc__torch__.nequip.nn._interaction_block InteractionBlock q)}(hhNXavg_num_neighborsqG@,+F1Xuse_scqXlinear_1qc__torch__.e3nn.o3._linear.___torch_mangle_40 Linear q)}(hJh((hhX8qh MtQqK(Mt(Kth )RtRhMh((hhX9qh KtQqK(Kt(Kth )RtRhPh((hhX10qh K tQqK(K t(Kth )RtRhhNhShThK KKhK KKhUh]((KKK K G?Ơf;tehVRhWhXMhYKhZc__torch__.torch.fx.graph_module.___torch_mangle_5 GraphModule q)}(hhNh\X def forward(self, x : torch.Tensor, w : torch.Tensor, b : torch.Tensor) -> torch.Tensor: getattr_1 = x.shape getitem = getattr_1[slice(None, -1, None)]; getattr_1 = None add = getitem + (32,); getitem = None reshape = x.reshape(-1, 32); x = None getattr_2 = reshape.shape getitem_1 = getattr_2[0]; getattr_2 = None reshape_1 = w.reshape(-1, 1024); w = None reshape_2 = reshape.reshape(getitem_1, 32, 1); reshape = None reshape_3 = reshape_1.reshape((32, 32)); reshape_1 = None mul = reshape_2 * 0.1767766952966369; reshape_2 = None tensordot = torch.functional.tensordot(mul, reshape_3, ([1], [0]), out = None); mul = reshape_3 = None permute = tensordot.permute([0, 2, 1]); tensordot = None reshape_4 = permute.reshape(getitem_1, 32); permute = getitem_1 = None reshape_5 = reshape_4.reshape(add); reshape_4 = add = None return reshape_5 qh^NubqubqXfcqc__torch__.e3nn.nn._fc FullyConnectedNet q)}(hhNXhsqh+(](KK@K@K@etRXlayer0qc__torch__.e3nn.nn._fc _Layer q)}(hJh((hhX11qh MtQqK(KK@t(K@Kth )RtRhhNXh_inqG@ Xh_outqG@PXvar_inqG?Xvar_outqG?hXLayer(8->64, act=normalize2mom)qXactqc__torch__.e3nn.math._normalize_activation.___torch_mangle_41 normalize2mom q)}(hhNubqubqXlayer1qc__torch__.e3nn.nn._fc.___torch_mangle_45 _Layer q)}(hJh((hhX12qh MtQqK(K@K@t(K@Kth )RtRhhNhG@PhG@PhG?hG?hX Layer(64->64, act=normalize2mom)qhc__torch__.e3nn.math._normalize_activation.___torch_mangle_43 normalize2mom q)}(hhNubqubqXlayer2qc__torch__.e3nn.nn._fc.___torch_mangle_46 _Layer q)}(hJh((hhX13qh MtQqK(K@K@t(K@Kth )RtRhhNhNhG@PhG@PhG?hG?hXLayer(64->64, act=None)qubqubqXtpqc__torch__.e3nn.o3._tensor_product._tensor_product TensorProduct q)}(hJh((hhX14qh KtQqK(Kt(Kth )RtRhPh((hhX15qh KtQqK(Kt(Kth )RtRhhNhK KKhKKKKKJhK KKK KJhUh]((KKKXuvuq׈G?K Kt(KKKh׈G?zXLK KtehRhK hKhShThhWhXK@hXGTensorProduct(32x0e x 1x0e+1x1o -> 32x0e+32x1o | 64 paths | 64 weights)qhc__torch__.torch.fx.graph_module.___torch_mangle_6 GraphModule q)}(hhNh\Xt def forward(self, x1 : torch.Tensor, x2 : torch.Tensor, w : torch.Tensor) -> torch.Tensor: empty = torch.empty((), device = 'cpu') getattr_1 = x1.shape getitem = getattr_1[slice(None, -1, None)]; getattr_1 = None expand = empty.expand(getitem); getitem = None getattr_2 = x2.shape getitem_1 = getattr_2[slice(None, -1, None)]; getattr_2 = None expand_1 = empty.expand(getitem_1); getitem_1 = None getattr_3 = w.shape getitem_2 = getattr_3[slice(None, -1, None)]; getattr_3 = None expand_2 = empty.expand(getitem_2); empty = getitem_2 = None broadcast_tensors = torch.functional.broadcast_tensors(expand, expand_1, expand_2); expand = expand_1 = expand_2 = None getitem_3 = broadcast_tensors[0]; broadcast_tensors = None getattr_4 = getitem_3.shape; getitem_3 = None add = getattr_4 + (-1,) broadcast_to = x1.broadcast_to(add); x1 = add = None add_1 = getattr_4 + (-1,) broadcast_to_1 = x2.broadcast_to(add_1); x2 = add_1 = None add_2 = getattr_4 + (-1,) broadcast_to_2 = w.broadcast_to(add_2); w = add_2 = None add_3 = getattr_4 + (128,); getattr_4 = None reshape = broadcast_to.reshape(-1, 32); broadcast_to = None reshape_1 = broadcast_to_1.reshape(-1, 4); broadcast_to_1 = None getattr_5 = reshape.shape getitem_4 = getattr_5[0]; getattr_5 = None reshape_2 = broadcast_to_2.reshape(-1, 64); broadcast_to_2 = None reshape_3 = reshape.reshape(getitem_4, 32, 1); reshape = None getitem_5 = reshape_1[(slice(None, None, None), slice(0, 1, None))] reshape_4 = getitem_5.reshape(getitem_4, 1, 1); getitem_5 = None getitem_6 = reshape_1[(slice(None, None, None), slice(1, 4, None))]; reshape_1 = None reshape_5 = getitem_6.reshape(getitem_4, 1, 3); getitem_6 = None getitem_7 = reshape_2[(slice(None, None, None), slice(0, 32, None))] reshape_6 = getitem_7.reshape((-1, 32, 1)); getitem_7 = None einsum = torch.functional.einsum('edb,eca->ecdab', reshape_4, reshape_3) reshape_7 = reshape_3.reshape(getitem_4, 32) reshape_8 = reshape_4.reshape(getitem_4, 1); reshape_4 = None einsum_1 = torch.functional.einsum('ca,cab->cab', reshape_7, reshape_6); reshape_7 = reshape_6 = None einsum_2 = torch.functional.einsum('cab,cb->ca', einsum_1, reshape_8); einsum_1 = reshape_8 = None reshape_9 = einsum_2.reshape(getitem_4, 32); einsum_2 = None getitem_8 = reshape_2[(slice(None, None, None), slice(32, 64, None))]; reshape_2 = None reshape_10 = getitem_8.reshape((-1, 32, 1)); getitem_8 = None einsum_3 = torch.functional.einsum('edb,eca->ecdab', reshape_5, reshape_3) reshape_11 = reshape_3.reshape(getitem_4, 32); reshape_3 = None einsum_4 = torch.functional.einsum('ca,cab->cab', reshape_11, reshape_10); reshape_11 = reshape_10 = None einsum_5 = torch.functional.einsum('dbc,dca->dba', einsum_4, reshape_5); einsum_4 = reshape_5 = None reshape_12 = einsum_5.reshape(getitem_4, 96); einsum_5 = getitem_4 = None cat = torch.cat([reshape_9, reshape_12], dim = 1); reshape_9 = reshape_12 = None reshape_13 = cat.reshape(add_3); cat = add_3 = None return reshape_13 qh^Nubqhc__torch__.torch.fx.graph_module.___torch_mangle_7 tp_forward q)}(hhNh\hh^NubqubqXlinear_2qc__torch__.e3nn.o3._linear.___torch_mangle_47 Linear q)}(hJh((hhX16qh M tQqK(M t(Kth )RtRhMh((hhX17qh KtQqK(Kt(Kth )RtRhPh((hhX18qh KtQqK(Kt(Kth )RtRhhNhShThK KKK KJhK@KKK KJhUh]((KKK K@G?Ơf;t(KKK K G?Ơf;tehVRhWhXM hYKhZc__torch__.torch.fx.graph_module.___torch_mangle_8 GraphModule q)}(hhNh\Xi def forward(self, x : torch.Tensor, w : torch.Tensor, b : torch.Tensor) -> torch.Tensor: getattr_1 = x.shape getitem = getattr_1[slice(None, -1, None)]; getattr_1 = None add = getitem + (160,); getitem = None reshape = x.reshape(-1, 128); x = None getattr_2 = reshape.shape getitem_1 = getattr_2[0]; getattr_2 = None reshape_1 = w.reshape(-1, 3072); w = None narrow = reshape.narrow(-1, 0, 32) reshape_2 = narrow.reshape(getitem_1, 32, 1); narrow = None narrow_1 = reshape.narrow(-1, 32, 96); reshape = None reshape_3 = narrow_1.reshape(getitem_1, 32, 3); narrow_1 = None narrow_2 = reshape_1.narrow(-1, 0, 2048) reshape_4 = narrow_2.reshape((32, 64)); narrow_2 = None mul = reshape_2 * 0.1767766952966369; reshape_2 = None tensordot = torch.functional.tensordot(mul, reshape_4, ([1], [0]), out = None); mul = reshape_4 = None permute = tensordot.permute([0, 2, 1]); tensordot = None reshape_5 = permute.reshape(getitem_1, 64); permute = None narrow_3 = reshape_1.narrow(-1, 2048, 1024); reshape_1 = None reshape_6 = narrow_3.reshape((32, 32)); narrow_3 = None mul_1 = reshape_3 * 0.1767766952966369; reshape_3 = None tensordot_1 = torch.functional.tensordot(mul_1, reshape_6, ([1], [0]), out = None); mul_1 = reshape_6 = None permute_1 = tensordot_1.permute([0, 2, 1]); tensordot_1 = None reshape_7 = permute_1.reshape(getitem_1, 96); permute_1 = getitem_1 = None cat = torch.cat([reshape_5, reshape_7], dim = -1); reshape_5 = reshape_7 = None reshape_8 = cat.reshape(add); cat = add = None return reshape_8 qh^Nubqubqhnc__torch__.e3nn.o3._tensor_product._sub FullyConnectedTensorProduct q)}(hJh((hhX19qh MtQqK(Mt(Kth )RtRhPh((hhX20qh KtQqK(Kt(Kth )RtRhhNhK KKhKKKhK@KKK KJhUh]((KKKXuvwqG?Ơf;K KK@tehRhK hKhShThhWhXMhXTFullyConnectedTensorProduct(32x0e x 1x0e -> 64x0e+32x1o | 2048 paths | 2048 weights)qhc__torch__.torch.fx.graph_module.___torch_mangle_9 GraphModule q)}(hhNh\X def forward(self, x1 : torch.Tensor, x2 : torch.Tensor, w : torch.Tensor) -> torch.Tensor: empty = torch.empty((), device = 'cpu') getattr_1 = x1.shape getitem = getattr_1[slice(None, -1, None)]; getattr_1 = None expand = empty.expand(getitem); getitem = None getattr_2 = x2.shape getitem_1 = getattr_2[slice(None, -1, None)]; getattr_2 = None expand_1 = empty.expand(getitem_1); empty = getitem_1 = None broadcast_tensors = torch.functional.broadcast_tensors(expand, expand_1); expand = expand_1 = None getitem_2 = broadcast_tensors[0]; broadcast_tensors = None getattr_3 = getitem_2.shape; getitem_2 = None add = getattr_3 + (-1,) broadcast_to = x1.broadcast_to(add); x1 = add = None add_1 = getattr_3 + (-1,) broadcast_to_1 = x2.broadcast_to(add_1); x2 = add_1 = None add_2 = getattr_3 + (160,); getattr_3 = None reshape = broadcast_to.reshape(-1, 32); broadcast_to = None reshape_1 = broadcast_to_1.reshape(-1, 1); broadcast_to_1 = None getattr_4 = reshape.shape getitem_3 = getattr_4[0]; getattr_4 = None reshape_2 = w.reshape(-1, 2048); w = None reshape_3 = reshape.reshape(getitem_3, 32, 1) reshape_4 = reshape_1.reshape(getitem_3, 1, 1); reshape_1 = None getitem_4 = reshape_2[(slice(None, None, None), slice(0, 2048, None))]; reshape_2 = None reshape_5 = getitem_4.reshape((32, 1, 64)); getitem_4 = None einsum = torch.functional.einsum('edb,eca->ecdab', reshape_4, reshape_3) reshape_6 = reshape_3.reshape(getitem_3, 32); reshape_3 = None reshape_7 = reshape_4.reshape(getitem_3, 1); reshape_4 = None mul = reshape_7 * 0.1767766952966369; reshape_7 = None einsum_1 = torch.functional.einsum('cb,ca->cba', mul, reshape_6); mul = reshape_6 = None tensordot = torch.functional.tensordot(einsum_1, reshape_5, ([1, 2], [1, 0]), out = None); einsum_1 = reshape_5 = None reshape_8 = tensordot.reshape(getitem_3, 64); tensordot = None new_zeros = reshape.new_zeros((getitem_3, 96)); reshape = getitem_3 = None cat = torch.cat([reshape_8, new_zeros], dim = 1); reshape_8 = new_zeros = None reshape_9 = cat.reshape(add_2); cat = add_2 = None return reshape_9 qh^Nubqhc__torch__.torch.fx.graph_module.___torch_mangle_10 tp_forward q)}(hhNh\hh^NubqubqubqubqXlayer1_convnetqc__torch__.nequip.nn._convnetlayer.___torch_mangle_111 ConvNetLayer q)}(hhNhd(K KKK KKK KJK KJthehfKhgc__torch__.e3nn.nn._gate.___torch_mangle_65 Gate q)}(hhNhiK KKhjK@KKhkK KKK KJhlK`KKK KJK KKhmK KKK KKK KJhnc__torch__.e3nn.nn._gate.___torch_mangle_49 _Sortcut q)}(hhNhpK KKK@KKK KKK KJhK`KKK KJK KKhqc__torch__.e3nn.nn._extract.___torch_mangle_48 Extract q)}(hhNh(K KKK@KKK KJK KKthpK KKK@KKK KKK KJhtc__torch__.torch.fx.graph_module.___torch_mangle_11 GraphModule q)}(hhNh\X def forward(self, x : torch.Tensor) -> typing_Tuple[torch.Tensor,torch.Tensor,torch.Tensor]: getattr_1 = x.shape getitem = getattr_1[-1]; getattr_1 = None eq = getitem == 288; getitem = None _assert = torch._assert(eq, 'invalid input shape'); eq = None getattr_2 = x.shape getitem_1 = getattr_2[slice(None, -1, None)]; getattr_2 = None add = getitem_1 + (32,); getitem_1 = None new_zeros = x.new_zeros(add); add = None getattr_3 = x.shape getitem_2 = getattr_3[slice(None, -1, None)]; getattr_3 = None add_1 = getitem_2 + (64,); getitem_2 = None new_zeros_1 = x.new_zeros(add_1); add_1 = None getattr_4 = x.shape getitem_3 = getattr_4[slice(None, -1, None)]; getattr_4 = None add_2 = getitem_3 + (192,); getitem_3 = None new_zeros_2 = x.new_zeros(add_2); add_2 = None narrow = new_zeros.narrow(-1, 0, 32) narrow_1 = x.narrow(-1, 0, 32) copy_ = narrow.copy_(narrow_1); narrow = narrow_1 = None narrow_2 = new_zeros_1.narrow(-1, 0, 64) narrow_3 = x.narrow(-1, 32, 64) copy__1 = narrow_2.copy_(narrow_3); narrow_2 = narrow_3 = None narrow_4 = new_zeros_2.narrow(-1, 0, 96) narrow_5 = x.narrow(-1, 192, 96) copy__2 = narrow_4.copy_(narrow_5); narrow_4 = narrow_5 = None narrow_6 = new_zeros_2.narrow(-1, 96, 96) narrow_7 = x.narrow(-1, 96, 96); x = None copy__3 = narrow_6.copy_(narrow_7); narrow_6 = narrow_7 = None return (new_zeros, new_zeros_1, new_zeros_2) rh^Nubrubrubrhzc__torch__.e3nn.nn._activation.___torch_mangle_53 Activation r)}(hhNh|c__torch__.torch.nn.modules.container.___torch_mangle_52 ModuleList r)}(hhNh c__torch__.e3nn.math._normalize_activation.___torch_mangle_50 normalize2mom r)}(hhNubrubrubr hc__torch__.e3nn.nn._activation.___torch_mangle_61 Activation r )}(hhNh|c__torch__.torch.nn.modules.container.___torch_mangle_60 ModuleList r )}(hhNh c__torch__.e3nn.math._normalize_activation.___torch_mangle_56 normalize2mom r )}(hhNubr hc__torch__.e3nn.math._normalize_activation.___torch_mangle_58 normalize2mom r)}(hhNubrubrubrhc__torch__.e3nn.o3._tensor_product._sub.___torch_mangle_64 ElementwiseTensorProduct r)}(hJh((hhX21rh KtQrK(Kt(Kth )RtRhPh((hhX22rh KtQrK(Kt(Kth )RtRhhNhK KKK KJhK KKK KKhK KKK KJhUh]((KKKhG?zXLK t(KKKhG?zXLK tehRhKhK@hShThhWhXKhXSElementwiseTensorProduct(32x1e+32x1o x 64x0e -> 32x1e+32x1o | 64 paths | 0 weights)rhc__torch__.torch.fx.graph_module.___torch_mangle_12 GraphModule r)}(hhNh\X def forward(self, x1 : torch.Tensor, x2 : torch.Tensor, w : torch.Tensor) -> torch.Tensor: empty = torch.empty((), device = 'cpu') getattr_1 = x1.shape getitem = getattr_1[slice(None, -1, None)]; getattr_1 = None expand = empty.expand(getitem); getitem = None getattr_2 = x2.shape getitem_1 = getattr_2[slice(None, -1, None)]; getattr_2 = None expand_1 = empty.expand(getitem_1); empty = getitem_1 = None broadcast_tensors = torch.functional.broadcast_tensors(expand, expand_1); expand = expand_1 = None getitem_2 = broadcast_tensors[0]; broadcast_tensors = None getattr_3 = getitem_2.shape; getitem_2 = None add = getattr_3 + (-1,) broadcast_to = x1.broadcast_to(add); x1 = add = None add_1 = getattr_3 + (-1,) broadcast_to_1 = x2.broadcast_to(add_1); x2 = add_1 = None add_2 = getattr_3 + (192,); getattr_3 = None reshape = broadcast_to.reshape(-1, 192); broadcast_to = None reshape_1 = broadcast_to_1.reshape(-1, 64); broadcast_to_1 = None getattr_4 = reshape.shape getitem_3 = getattr_4[0]; getattr_4 = None getitem_4 = reshape[(slice(None, None, None), slice(0, 96, None))] reshape_2 = getitem_4.reshape(getitem_3, 32, 3); getitem_4 = None getitem_5 = reshape[(slice(None, None, None), slice(96, 192, None))]; reshape = None reshape_3 = getitem_5.reshape(getitem_3, 32, 3); getitem_5 = None getitem_6 = reshape_1[(slice(None, None, None), slice(0, 32, None))] reshape_4 = getitem_6.reshape(getitem_3, 32, 1); getitem_6 = None getitem_7 = reshape_1[(slice(None, None, None), slice(32, 64, None))]; reshape_1 = None reshape_5 = getitem_7.reshape(getitem_3, 32, 1); getitem_7 = None einsum = torch.functional.einsum('dcb,dca->dcab', reshape_4, reshape_2) reshape_6 = reshape_4.reshape(getitem_3, 32); reshape_4 = None einsum_1 = torch.functional.einsum('cb,cba->cba', reshape_6, reshape_2); reshape_6 = reshape_2 = None reshape_7 = einsum_1.reshape(getitem_3, 96); einsum_1 = None einsum_2 = torch.functional.einsum('dcb,dca->dcab', reshape_5, reshape_3) reshape_8 = reshape_5.reshape(getitem_3, 32); reshape_5 = None einsum_3 = torch.functional.einsum('cb,cba->cba', reshape_8, reshape_3); reshape_8 = reshape_3 = None reshape_9 = einsum_3.reshape(getitem_3, 96); einsum_3 = getitem_3 = None cat = torch.cat([reshape_7, reshape_9], dim = 1); reshape_7 = reshape_9 = None reshape_10 = cat.reshape(add_2); cat = add_2 = None return reshape_10 rh^Nubrhc__torch__.torch.fx.graph_module.___torch_mangle_13 tp_forward r)}(hhNh\hh^Nubrubrubrhc__torch__.nequip.nn._interaction_block.___torch_mangle_110 InteractionBlock r)}(hhNhG@,+F1hhc__torch__.e3nn.o3._linear.___torch_mangle_66 Linear r )}(hJh((hhX23r!h MtQr"K(Mt(Kth )RtRhMh((hhX24r#h KtQr$K(Kt(Kth )RtRhPh((hhX25r%h KtQr&K(Kt(Kth )RtRhhNhShThK KKK KJhK KKK KJhUh]((KKK K G?Ơf;t(KKK K G?Ơf;tehVRhWhXMhYKhZc__torch__.torch.fx.graph_module.___torch_mangle_14 GraphModule r')}(hhNh\Xi def forward(self, x : torch.Tensor, w : torch.Tensor, b : torch.Tensor) -> torch.Tensor: getattr_1 = x.shape getitem = getattr_1[slice(None, -1, None)]; getattr_1 = None add = getitem + (128,); getitem = None reshape = x.reshape(-1, 128); x = None getattr_2 = reshape.shape getitem_1 = getattr_2[0]; getattr_2 = None reshape_1 = w.reshape(-1, 2048); w = None narrow = reshape.narrow(-1, 0, 32) reshape_2 = narrow.reshape(getitem_1, 32, 1); narrow = None narrow_1 = reshape.narrow(-1, 32, 96); reshape = None reshape_3 = narrow_1.reshape(getitem_1, 32, 3); narrow_1 = None narrow_2 = reshape_1.narrow(-1, 0, 1024) reshape_4 = narrow_2.reshape((32, 32)); narrow_2 = None mul = reshape_2 * 0.1767766952966369; reshape_2 = None tensordot = torch.functional.tensordot(mul, reshape_4, ([1], [0]), out = None); mul = reshape_4 = None permute = tensordot.permute([0, 2, 1]); tensordot = None reshape_5 = permute.reshape(getitem_1, 32); permute = None narrow_3 = reshape_1.narrow(-1, 1024, 1024); reshape_1 = None reshape_6 = narrow_3.reshape((32, 32)); narrow_3 = None mul_1 = reshape_3 * 0.1767766952966369; reshape_3 = None tensordot_1 = torch.functional.tensordot(mul_1, reshape_6, ([1], [0]), out = None); mul_1 = reshape_6 = None permute_1 = tensordot_1.permute([0, 2, 1]); tensordot_1 = None reshape_7 = permute_1.reshape(getitem_1, 96); permute_1 = getitem_1 = None cat = torch.cat([reshape_5, reshape_7], dim = -1); reshape_5 = reshape_7 = None reshape_8 = cat.reshape(add); cat = add = None return reshape_8 r(h^Nubr)ubr*hc__torch__.e3nn.nn._fc.___torch_mangle_73 FullyConnectedNet r+)}(hhNhh+(](KK@K@KetRhc__torch__.e3nn.nn._fc.___torch_mangle_69 _Layer r,)}(hJh((hhX26r-h MtQr.K(KK@t(K@Kth )RtRhhNhG@ hG@PhG?hG?hhhc__torch__.e3nn.math._normalize_activation.___torch_mangle_67 normalize2mom r/)}(hhNubr0ubr1hc__torch__.e3nn.nn._fc.___torch_mangle_72 _Layer r2)}(hJh((hhX27r3h MtQr4K(K@K@t(K@Kth )RtRhhNhG@PhG@PhG?hG?hhhc__torch__.e3nn.math._normalize_activation.___torch_mangle_70 normalize2mom r5)}(hhNubr6ubr7hh)}(hJh((hhX28r8h M(tQr9K(K@Kt(KKth )RtRhhNhNhG@PhG@dhG?hG?hXLayer(64->160, act=None)r:ubr;ubr<hc__torch__.e3nn.o3._tensor_product._tensor_product.___torch_mangle_74 TensorProduct r=)}(hJh((hhX29r>h KtQr?K(Kt(Kth )RtRhPh((hhX30r@h M`tQrAK(M`t(Kth )RtRhhNhK KKK KJhKKKKKJh(K KKK KKK KJK KJK KKthUh]((KKKh׈G?K Kt(KKKh׈G?zXLK Kt(KKKh׈G?zXLK Kt(KKKh׈G?K Kt(KKKh׈G?zXLK KtehRhKhKhShThhWhXKhXUTensorProduct(32x0e+32x1o x 1x0e+1x1o -> 64x0e+64x1o+32x1e | 160 paths | 160 weights)rBhc__torch__.torch.fx.graph_module.___torch_mangle_15 GraphModule rC)}(X _w3j_1_1_1rDh((hhX31rEh KtQrFK(KKKt(K KKth )RtRhhNh\X  def forward(self, x1 : torch.Tensor, x2 : torch.Tensor, w : torch.Tensor) -> torch.Tensor: empty = torch.empty((), device = 'cpu') getattr_1 = x1.shape getitem = getattr_1[slice(None, -1, None)]; getattr_1 = None expand = empty.expand(getitem); getitem = None getattr_2 = x2.shape getitem_1 = getattr_2[slice(None, -1, None)]; getattr_2 = None expand_1 = empty.expand(getitem_1); getitem_1 = None getattr_3 = w.shape getitem_2 = getattr_3[slice(None, -1, None)]; getattr_3 = None expand_2 = empty.expand(getitem_2); empty = getitem_2 = None broadcast_tensors = torch.functional.broadcast_tensors(expand, expand_1, expand_2); expand = expand_1 = expand_2 = None getitem_3 = broadcast_tensors[0]; broadcast_tensors = None getattr_4 = getitem_3.shape; getitem_3 = None add = getattr_4 + (-1,) broadcast_to = x1.broadcast_to(add); x1 = add = None add_1 = getattr_4 + (-1,) broadcast_to_1 = x2.broadcast_to(add_1); x2 = add_1 = None add_2 = getattr_4 + (-1,) broadcast_to_2 = w.broadcast_to(add_2); w = add_2 = None add_3 = getattr_4 + (352,); getattr_4 = None reshape = broadcast_to.reshape(-1, 128); broadcast_to = None reshape_1 = broadcast_to_1.reshape(-1, 4); broadcast_to_1 = None getattr_5 = reshape.shape getitem_4 = getattr_5[0]; getattr_5 = None reshape_2 = broadcast_to_2.reshape(-1, 160); broadcast_to_2 = None getitem_5 = reshape[(slice(None, None, None), slice(0, 32, None))] reshape_3 = getitem_5.reshape(getitem_4, 32, 1); getitem_5 = None getitem_6 = reshape[(slice(None, None, None), slice(32, 128, None))]; reshape = None reshape_4 = getitem_6.reshape(getitem_4, 32, 3); getitem_6 = None getitem_7 = reshape_1[(slice(None, None, None), slice(0, 1, None))] reshape_5 = getitem_7.reshape(getitem_4, 1, 1); getitem_7 = None getitem_8 = reshape_1[(slice(None, None, None), slice(1, 4, None))]; reshape_1 = None reshape_6 = getitem_8.reshape(getitem_4, 1, 3); getitem_8 = None getitem_9 = reshape_2[(slice(None, None, None), slice(0, 32, None))] reshape_7 = getitem_9.reshape((-1, 32, 1)); getitem_9 = None einsum = torch.functional.einsum('edb,eca->ecdab', reshape_5, reshape_3) reshape_8 = reshape_3.reshape(getitem_4, 32) reshape_9 = reshape_5.reshape(getitem_4, 1) einsum_1 = torch.functional.einsum('ca,cab->cab', reshape_8, reshape_7); reshape_8 = reshape_7 = None einsum_2 = torch.functional.einsum('cab,cb->ca', einsum_1, reshape_9); einsum_1 = reshape_9 = None reshape_10 = einsum_2.reshape(getitem_4, 32); einsum_2 = None getitem_10 = reshape_2[(slice(None, None, None), slice(32, 64, None))] reshape_11 = getitem_10.reshape((-1, 32, 1)); getitem_10 = None einsum_3 = torch.functional.einsum('edb,eca->ecdab', reshape_6, reshape_3) reshape_12 = reshape_3.reshape(getitem_4, 32); reshape_3 = None einsum_4 = torch.functional.einsum('ca,cab->cab', reshape_12, reshape_11); reshape_12 = reshape_11 = None einsum_5 = torch.functional.einsum('dbc,dca->dba', einsum_4, reshape_6); einsum_4 = None reshape_13 = einsum_5.reshape(getitem_4, 96); einsum_5 = None getitem_11 = reshape_2[(slice(None, None, None), slice(64, 96, None))] reshape_14 = getitem_11.reshape((-1, 32, 1)); getitem_11 = None einsum_6 = torch.functional.einsum('edb,eca->ecdab', reshape_5, reshape_4) reshape_15 = reshape_5.reshape(getitem_4, 1); reshape_5 = None einsum_7 = torch.functional.einsum('cb,cab->ca', reshape_15, reshape_14); reshape_15 = reshape_14 = None einsum_8 = torch.functional.einsum('cb,cba->cba', einsum_7, reshape_4); einsum_7 = None reshape_16 = einsum_8.reshape(getitem_4, 96); einsum_8 = None getitem_12 = reshape_2[(slice(None, None, None), slice(96, 128, None))] reshape_17 = getitem_12.reshape((-1, 32, 1)); getitem_12 = None mul = reshape_6 * 0.5773502691896258 einsum_9 = torch.functional.einsum('dca,dba->dcb', mul, reshape_4); mul = None einsum_10 = torch.functional.einsum('cba,cab->ca', einsum_9, reshape_17); einsum_9 = reshape_17 = None reshape_18 = einsum_10.reshape(getitem_4, 32); einsum_10 = None getitem_13 = reshape_2[(slice(None, None, None), slice(128, 160, None))]; reshape_2 = None reshape_19 = getitem_13.reshape((-1, 32, 1)); getitem_13 = None _w3j_1_1_1 = self._w3j_1_1_1 einsum_11 = torch.functional.einsum('dba,dbc->dbac', reshape_4, reshape_19); reshape_4 = reshape_19 = None mul_1 = reshape_6 * 1.7320508075688772; reshape_6 = None tensordot = torch.functional.tensordot(mul_1, _w3j_1_1_1, ([2], [1]), out = None); mul_1 = _w3j_1_1_1 = None einsum_12 = torch.functional.einsum('edab,ecad->ecb', tensordot, einsum_11); tensordot = einsum_11 = None reshape_20 = einsum_12.reshape(getitem_4, 96); einsum_12 = getitem_4 = None cat = torch.cat([reshape_10, reshape_18, reshape_13, reshape_16, reshape_20], dim = 1); reshape_10 = reshape_18 = reshape_13 = reshape_16 = reshape_20 = None reshape_21 = cat.reshape(add_3); cat = add_3 = None return reshape_21 rGh^NubrHhc__torch__.torch.fx.graph_module.___torch_mangle_16 tp_forward rI)}(hhNh\hh^NubrJubrKhc__torch__.e3nn.o3._linear.___torch_mangle_75 Linear rL)}(hJh((hhX32rMh M$tQrNK(M$t(Kth )RtRhMh((hhX33rOh KtQrPK(Kt(Kth )RtRhPh((hhX34rQh M tQrRK(M t(Kth )RtRhhNhShThK@KKK@KJK KKhK`KKK KJK KKhUh]((KKK@K`G?t(KKK@K G?t(KKK K G?Ơf;tehVRhWhXM$hYKhZc__torch__.torch.fx.graph_module.___torch_mangle_17 GraphModule rS)}(hhNh\X def forward(self, x : torch.Tensor, w : torch.Tensor, b : torch.Tensor) -> torch.Tensor: getattr_1 = x.shape getitem = getattr_1[slice(None, -1, None)]; getattr_1 = None add = getitem + (288,); getitem = None reshape = x.reshape(-1, 352); x = None getattr_2 = reshape.shape getitem_1 = getattr_2[0]; getattr_2 = None reshape_1 = w.reshape(-1, 9216); w = None narrow = reshape.narrow(-1, 0, 64) reshape_2 = narrow.reshape(getitem_1, 64, 1); narrow = None narrow_1 = reshape.narrow(-1, 64, 192) reshape_3 = narrow_1.reshape(getitem_1, 64, 3); narrow_1 = None narrow_2 = reshape.narrow(-1, 256, 96); reshape = None reshape_4 = narrow_2.reshape(getitem_1, 32, 3); narrow_2 = None narrow_3 = reshape_1.narrow(-1, 0, 6144) reshape_5 = narrow_3.reshape((64, 96)); narrow_3 = None mul = reshape_2 * 0.125; reshape_2 = None tensordot = torch.functional.tensordot(mul, reshape_5, ([1], [0]), out = None); mul = reshape_5 = None permute = tensordot.permute([0, 2, 1]); tensordot = None reshape_6 = permute.reshape(getitem_1, 96); permute = None narrow_4 = reshape_1.narrow(-1, 6144, 2048) reshape_7 = narrow_4.reshape((64, 32)); narrow_4 = None tensordot_1 = torch.functional.tensordot(reshape_3, reshape_7, ([1], [0]), out = None); reshape_3 = reshape_7 = None mul_1 = tensordot_1 * 0.125; tensordot_1 = None permute_1 = mul_1.permute([0, 2, 1]); mul_1 = None reshape_8 = permute_1.reshape(getitem_1, 96); permute_1 = None narrow_5 = reshape_1.narrow(-1, 8192, 1024); reshape_1 = None reshape_9 = narrow_5.reshape((32, 32)); narrow_5 = None mul_2 = reshape_4 * 0.1767766952966369; reshape_4 = None tensordot_2 = torch.functional.tensordot(mul_2, reshape_9, ([1], [0]), out = None); mul_2 = reshape_9 = None permute_2 = tensordot_2.permute([0, 2, 1]); tensordot_2 = None reshape_10 = permute_2.reshape(getitem_1, 96); permute_2 = getitem_1 = None cat = torch.cat([reshape_6, reshape_8, reshape_10], dim = -1); reshape_6 = reshape_8 = reshape_10 = None reshape_11 = cat.reshape(add); cat = add = None return reshape_11 rTh^NubrUubrVhnc__torch__.e3nn.o3._tensor_product._sub.___torch_mangle_76 FullyConnectedTensorProduct rW)}(hJh((hhX35rXh MtQrYK(Mt(Kth )RtRhPh((hhX36rZh M tQr[K(M t(Kth )RtRhhNhK KKK KJhKKKhK`KKK KJK KKhUh]((KKKhG?Ơf;K KK`t(KKKhG?Ә !.K KK tehRhKhKhShThhWhXMhX`FullyConnectedTensorProduct(32x0e+32x1o x 1x0e -> 96x0e+32x1o+32x1e | 4096 paths | 4096 weights)r\hc__torch__.torch.fx.graph_module.___torch_mangle_18 GraphModule r])}(hhNh\Xx def forward(self, x1 : torch.Tensor, x2 : torch.Tensor, w : torch.Tensor) -> torch.Tensor: empty = torch.empty((), device = 'cpu') getattr_1 = x1.shape getitem = getattr_1[slice(None, -1, None)]; getattr_1 = None expand = empty.expand(getitem); getitem = None getattr_2 = x2.shape getitem_1 = getattr_2[slice(None, -1, None)]; getattr_2 = None expand_1 = empty.expand(getitem_1); empty = getitem_1 = None broadcast_tensors = torch.functional.broadcast_tensors(expand, expand_1); expand = expand_1 = None getitem_2 = broadcast_tensors[0]; broadcast_tensors = None getattr_3 = getitem_2.shape; getitem_2 = None add = getattr_3 + (-1,) broadcast_to = x1.broadcast_to(add); x1 = add = None add_1 = getattr_3 + (-1,) broadcast_to_1 = x2.broadcast_to(add_1); x2 = add_1 = None add_2 = getattr_3 + (288,); getattr_3 = None reshape = broadcast_to.reshape(-1, 128); broadcast_to = None reshape_1 = broadcast_to_1.reshape(-1, 1); broadcast_to_1 = None getattr_4 = reshape.shape getitem_3 = getattr_4[0]; getattr_4 = None reshape_2 = w.reshape(-1, 4096); w = None getitem_4 = reshape[(slice(None, None, None), slice(0, 32, None))] reshape_3 = getitem_4.reshape(getitem_3, 32, 1); getitem_4 = None getitem_5 = reshape[(slice(None, None, None), slice(32, 128, None))] reshape_4 = getitem_5.reshape(getitem_3, 32, 3); getitem_5 = None reshape_5 = reshape_1.reshape(getitem_3, 1, 1); reshape_1 = None getitem_6 = reshape_2[(slice(None, None, None), slice(0, 3072, None))] reshape_6 = getitem_6.reshape((32, 1, 96)); getitem_6 = None einsum = torch.functional.einsum('edb,eca->ecdab', reshape_5, reshape_3) reshape_7 = reshape_3.reshape(getitem_3, 32); reshape_3 = None reshape_8 = reshape_5.reshape(getitem_3, 1) mul = reshape_8 * 0.1767766952966369; reshape_8 = None einsum_1 = torch.functional.einsum('cb,ca->cba', mul, reshape_7); mul = reshape_7 = None tensordot = torch.functional.tensordot(einsum_1, reshape_6, ([1, 2], [1, 0]), out = None); einsum_1 = reshape_6 = None reshape_9 = tensordot.reshape(getitem_3, 96); tensordot = None getitem_7 = reshape_2[(slice(None, None, None), slice(3072, 4096, None))]; reshape_2 = None reshape_10 = getitem_7.reshape((32, 1, 32)); getitem_7 = None einsum_2 = torch.functional.einsum('edb,eca->ecdab', reshape_5, reshape_4) reshape_11 = reshape_5.reshape(getitem_3, 1); reshape_5 = None mul_1 = reshape_11 * 0.1767766952966369; reshape_11 = None einsum_3 = torch.functional.einsum('dc,dba->dcba', mul_1, reshape_4); mul_1 = reshape_4 = None tensordot_1 = torch.functional.tensordot(einsum_3, reshape_10, ([1, 2], [1, 0]), out = None); einsum_3 = reshape_10 = None permute = tensordot_1.permute([0, 2, 1]); tensordot_1 = None reshape_12 = permute.reshape(getitem_3, 96); permute = None new_zeros = reshape.new_zeros((getitem_3, 96)); reshape = getitem_3 = None cat = torch.cat([reshape_9, reshape_12, new_zeros], dim = 1); reshape_9 = reshape_12 = new_zeros = None reshape_13 = cat.reshape(add_2); cat = add_2 = None return reshape_13 r^h^Nubr_hc__torch__.torch.fx.graph_module.___torch_mangle_19 tp_forward r`)}(hhNh\hh^NubraubrbubrcubrdXlayer2_convnetrec__torch__.nequip.nn._convnetlayer.___torch_mangle_113 ConvNetLayer rf)}(hhNhd(K KKK KKK KJK KJthehfKhgc__torch__.e3nn.nn._gate.___torch_mangle_96 Gate rg)}(hhNhiK KKK KJhjK@KKhkK KKK KJhl(K KJK`KKK KJK KKthm(K KKK KJK KKK KJthnc__torch__.e3nn.nn._gate.___torch_mangle_78 _Sortcut rh)}(hhNhpK KKK KJK@KKK KKK KJh(K KJK`KKK KJK KKthqc__torch__.e3nn.nn._extract.___torch_mangle_77 Extract ri)}(hhNh(K KJK KKK@KKK KJK KKthpK KKK KJK@KKK KKK KJhtc__torch__.torch.fx.graph_module.___torch_mangle_20 GraphModule rj)}(hhNh\Xo def forward(self, x : torch.Tensor) -> typing_Tuple[torch.Tensor,torch.Tensor,torch.Tensor]: getattr_1 = x.shape getitem = getattr_1[-1]; getattr_1 = None eq = getitem == 320; getitem = None _assert = torch._assert(eq, 'invalid input shape'); eq = None getattr_2 = x.shape getitem_1 = getattr_2[slice(None, -1, None)]; getattr_2 = None add = getitem_1 + (64,); getitem_1 = None new_zeros = x.new_zeros(add); add = None getattr_3 = x.shape getitem_2 = getattr_3[slice(None, -1, None)]; getattr_3 = None add_1 = getitem_2 + (64,); getitem_2 = None new_zeros_1 = x.new_zeros(add_1); add_1 = None getattr_4 = x.shape getitem_3 = getattr_4[slice(None, -1, None)]; getattr_4 = None add_2 = getitem_3 + (192,); getitem_3 = None new_zeros_2 = x.new_zeros(add_2); add_2 = None narrow = new_zeros.narrow(-1, 0, 32) narrow_1 = x.narrow(-1, 32, 32) copy_ = narrow.copy_(narrow_1); narrow = narrow_1 = None narrow_2 = new_zeros.narrow(-1, 32, 32) narrow_3 = x.narrow(-1, 0, 32) copy__1 = narrow_2.copy_(narrow_3); narrow_2 = narrow_3 = None narrow_4 = new_zeros_1.narrow(-1, 0, 64) narrow_5 = x.narrow(-1, 64, 64) copy__2 = narrow_4.copy_(narrow_5); narrow_4 = narrow_5 = None narrow_6 = new_zeros_2.narrow(-1, 0, 96) narrow_7 = x.narrow(-1, 224, 96) copy__3 = narrow_6.copy_(narrow_7); narrow_6 = narrow_7 = None narrow_8 = new_zeros_2.narrow(-1, 96, 96) narrow_9 = x.narrow(-1, 128, 96); x = None copy__4 = narrow_8.copy_(narrow_9); narrow_8 = narrow_9 = None return (new_zeros, new_zeros_1, new_zeros_2) rkh^Nubrlubrmubrnhzc__torch__.e3nn.nn._activation.___torch_mangle_84 Activation ro)}(hhNh|c__torch__.torch.nn.modules.container.___torch_mangle_83 ModuleList rp)}(hhNh c__torch__.e3nn.math._normalize_activation.___torch_mangle_79 normalize2mom rq)}(hhNubrrhc__torch__.e3nn.math._normalize_activation.___torch_mangle_81 normalize2mom rs)}(hhNubrtubruubrvhc__torch__.e3nn.nn._activation.___torch_mangle_92 Activation rw)}(hhNh|c__torch__.torch.nn.modules.container.___torch_mangle_91 ModuleList rx)}(hhNh c__torch__.e3nn.math._normalize_activation.___torch_mangle_87 normalize2mom ry)}(hhNubrzhc__torch__.e3nn.math._normalize_activation.___torch_mangle_89 normalize2mom r{)}(hhNubr|ubr}ubr~hc__torch__.e3nn.o3._tensor_product._sub.___torch_mangle_95 ElementwiseTensorProduct r)}(hJh((hhX37rh KtQrK(Kt(Kth )RtRhPh((hhX38rh KtQrK(Kt(Kth )RtRhhNhK KKK KJhK KKK KKhK KKK KJhUh]((KKKhG?zXLK t(KKKhG?zXLK tehRhKhK@hShThhWhXKhjhc__torch__.torch.fx.graph_module.___torch_mangle_21 GraphModule r)}(hhNh\jh^Nubrhc__torch__.torch.fx.graph_module.___torch_mangle_22 tp_forward r)}(hhNh\hh^Nubrubrubrhc__torch__.nequip.nn._interaction_block.___torch_mangle_112 InteractionBlock r)}(hhNhG@,+F1hhc__torch__.e3nn.o3._linear.___torch_mangle_97 Linear r)}(hJh((hhX39rh M tQrK(M t(Kth )RtRhMh((hhX40rh KtQrK(Kt(Kth )RtRhPh((hhX41rh KtQrK(Kt(Kth )RtRhhNhShThK KKK KKK KJhK KKK KKK KJhUh]((KKK K G?Ơf;t(KKK K G?Ơf;t(KKK K G?Ơf;tehVRhWhXM hYKhZc__torch__.torch.fx.graph_module.___torch_mangle_23 GraphModule r)}(hhNh\X def forward(self, x : torch.Tensor, w : torch.Tensor, b : torch.Tensor) -> torch.Tensor: getattr_1 = x.shape getitem = getattr_1[slice(None, -1, None)]; getattr_1 = None add = getitem + (224,); getitem = None reshape = x.reshape(-1, 224); x = None getattr_2 = reshape.shape getitem_1 = getattr_2[0]; getattr_2 = None reshape_1 = w.reshape(-1, 3072); w = None narrow = reshape.narrow(-1, 0, 32) reshape_2 = narrow.reshape(getitem_1, 32, 1); narrow = None narrow_1 = reshape.narrow(-1, 32, 96) reshape_3 = narrow_1.reshape(getitem_1, 32, 3); narrow_1 = None narrow_2 = reshape.narrow(-1, 128, 96); reshape = None reshape_4 = narrow_2.reshape(getitem_1, 32, 3); narrow_2 = None narrow_3 = reshape_1.narrow(-1, 0, 1024) reshape_5 = narrow_3.reshape((32, 32)); narrow_3 = None mul = reshape_2 * 0.1767766952966369; reshape_2 = None tensordot = torch.functional.tensordot(mul, reshape_5, ([1], [0]), out = None); mul = reshape_5 = None permute = tensordot.permute([0, 2, 1]); tensordot = None reshape_6 = permute.reshape(getitem_1, 32); permute = None narrow_4 = reshape_1.narrow(-1, 1024, 1024) reshape_7 = narrow_4.reshape((32, 32)); narrow_4 = None mul_1 = reshape_3 * 0.1767766952966369; reshape_3 = None tensordot_1 = torch.functional.tensordot(mul_1, reshape_7, ([1], [0]), out = None); mul_1 = reshape_7 = None permute_1 = tensordot_1.permute([0, 2, 1]); tensordot_1 = None reshape_8 = permute_1.reshape(getitem_1, 96); permute_1 = None narrow_5 = reshape_1.narrow(-1, 2048, 1024); reshape_1 = None reshape_9 = narrow_5.reshape((32, 32)); narrow_5 = None mul_2 = reshape_4 * 0.1767766952966369; reshape_4 = None tensordot_2 = torch.functional.tensordot(mul_2, reshape_9, ([1], [0]), out = None); mul_2 = reshape_9 = None permute_2 = tensordot_2.permute([0, 2, 1]); tensordot_2 = None reshape_10 = permute_2.reshape(getitem_1, 96); permute_2 = getitem_1 = None cat = torch.cat([reshape_6, reshape_8, reshape_10], dim = -1); reshape_6 = reshape_8 = reshape_10 = None reshape_11 = cat.reshape(add); cat = add = None return reshape_11 rh^Nubrubrhc__torch__.e3nn.nn._fc.___torch_mangle_104 FullyConnectedNet r)}(hhNhh+(](KK@K@MetRhc__torch__.e3nn.nn._fc.___torch_mangle_100 _Layer r)}(hJh((hhX42rh MtQrK(KK@t(K@Kth )RtRhhNhG@ hG@PhG?hG?hhhc__torch__.e3nn.math._normalize_activation.___torch_mangle_98 normalize2mom r)}(hhNubrubrhc__torch__.e3nn.nn._fc.___torch_mangle_103 _Layer r)}(hJh((hhX43rh MtQrK(K@K@t(K@Kth )RtRhhNhG@PhG@PhG?hG?hhhc__torch__.e3nn.math._normalize_activation.___torch_mangle_101 normalize2mom r)}(hhNubrubrhh)}(hJh((hhX44rh M@tQrK(K@Mt(MKth )RtRhhNhNhG@PhG@phG?hG?hXLayer(64->256, act=None)rubrubrhc__torch__.e3nn.o3._tensor_product._tensor_product.___torch_mangle_105 TensorProduct r)}(hJh((hhX45rh KtQrK(Kt(Kth )RtRhPh((hhX46rh M@tQrK(M@t(Kth )RtRhhNhK KKK KKK KJhKKKKKJh(K KJK KKK KKK KJK KJK KJK KKK KKthUh]((KKKh׈G?K Kt(KKKh׈G?zXLK Kt(KKKh׈G?zXLK Kt(KKKh׈G?K Kt(KKKh׈G?zXLK Kt(KKKh׈G?zXLK Kt(KKKh׈G?K Kt(KKKh׈G?zXLK KtehRhKhKhShThhWhXMhXaTensorProduct(32x0e+32x1e+32x1o x 1x0e+1x1o -> 32x0o+64x0e+96x1o+64x1e | 256 paths | 256 weights)rhc__torch__.torch.fx.graph_module.___torch_mangle_24 GraphModule r)}(jDh((hhX47rh KtQrK(KKKt(K KKth )RtRhhNh\XW def forward(self, x1 : torch.Tensor, x2 : torch.Tensor, w : torch.Tensor) -> torch.Tensor: empty = torch.empty((), device = 'cpu') getattr_1 = x1.shape getitem = getattr_1[slice(None, -1, None)]; getattr_1 = None expand = empty.expand(getitem); getitem = None getattr_2 = x2.shape getitem_1 = getattr_2[slice(None, -1, None)]; getattr_2 = None expand_1 = empty.expand(getitem_1); getitem_1 = None getattr_3 = w.shape getitem_2 = getattr_3[slice(None, -1, None)]; getattr_3 = None expand_2 = empty.expand(getitem_2); empty = getitem_2 = None broadcast_tensors = torch.functional.broadcast_tensors(expand, expand_1, expand_2); expand = expand_1 = expand_2 = None getitem_3 = broadcast_tensors[0]; broadcast_tensors = None getattr_4 = getitem_3.shape; getitem_3 = None add = getattr_4 + (-1,) broadcast_to = x1.broadcast_to(add); x1 = add = None add_1 = getattr_4 + (-1,) broadcast_to_1 = x2.broadcast_to(add_1); x2 = add_1 = None add_2 = getattr_4 + (-1,) broadcast_to_2 = w.broadcast_to(add_2); w = add_2 = None add_3 = getattr_4 + (576,); getattr_4 = None reshape = broadcast_to.reshape(-1, 224); broadcast_to = None reshape_1 = broadcast_to_1.reshape(-1, 4); broadcast_to_1 = None getattr_5 = reshape.shape getitem_4 = getattr_5[0]; getattr_5 = None reshape_2 = broadcast_to_2.reshape(-1, 256); broadcast_to_2 = None getitem_5 = reshape[(slice(None, None, None), slice(0, 32, None))] reshape_3 = getitem_5.reshape(getitem_4, 32, 1); getitem_5 = None getitem_6 = reshape[(slice(None, None, None), slice(32, 128, None))] reshape_4 = getitem_6.reshape(getitem_4, 32, 3); getitem_6 = None getitem_7 = reshape[(slice(None, None, None), slice(128, 224, None))]; reshape = None reshape_5 = getitem_7.reshape(getitem_4, 32, 3); getitem_7 = None getitem_8 = reshape_1[(slice(None, None, None), slice(0, 1, None))] reshape_6 = getitem_8.reshape(getitem_4, 1, 1); getitem_8 = None getitem_9 = reshape_1[(slice(None, None, None), slice(1, 4, None))]; reshape_1 = None reshape_7 = getitem_9.reshape(getitem_4, 1, 3); getitem_9 = None getitem_10 = reshape_2[(slice(None, None, None), slice(0, 32, None))] reshape_8 = getitem_10.reshape((-1, 32, 1)); getitem_10 = None einsum = torch.functional.einsum('edb,eca->ecdab', reshape_6, reshape_3) reshape_9 = reshape_3.reshape(getitem_4, 32) reshape_10 = reshape_6.reshape(getitem_4, 1) einsum_1 = torch.functional.einsum('ca,cab->cab', reshape_9, reshape_8); reshape_9 = reshape_8 = None einsum_2 = torch.functional.einsum('cab,cb->ca', einsum_1, reshape_10); einsum_1 = reshape_10 = None reshape_11 = einsum_2.reshape(getitem_4, 32); einsum_2 = None getitem_11 = reshape_2[(slice(None, None, None), slice(32, 64, None))] reshape_12 = getitem_11.reshape((-1, 32, 1)); getitem_11 = None einsum_3 = torch.functional.einsum('edb,eca->ecdab', reshape_7, reshape_3) reshape_13 = reshape_3.reshape(getitem_4, 32); reshape_3 = None einsum_4 = torch.functional.einsum('ca,cab->cab', reshape_13, reshape_12); reshape_13 = reshape_12 = None einsum_5 = torch.functional.einsum('dbc,dca->dba', einsum_4, reshape_7); einsum_4 = None reshape_14 = einsum_5.reshape(getitem_4, 96); einsum_5 = None getitem_12 = reshape_2[(slice(None, None, None), slice(64, 96, None))] reshape_15 = getitem_12.reshape((-1, 32, 1)); getitem_12 = None einsum_6 = torch.functional.einsum('edb,eca->ecdab', reshape_6, reshape_4) reshape_16 = reshape_6.reshape(getitem_4, 1) einsum_7 = torch.functional.einsum('cb,cab->ca', reshape_16, reshape_15); reshape_16 = reshape_15 = None einsum_8 = torch.functional.einsum('cb,cba->cba', einsum_7, reshape_4); einsum_7 = None reshape_17 = einsum_8.reshape(getitem_4, 96); einsum_8 = None getitem_13 = reshape_2[(slice(None, None, None), slice(96, 128, None))] reshape_18 = getitem_13.reshape((-1, 32, 1)); getitem_13 = None mul = reshape_7 * 0.5773502691896258 einsum_9 = torch.functional.einsum('dca,dba->dcb', mul, reshape_4); mul = None einsum_10 = torch.functional.einsum('cba,cab->ca', einsum_9, reshape_18); einsum_9 = reshape_18 = None reshape_19 = einsum_10.reshape(getitem_4, 32); einsum_10 = None getitem_14 = reshape_2[(slice(None, None, None), slice(128, 160, None))] reshape_20 = getitem_14.reshape((-1, 32, 1)); getitem_14 = None _w3j_1_1_1 = self._w3j_1_1_1 einsum_11 = torch.functional.einsum('dba,dbc->dbac', reshape_4, reshape_20); reshape_4 = reshape_20 = None mul_1 = reshape_7 * 1.7320508075688772 tensordot = torch.functional.tensordot(mul_1, _w3j_1_1_1, ([2], [1]), out = None); mul_1 = _w3j_1_1_1 = None einsum_12 = torch.functional.einsum('edab,ecad->ecb', tensordot, einsum_11); tensordot = einsum_11 = None reshape_21 = einsum_12.reshape(getitem_4, 96); einsum_12 = None getitem_15 = reshape_2[(slice(None, None, None), slice(160, 192, None))] reshape_22 = getitem_15.reshape((-1, 32, 1)); getitem_15 = None einsum_13 = torch.functional.einsum('edb,eca->ecdab', reshape_6, reshape_5) reshape_23 = reshape_6.reshape(getitem_4, 1); reshape_6 = None einsum_14 = torch.functional.einsum('cb,cab->ca', reshape_23, reshape_22); reshape_23 = reshape_22 = None einsum_15 = torch.functional.einsum('cb,cba->cba', einsum_14, reshape_5); einsum_14 = None reshape_24 = einsum_15.reshape(getitem_4, 96); einsum_15 = None getitem_16 = reshape_2[(slice(None, None, None), slice(192, 224, None))] reshape_25 = getitem_16.reshape((-1, 32, 1)); getitem_16 = None mul_2 = reshape_7 * 0.5773502691896258 einsum_16 = torch.functional.einsum('dca,dba->dcb', mul_2, reshape_5); mul_2 = None einsum_17 = torch.functional.einsum('cba,cab->ca', einsum_16, reshape_25); einsum_16 = reshape_25 = None reshape_26 = einsum_17.reshape(getitem_4, 32); einsum_17 = None getitem_17 = reshape_2[(slice(None, None, None), slice(224, 256, None))]; reshape_2 = None reshape_27 = getitem_17.reshape((-1, 32, 1)); getitem_17 = None _w3j_1_1_3 = self._w3j_1_1_1 einsum_18 = torch.functional.einsum('dba,dbc->dbac', reshape_5, reshape_27); reshape_5 = reshape_27 = None mul_3 = reshape_7 * 1.7320508075688772; reshape_7 = None tensordot_1 = torch.functional.tensordot(mul_3, _w3j_1_1_3, ([2], [1]), out = None); mul_3 = _w3j_1_1_3 = None einsum_19 = torch.functional.einsum('edab,ecad->ecb', tensordot_1, einsum_18); tensordot_1 = einsum_18 = None reshape_28 = einsum_19.reshape(getitem_4, 96); einsum_19 = getitem_4 = None cat = torch.cat([reshape_19, reshape_11, reshape_26, reshape_14, reshape_21, reshape_24, reshape_17, reshape_28], dim = 1); reshape_19 = reshape_11 = reshape_26 = reshape_14 = reshape_21 = reshape_24 = reshape_17 = reshape_28 = None reshape_29 = cat.reshape(add_3); cat = add_3 = None return reshape_29 rh^Nubrhc__torch__.torch.fx.graph_module.___torch_mangle_25 tp_forward r)}(hhNh\hh^Nubrubrhc__torch__.e3nn.o3._linear.___torch_mangle_106 Linear r)}(hJh((hhX48rh M0tQrK(M0t(Kth )RtRhMh((hhX49rh KtQrK(Kt(Kth )RtRhPh((hhX50rh M@tQrK(M@t(Kth )RtRhhNhShTh(K KJK@KKK`KJK@KKth(K KJK`KKK KJK KKthUh]((KKK K G?Ơf;t(KKK@K`G?t(KKK`K G? p ,>t(KKK@K G?tehVRhWhXM0hYKhZc__torch__.torch.fx.graph_module.___torch_mangle_26 GraphModule r)}(hhNh\X def forward(self, x : torch.Tensor, w : torch.Tensor, b : torch.Tensor) -> torch.Tensor: getattr_1 = x.shape getitem = getattr_1[slice(None, -1, None)]; getattr_1 = None add = getitem + (320,); getitem = None reshape = x.reshape(-1, 576); x = None getattr_2 = reshape.shape getitem_1 = getattr_2[0]; getattr_2 = None reshape_1 = w.reshape(-1, 12288); w = None narrow = reshape.narrow(-1, 0, 32) reshape_2 = narrow.reshape(getitem_1, 32, 1); narrow = None narrow_1 = reshape.narrow(-1, 32, 64) reshape_3 = narrow_1.reshape(getitem_1, 64, 1); narrow_1 = None narrow_2 = reshape.narrow(-1, 96, 288) reshape_4 = narrow_2.reshape(getitem_1, 96, 3); narrow_2 = None narrow_3 = reshape.narrow(-1, 384, 192); reshape = None reshape_5 = narrow_3.reshape(getitem_1, 64, 3); narrow_3 = None narrow_4 = reshape_1.narrow(-1, 0, 1024) reshape_6 = narrow_4.reshape((32, 32)); narrow_4 = None mul = reshape_2 * 0.1767766952966369; reshape_2 = None tensordot = torch.functional.tensordot(mul, reshape_6, ([1], [0]), out = None); mul = reshape_6 = None permute = tensordot.permute([0, 2, 1]); tensordot = None reshape_7 = permute.reshape(getitem_1, 32); permute = None narrow_5 = reshape_1.narrow(-1, 1024, 6144) reshape_8 = narrow_5.reshape((64, 96)); narrow_5 = None mul_1 = reshape_3 * 0.125; reshape_3 = None tensordot_1 = torch.functional.tensordot(mul_1, reshape_8, ([1], [0]), out = None); mul_1 = reshape_8 = None permute_1 = tensordot_1.permute([0, 2, 1]); tensordot_1 = None reshape_9 = permute_1.reshape(getitem_1, 96); permute_1 = None narrow_6 = reshape_1.narrow(-1, 7168, 3072) reshape_10 = narrow_6.reshape((96, 32)); narrow_6 = None tensordot_2 = torch.functional.tensordot(reshape_4, reshape_10, ([1], [0]), out = None); reshape_4 = reshape_10 = None mul_2 = tensordot_2 * 0.10206207261596575; tensordot_2 = None permute_2 = mul_2.permute([0, 2, 1]); mul_2 = None reshape_11 = permute_2.reshape(getitem_1, 96); permute_2 = None narrow_7 = reshape_1.narrow(-1, 10240, 2048); reshape_1 = None reshape_12 = narrow_7.reshape((64, 32)); narrow_7 = None tensordot_3 = torch.functional.tensordot(reshape_5, reshape_12, ([1], [0]), out = None); reshape_5 = reshape_12 = None mul_3 = tensordot_3 * 0.125; tensordot_3 = None permute_3 = mul_3.permute([0, 2, 1]); mul_3 = None reshape_13 = permute_3.reshape(getitem_1, 96); permute_3 = getitem_1 = None cat = torch.cat([reshape_7, reshape_9, reshape_11, reshape_13], dim = -1); reshape_7 = reshape_9 = reshape_11 = reshape_13 = None reshape_14 = cat.reshape(add); cat = add = None return reshape_14 rh^Nubrubrhnc__torch__.e3nn.o3._tensor_product._sub.___torch_mangle_107 FullyConnectedTensorProduct r)}(hJh((hhX51rh MtQrK(Mt(Kth )RtRhPh((hhX52rh M@tQrK(M@t(Kth )RtRhhNhK KKK KKK KJhKKKh(K KJK`KKK KJK KKthUh]((KKKhG?Ơf;K KK`t(KKKhG?Ә !.K KK t(KKKhG?Ә !.K KK tehRhKhKhShThhWhXMhXlFullyConnectedTensorProduct(32x0e+32x1e+32x1o x 1x0e -> 32x0o+96x0e+32x1o+32x1e | 5120 paths | 5120 weights)rhc__torch__.torch.fx.graph_module.___torch_mangle_27 GraphModule r)}(hhNh\X def forward(self, x1 : torch.Tensor, x2 : torch.Tensor, w : torch.Tensor) -> torch.Tensor: empty = torch.empty((), device = 'cpu') getattr_1 = x1.shape getitem = getattr_1[slice(None, -1, None)]; getattr_1 = None expand = empty.expand(getitem); getitem = None getattr_2 = x2.shape getitem_1 = getattr_2[slice(None, -1, None)]; getattr_2 = None expand_1 = empty.expand(getitem_1); empty = getitem_1 = None broadcast_tensors = torch.functional.broadcast_tensors(expand, expand_1); expand = expand_1 = None getitem_2 = broadcast_tensors[0]; broadcast_tensors = None getattr_3 = getitem_2.shape; getitem_2 = None add = getattr_3 + (-1,) broadcast_to = x1.broadcast_to(add); x1 = add = None add_1 = getattr_3 + (-1,) broadcast_to_1 = x2.broadcast_to(add_1); x2 = add_1 = None add_2 = getattr_3 + (320,); getattr_3 = None reshape = broadcast_to.reshape(-1, 224); broadcast_to = None reshape_1 = broadcast_to_1.reshape(-1, 1); broadcast_to_1 = None getattr_4 = reshape.shape getitem_3 = getattr_4[0]; getattr_4 = None reshape_2 = w.reshape(-1, 5120); w = None getitem_4 = reshape[(slice(None, None, None), slice(0, 32, None))] reshape_3 = getitem_4.reshape(getitem_3, 32, 1); getitem_4 = None getitem_5 = reshape[(slice(None, None, None), slice(32, 128, None))] reshape_4 = getitem_5.reshape(getitem_3, 32, 3); getitem_5 = None getitem_6 = reshape[(slice(None, None, None), slice(128, 224, None))] reshape_5 = getitem_6.reshape(getitem_3, 32, 3); getitem_6 = None reshape_6 = reshape_1.reshape(getitem_3, 1, 1); reshape_1 = None getitem_7 = reshape_2[(slice(None, None, None), slice(0, 3072, None))] reshape_7 = getitem_7.reshape((32, 1, 96)); getitem_7 = None einsum = torch.functional.einsum('edb,eca->ecdab', reshape_6, reshape_3) reshape_8 = reshape_3.reshape(getitem_3, 32); reshape_3 = None reshape_9 = reshape_6.reshape(getitem_3, 1) mul = reshape_9 * 0.1767766952966369; reshape_9 = None einsum_1 = torch.functional.einsum('cb,ca->cba', mul, reshape_8); mul = reshape_8 = None tensordot = torch.functional.tensordot(einsum_1, reshape_7, ([1, 2], [1, 0]), out = None); einsum_1 = reshape_7 = None reshape_10 = tensordot.reshape(getitem_3, 96); tensordot = None getitem_8 = reshape_2[(slice(None, None, None), slice(3072, 4096, None))] reshape_11 = getitem_8.reshape((32, 1, 32)); getitem_8 = None einsum_2 = torch.functional.einsum('edb,eca->ecdab', reshape_6, reshape_4) reshape_12 = reshape_6.reshape(getitem_3, 1) mul_1 = reshape_12 * 0.1767766952966369; reshape_12 = None einsum_3 = torch.functional.einsum('dc,dba->dcba', mul_1, reshape_4); mul_1 = reshape_4 = None tensordot_1 = torch.functional.tensordot(einsum_3, reshape_11, ([1, 2], [1, 0]), out = None); einsum_3 = reshape_11 = None permute = tensordot_1.permute([0, 2, 1]); tensordot_1 = None reshape_13 = permute.reshape(getitem_3, 96); permute = None getitem_9 = reshape_2[(slice(None, None, None), slice(4096, 5120, None))]; reshape_2 = None reshape_14 = getitem_9.reshape((32, 1, 32)); getitem_9 = None einsum_4 = torch.functional.einsum('edb,eca->ecdab', reshape_6, reshape_5) reshape_15 = reshape_6.reshape(getitem_3, 1); reshape_6 = None mul_2 = reshape_15 * 0.1767766952966369; reshape_15 = None einsum_5 = torch.functional.einsum('dc,dba->dcba', mul_2, reshape_5); mul_2 = reshape_5 = None tensordot_2 = torch.functional.tensordot(einsum_5, reshape_14, ([1, 2], [1, 0]), out = None); einsum_5 = reshape_14 = None permute_1 = tensordot_2.permute([0, 2, 1]); tensordot_2 = None reshape_16 = permute_1.reshape(getitem_3, 96); permute_1 = None new_zeros = reshape.new_zeros((getitem_3, 32)); reshape = getitem_3 = None cat = torch.cat([new_zeros, reshape_10, reshape_16, reshape_13], dim = 1); new_zeros = reshape_10 = reshape_16 = reshape_13 = None reshape_17 = cat.reshape(add_2); cat = add_2 = None return reshape_17 rh^Nubrhc__torch__.torch.fx.graph_module.___torch_mangle_28 tp_forward r)}(hhNh\hh^NubrubrubrubrXconv_to_output_hiddenrc__torch__.nequip.nn._atomwise.___torch_mangle_114 AtomwiseLinear r)}(hhNhGhh"hhHc__torch__.e3nn.o3._linear.___torch_mangle_108 Linear r)}(hJh((hhX53rh MtQrK(Mt(Kth )RtRhMh((hhX54rh KtQrK(Kt(Kth )RtRhPh((hhX55rh KtQrK(Kt(Kth )RtRhhNhShTh(K KKK KJK KKK KJthKKKhUh]((KKK KG?Ơf;tehVRhWhXMhYKhZc__torch__.torch.fx.graph_module.___torch_mangle_29 GraphModule r)}(hhNh\X def forward(self, x : torch.Tensor, w : torch.Tensor, b : torch.Tensor) -> torch.Tensor: getattr_1 = x.shape getitem = getattr_1[slice(None, -1, None)]; getattr_1 = None add = getitem + (16,); getitem = None reshape = x.reshape(-1, 256); x = None getattr_2 = reshape.shape getitem_1 = getattr_2[0]; getattr_2 = None reshape_1 = w.reshape(-1, 512); w = None narrow = reshape.narrow(-1, 0, 32) reshape_2 = narrow.reshape(getitem_1, 32, 1); narrow = None narrow_1 = reshape.narrow(-1, 32, 32) reshape_3 = narrow_1.reshape(getitem_1, 32, 1); narrow_1 = None narrow_2 = reshape.narrow(-1, 64, 96) reshape_4 = narrow_2.reshape(getitem_1, 32, 3); narrow_2 = None narrow_3 = reshape.narrow(-1, 160, 96); reshape = None reshape_5 = narrow_3.reshape(getitem_1, 32, 3); narrow_3 = None reshape_6 = reshape_1.reshape((32, 16)); reshape_1 = None tensordot = torch.functional.tensordot(reshape_2, reshape_6, ([1], [0]), out = None); reshape_2 = reshape_6 = None mul = tensordot * 0.1767766952966369; tensordot = None permute = mul.permute([0, 2, 1]); mul = None reshape_7 = permute.reshape(getitem_1, 16); permute = getitem_1 = None reshape_8 = reshape_7.reshape(add); reshape_7 = add = None return reshape_8 rh^NubrubrubrXoutput_hidden_to_scalarrc__torch__.nequip.nn._atomwise.___torch_mangle_115 AtomwiseLinear r)}(hhNhGhh"X atomic_energyrhHc__torch__.e3nn.o3._linear.___torch_mangle_109 Linear r)}(hJh((hhX56rh KtQrK(Kt(Kth )RtRhMh((hhX57rh KtQrK(Kt(Kth )RtRhPh((hhX58rh KtQrK(Kt(Kth )RtRhhNhShThKKKhKKKhUh]((KKKKG?tehVRhWhXKhYKhZc__torch__.torch.fx.graph_module.___torch_mangle_30 GraphModule r)}(hhNh\X def forward(self, x : torch.Tensor, w : torch.Tensor, b : torch.Tensor) -> torch.Tensor: getattr_1 = x.shape getitem = getattr_1[slice(None, -1, None)]; getattr_1 = None add = getitem + (1,); getitem = None reshape = x.reshape(-1, 16); x = None getattr_2 = reshape.shape getitem_1 = getattr_2[0]; getattr_2 = None reshape_1 = w.reshape(-1, 16); w = None reshape_2 = reshape.reshape(getitem_1, 16, 1); reshape = None reshape_3 = reshape_1.reshape((16, 1)); reshape_1 = None tensordot = torch.functional.tensordot(reshape_2, reshape_3, ([1], [0]), out = None); reshape_2 = reshape_3 = None mul = tensordot * 0.25; tensordot = None permute = mul.permute([0, 2, 1]); mul = None reshape_4 = permute.reshape(getitem_1, 1); permute = getitem_1 = None reshape_5 = reshape_4.reshape(add); reshape_4 = add = None return reshape_5 rh^NubrubrubrXper_species_rescalerc__torch__.nequip.nn._atomwise PerSpeciesScaleShift r)}(Xshiftsrh((hhX59rh KtQrK(Kt(Kth )RtRXscalesrh((hhX60rh KtQrK(Kt(Kth )RtRhhNhKX type_namesrh](XSireX List[str]rRhGjh"jX has_shiftsrXshifts_trainablerX has_scalesrXscales_trainablerXarguments_in_dataset_unitsrubrXtotal_energy_sumrc__torch__.nequip.nn._atomwise AtomwiseReduce r)}(hhNXconstantrG?XreducerXsumrhGjh"X total_energyrubrubrubrubr.PK`  PK*_neg_NEQUIP_L1_4A_GAP_Si/code/__torch__.pyFBZZZZZZZZZZZZZZZZZZZZZZZZZZZTKk@W,>I GCzKieًW>_}IJrfoEIgk9;0"Pctm\F&85>iZqMQ<;go}>`ljNqZJdxoh,p]*0.-`y XN/bM,9=ڞ>&pga+,= p=͖ F;$޼r-RŴb^y"5PgO}uQg2Ea%;Y,ЃB.{źrGͥp" q. 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