class <lambda>(torch.nn.Module):
    def forward(self, arg0_1: "f32[256]", arg1_1: "f32[256]", arg2_1: "Sym(s0)", arg3_1: "f32[s0, 1, 256]", arg4_1: "f32[s0, 1, 256]", arg5_1: "Sym(s4)", arg6_1: "Sym(s5)", arg7_1: "f32[256, 256]", arg8_1: "f32[256]", arg9_1: "f32[256, 256]", arg10_1: "f32[256]", arg11_1: "f32[256, 256]", arg12_1: "f32[256]", arg13_1: "Sym(s6)", arg14_1: "f32[256, 256]", arg15_1: "f32[256]", arg16_1: "f32[256]", arg17_1: "f32[256]", arg18_1: "f32[512, 256]", arg19_1: "f32[512]", arg20_1: "Sym(s7)", arg21_1: "Sym(s8)", arg22_1: "Sym(s10)", arg23_1: "f16[s10, 1, 256]", arg24_1: "f16[s10, 1, 256]", arg25_1: "f32[256, 256]", arg26_1: "f32[256]"):
        # File: /opt/conda/lib/python3.11/site-packages/torch/nn/modules/normalization.py:201 in forward, code: return F.layer_norm(
        var_mean = torch.ops.aten.var_mean.correction(arg3_1, [2], correction = 0, keepdim = True)
        getitem: "f32[s0, 1, 1]" = var_mean[0]
        getitem_1: "f32[s0, 1, 1]" = var_mean[1];  var_mean = None
        add: "f32[s0, 1, 1]" = torch.ops.aten.add.Tensor(getitem, 1e-05);  getitem = None
        rsqrt: "f32[s0, 1, 1]" = torch.ops.aten.rsqrt.default(add);  add = None
        sub: "f32[s0, 1, 256]" = torch.ops.aten.sub.Tensor(arg3_1, getitem_1);  getitem_1 = None
        mul: "f32[s0, 1, 256]" = torch.ops.aten.mul.Tensor(sub, rsqrt);  sub = rsqrt = None
        mul_1: "f32[s0, 1, 256]" = torch.ops.aten.mul.Tensor(mul, arg0_1);  mul = arg0_1 = None
        add_1: "f32[s0, 1, 256]" = torch.ops.aten.add.Tensor(mul_1, arg1_1);  mul_1 = arg1_1 = None
        
        # File: /workspace/networks/layers/transformer.py:752 in with_pos_embed, code: return tensor if pos is None else tensor + pos
        add_2: "f32[s0, 1, 256]" = torch.ops.aten.add.Tensor(add_1, arg4_1);  arg4_1 = None
        
        # File: /opt/conda/lib/python3.11/site-packages/torch/nn/modules/linear.py:116 in forward, code: return F.linear(input, self.weight, self.bias)
        convert_element_type: "f16[256]" = torch.ops.prims.convert_element_type.default(arg8_1, torch.float16);  arg8_1 = None
        convert_element_type_1: "f16[256, 256]" = torch.ops.prims.convert_element_type.default(arg7_1, torch.float16);  arg7_1 = None
        convert_element_type_2: "f16[s0, 1, 256]" = torch.ops.prims.convert_element_type.default(add_2, torch.float16)
        view: "f16[s0, 256]" = torch.ops.aten.view.default(convert_element_type_2, [arg2_1, 256]);  convert_element_type_2 = None
        permute: "f16[256, 256]" = torch.ops.aten.permute.default(convert_element_type_1, [1, 0]);  convert_element_type_1 = None
        addmm: "f16[s0, 256]" = torch.ops.aten.addmm.default(convert_element_type, view, permute);  convert_element_type = view = permute = None
        view_1: "f16[s0, 1, 256]" = torch.ops.aten.view.default(addmm, [arg2_1, 1, 256]);  addmm = None
        
        # File: /opt/conda/lib/python3.11/site-packages/torch/nn/modules/linear.py:116 in forward, code: return F.linear(input, self.weight, self.bias)
        convert_element_type_6: "f16[256]" = torch.ops.prims.convert_element_type.default(arg10_1, torch.float16);  arg10_1 = None
        convert_element_type_7: "f16[256, 256]" = torch.ops.prims.convert_element_type.default(arg9_1, torch.float16);  arg9_1 = None
        convert_element_type_8: "f16[s0, 1, 256]" = torch.ops.prims.convert_element_type.default(add_2, torch.float16);  add_2 = None
        view_2: "f16[s0, 256]" = torch.ops.aten.view.default(convert_element_type_8, [arg2_1, 256]);  convert_element_type_8 = None
        permute_1: "f16[256, 256]" = torch.ops.aten.permute.default(convert_element_type_7, [1, 0]);  convert_element_type_7 = None
        addmm_1: "f16[s0, 256]" = torch.ops.aten.addmm.default(convert_element_type_6, view_2, permute_1);  convert_element_type_6 = view_2 = permute_1 = None
        view_3: "f16[s0, 1, 256]" = torch.ops.aten.view.default(addmm_1, [arg2_1, 1, 256]);  addmm_1 = None
        
        # File: /opt/conda/lib/python3.11/site-packages/torch/nn/modules/linear.py:116 in forward, code: return F.linear(input, self.weight, self.bias)
        convert_element_type_12: "f16[256]" = torch.ops.prims.convert_element_type.default(arg12_1, torch.float16);  arg12_1 = None
        convert_element_type_13: "f16[256, 256]" = torch.ops.prims.convert_element_type.default(arg11_1, torch.float16);  arg11_1 = None
        convert_element_type_14: "f16[s0, 1, 256]" = torch.ops.prims.convert_element_type.default(add_1, torch.float16);  add_1 = None
        view_4: "f16[s0, 256]" = torch.ops.aten.view.default(convert_element_type_14, [arg2_1, 256]);  convert_element_type_14 = None
        permute_2: "f16[256, 256]" = torch.ops.aten.permute.default(convert_element_type_13, [1, 0]);  convert_element_type_13 = None
        addmm_2: "f16[s0, 256]" = torch.ops.aten.addmm.default(convert_element_type_12, view_4, permute_2);  convert_element_type_12 = view_4 = permute_2 = None
        view_5: "f16[s0, 1, 256]" = torch.ops.aten.view.default(addmm_2, [arg2_1, 1, 256]);  addmm_2 = None
        
        # File: /workspace/networks/layers/attention.py:80 in forward, code: Q = Q / self.T
        div: "f16[s0, 1, 256]" = torch.ops.aten.div.Tensor(view_1, 5.656854249492381);  view_1 = None
        
        # File: /workspace/networks/layers/attention.py:90 in forward, code: Q = Q.view(-1, bs, num_head, self.d_att).permute(1, 2, 0, 3)
        view_6: "f16[s0, 1, s4, (256//s4)]" = torch.ops.aten.view.default(div, [-1, 1, arg5_1, arg13_1]);  div = None
        permute_3: "f16[1, s4, s0, (256//s4)]" = torch.ops.aten.permute.default(view_6, [1, 2, 0, 3])
        
        # File: /workspace/networks/layers/attention.py:91 in forward, code: K = K.view(-1, bs, num_head, self.d_att).permute(1, 2, 3, 0)
        view_7: "f16[s0, 1, s4, (256//s4)]" = torch.ops.aten.view.default(view_3, [-1, 1, arg5_1, arg13_1]);  view_3 = arg13_1 = None
        permute_4: "f16[1, s4, (256//s4), s0]" = torch.ops.aten.permute.default(view_7, [1, 2, 3, 0])
        
        # File: /workspace/networks/layers/attention.py:92 in forward, code: V = V.view(-1, bs, num_head, hidden_dim).permute(1, 2, 0, 3)
        view_8: "f16[s0, 1, s4, (256//s4)]" = torch.ops.aten.view.default(view_5, [-1, 1, arg5_1, arg6_1]);  view_5 = arg6_1 = None
        permute_5: "f16[1, s4, s0, (256//s4)]" = torch.ops.aten.permute.default(view_8, [1, 2, 0, 3])
        
        # File: /workspace/networks/layers/attention.py:8 in multiply_by_ychunks, code: return x @ y
        sym_size_int_1: "Sym((256//s4))" = torch.ops.aten.sym_size.int(view_6, 3);  view_6 = None
        expand: "f16[1, s4, s0, (256//s4)]" = torch.ops.aten.expand.default(permute_3, [1, arg5_1, arg2_1, sym_size_int_1]);  permute_3 = None
        view_9: "f16[s4, s0, (256//s4)]" = torch.ops.aten.view.default(expand, [arg5_1, arg2_1, sym_size_int_1]);  expand = sym_size_int_1 = None
        sym_size_int_2: "Sym((256//s4))" = torch.ops.aten.sym_size.int(view_7, 3);  view_7 = None
        expand_1: "f16[1, s4, (256//s4), s0]" = torch.ops.aten.expand.default(permute_4, [1, arg5_1, sym_size_int_2, arg2_1]);  permute_4 = None
        view_10: "f16[s4, (256//s4), s0]" = torch.ops.aten.view.default(expand_1, [arg5_1, sym_size_int_2, arg2_1]);  expand_1 = sym_size_int_2 = None
        bmm: "f16[s4, s0, s0]" = torch.ops.aten.bmm.default(view_9, view_10);  view_9 = view_10 = None
        view_11: "f16[1, s4, s0, s0]" = torch.ops.aten.view.default(bmm, [1, arg5_1, arg2_1, arg2_1]);  bmm = None
        
        # File: /workspace/networks/layers/attention.py:114 in forward, code: attn = torch.softmax(QK, dim=-1)
        convert_element_type_20: "f32[1, s4, s0, s0]" = torch.ops.prims.convert_element_type.default(view_11, torch.float32);  view_11 = None
        amax: "f32[1, s4, s0, 1]" = torch.ops.aten.amax.default(convert_element_type_20, [-1], True)
        sub_1: "f32[1, s4, s0, s0]" = torch.ops.aten.sub.Tensor(convert_element_type_20, amax);  convert_element_type_20 = amax = None
        exp: "f32[1, s4, s0, s0]" = torch.ops.aten.exp.default(sub_1);  sub_1 = None
        sum_1: "f32[1, s4, s0, 1]" = torch.ops.aten.sum.dim_IntList(exp, [-1], True)
        div_1: "f32[1, s4, s0, s0]" = torch.ops.aten.div.Tensor(exp, sum_1);  exp = sum_1 = None
        
        # File: /opt/conda/lib/python3.11/site-packages/torch/nn/modules/dropout.py:59 in forward, code: return F.dropout(input, self.p, self.training, self.inplace)
        clone: "f32[1, s4, s0, s0]" = torch.ops.aten.clone.default(div_1);  div_1 = None
        
        # File: /workspace/networks/layers/attention.py:14 in multiply_by_xchunks, code: return x @ y
        convert_element_type_21: "f16[1, s4, s0, s0]" = torch.ops.prims.convert_element_type.default(clone, torch.float16);  clone = None
        expand_2: "f16[1, s4, s0, s0]" = torch.ops.aten.expand.default(convert_element_type_21, [1, arg5_1, arg2_1, arg2_1]);  convert_element_type_21 = None
        view_12: "f16[s4, s0, s0]" = torch.ops.aten.view.default(expand_2, [arg5_1, arg2_1, arg2_1]);  expand_2 = None
        sym_size_int_4: "Sym((256//s4))" = torch.ops.aten.sym_size.int(view_8, 3);  view_8 = None
        expand_3: "f16[1, s4, s0, (256//s4)]" = torch.ops.aten.expand.default(permute_5, [1, arg5_1, arg2_1, sym_size_int_4]);  permute_5 = None
        view_13: "f16[s4, s0, (256//s4)]" = torch.ops.aten.view.default(expand_3, [arg5_1, arg2_1, sym_size_int_4]);  expand_3 = None
        bmm_1: "f16[s4, s0, (256//s4)]" = torch.ops.aten.bmm.default(view_12, view_13);  view_12 = view_13 = None
        view_14: "f16[1, s4, s0, (256//s4)]" = torch.ops.aten.view.default(bmm_1, [1, arg5_1, arg2_1, sym_size_int_4]);  bmm_1 = None
        
        # File: /workspace/networks/layers/attention.py:120 in forward, code: self.qk_chunks).permute(2, 0, 1, 3)
        permute_6: "f16[s0, 1, s4, (256//s4)]" = torch.ops.aten.permute.default(view_14, [2, 0, 1, 3]);  view_14 = None
        
        # File: /workspace/networks/layers/attention.py:122 in forward, code: outputs = outputs.reshape(-1, bs, self.d_model)
        clone_1: "f16[s0, 1, s4, (256//s4)]" = torch.ops.aten.clone.default(permute_6, memory_format = torch.contiguous_format);  permute_6 = None
        mul_2: "Sym(s0*s4)" = arg2_1 * arg5_1;  arg5_1 = None
        mul_3: "Sym(s0*s4*((256//s4)))" = mul_2 * sym_size_int_4;  mul_2 = sym_size_int_4 = None
        floordiv: "Sym(((s0*s4*((256//s4)))//256))" = mul_3 // 256;  mul_3 = None
        view_15: "f16[s0, 1, s4*((256//s4))]" = torch.ops.aten.view.default(clone_1, [floordiv, 1, 256]);  clone_1 = floordiv = None
        
        # File: /opt/conda/lib/python3.11/site-packages/torch/nn/modules/linear.py:116 in forward, code: return F.linear(input, self.weight, self.bias)
        convert_element_type_24: "f16[256]" = torch.ops.prims.convert_element_type.default(arg15_1, torch.float16);  arg15_1 = None
        convert_element_type_25: "f16[256, 256]" = torch.ops.prims.convert_element_type.default(arg14_1, torch.float16);  arg14_1 = None
        sym_size_int_6: "Sym(s4*((256//s4)))" = torch.ops.aten.sym_size.int(view_15, 2)
        view_16: "f16[s0, s4*((256//s4))]" = torch.ops.aten.view.default(view_15, [arg2_1, sym_size_int_6]);  view_15 = sym_size_int_6 = None
        permute_7: "f16[256, 256]" = torch.ops.aten.permute.default(convert_element_type_25, [1, 0]);  convert_element_type_25 = None
        addmm_3: "f16[s0, 256]" = torch.ops.aten.addmm.default(convert_element_type_24, view_16, permute_7);  convert_element_type_24 = view_16 = permute_7 = None
        view_17: "f16[s0, 1, 256]" = torch.ops.aten.view.default(addmm_3, [arg2_1, 1, 256]);  addmm_3 = None
        
        # File: /workspace/networks/layers/transformer.py:772 in forward, code: tgt = tgt + self.droppath(tgt2)
        add_3: "f32[s0, 1, 256]" = torch.ops.aten.add.Tensor(arg3_1, view_17);  arg3_1 = view_17 = None
        
        # File: /opt/conda/lib/python3.11/site-packages/torch/nn/modules/normalization.py:201 in forward, code: return F.layer_norm(
        var_mean_1 = torch.ops.aten.var_mean.correction(add_3, [2], correction = 0, keepdim = True)
        getitem_2: "f32[s0, 1, 1]" = var_mean_1[0]
        getitem_3: "f32[s0, 1, 1]" = var_mean_1[1];  var_mean_1 = None
        add_4: "f32[s0, 1, 1]" = torch.ops.aten.add.Tensor(getitem_2, 1e-05);  getitem_2 = None
        rsqrt_1: "f32[s0, 1, 1]" = torch.ops.aten.rsqrt.default(add_4);  add_4 = None
        sub_2: "f32[s0, 1, 256]" = torch.ops.aten.sub.Tensor(add_3, getitem_3);  getitem_3 = None
        mul_4: "f32[s0, 1, 256]" = torch.ops.aten.mul.Tensor(sub_2, rsqrt_1);  sub_2 = rsqrt_1 = None
        mul_5: "f32[s0, 1, 256]" = torch.ops.aten.mul.Tensor(mul_4, arg16_1);  mul_4 = arg16_1 = None
        add_5: "f32[s0, 1, 256]" = torch.ops.aten.add.Tensor(mul_5, arg17_1);  mul_5 = arg17_1 = None
        
        # File: /opt/conda/lib/python3.11/site-packages/torch/nn/modules/linear.py:116 in forward, code: return F.linear(input, self.weight, self.bias)
        convert_element_type_29: "f16[512]" = torch.ops.prims.convert_element_type.default(arg19_1, torch.float16);  arg19_1 = None
        convert_element_type_30: "f16[512, 256]" = torch.ops.prims.convert_element_type.default(arg18_1, torch.float16);  arg18_1 = None
        convert_element_type_31: "f16[s0, 1, 256]" = torch.ops.prims.convert_element_type.default(add_5, torch.float16)
        view_18: "f16[s0, 256]" = torch.ops.aten.view.default(convert_element_type_31, [arg2_1, 256]);  convert_element_type_31 = None
        permute_8: "f16[256, 512]" = torch.ops.aten.permute.default(convert_element_type_30, [1, 0]);  convert_element_type_30 = None
        addmm_4: "f16[s0, 512]" = torch.ops.aten.addmm.default(convert_element_type_29, view_18, permute_8);  convert_element_type_29 = view_18 = permute_8 = None
        view_19: "f16[s0, 1, 512]" = torch.ops.aten.view.default(addmm_4, [arg2_1, 1, 512]);  addmm_4 = None
        
        # File: /workspace/networks/layers/transformer.py:778 in forward, code: curr_QV = torch.split(curr_QV, self.d_model, dim=2)
        split = torch.ops.aten.split.Tensor(view_19, 256, 2);  view_19 = None
        getitem_4: "f16[s0, 1, 256]" = split[0]
        getitem_5: "f16[s0, 1, 256]" = split[1];  split = None
        
        # File: /workspace/networks/layers/basic.py:93 in seq_to_2d, code: tensor = tensor.view(h, w, n, c).permute(2, 3, 0, 1).contiguous()
        view_20: "f16[s7, (s0//s7), 1, 256]" = torch.ops.aten.view.default(getitem_4, [arg20_1, arg21_1, 1, 256]);  arg20_1 = arg21_1 = None
        permute_9: "f16[1, 256, s7, (s0//s7)]" = torch.ops.aten.permute.default(view_20, [2, 3, 0, 1]);  view_20 = None
        clone_2: "f16[1, 256, s7, (s0//s7)]" = torch.ops.aten.clone.default(permute_9, memory_format = torch.contiguous_format);  permute_9 = None
        
        # File: /workspace/networks/layers/attention.py:80 in forward, code: Q = Q / self.T
        div_2: "f16[s0, 1, 256]" = torch.ops.aten.div.Tensor(getitem_4, 16.0)
        
        # File: /workspace/networks/layers/attention.py:90 in forward, code: Q = Q.view(-1, bs, num_head, self.d_att).permute(1, 2, 0, 3)
        view_21: "f16[s0, 1, 1, 256]" = torch.ops.aten.view.default(div_2, [-1, 1, 1, 256]);  div_2 = None
        permute_10: "f16[1, 1, s0, 256]" = torch.ops.aten.permute.default(view_21, [1, 2, 0, 3]);  view_21 = None
        
        # File: /workspace/networks/layers/attention.py:91 in forward, code: K = K.view(-1, bs, num_head, self.d_att).permute(1, 2, 3, 0)
        view_22: "f16[s10, 1, 1, 256]" = torch.ops.aten.view.default(arg23_1, [-1, 1, 1, 256]);  arg23_1 = None
        permute_11: "f16[1, 1, 256, s10]" = torch.ops.aten.permute.default(view_22, [1, 2, 3, 0]);  view_22 = None
        
        # File: /workspace/networks/layers/attention.py:92 in forward, code: V = V.view(-1, bs, num_head, hidden_dim).permute(1, 2, 0, 3)
        view_23: "f16[s10, 1, 1, 256]" = torch.ops.aten.view.default(arg24_1, [-1, 1, 1, 256]);  arg24_1 = None
        permute_12: "f16[1, 1, s10, 256]" = torch.ops.aten.permute.default(view_23, [1, 2, 0, 3]);  view_23 = None
        
        # File: /workspace/networks/layers/attention.py:8 in multiply_by_ychunks, code: return x @ y
        expand_4: "f16[1, 1, s0, 256]" = torch.ops.aten.expand.default(permute_10, [1, 1, arg2_1, 256]);  permute_10 = None
        view_24: "f16[1, s0, 256]" = torch.ops.aten.view.default(expand_4, [1, arg2_1, 256]);  expand_4 = None
        expand_5: "f16[1, 1, 256, s10]" = torch.ops.aten.expand.default(permute_11, [1, 1, 256, arg22_1]);  permute_11 = None
        view_25: "f16[1, 256, s10]" = torch.ops.aten.view.default(expand_5, [1, 256, arg22_1]);  expand_5 = None
        bmm_2: "f16[1, s0, s10]" = torch.ops.aten.bmm.default(view_24, view_25);  view_24 = view_25 = None
        view_26: "f16[1, 1, s0, s10]" = torch.ops.aten.view.default(bmm_2, [1, 1, arg2_1, arg22_1]);  bmm_2 = None
        
        # File: /workspace/networks/layers/attention.py:114 in forward, code: attn = torch.softmax(QK, dim=-1)
        convert_element_type_37: "f32[1, 1, s0, s10]" = torch.ops.prims.convert_element_type.default(view_26, torch.float32);  view_26 = None
        amax_1: "f32[1, 1, s0, 1]" = torch.ops.aten.amax.default(convert_element_type_37, [-1], True)
        sub_3: "f32[1, 1, s0, s10]" = torch.ops.aten.sub.Tensor(convert_element_type_37, amax_1);  convert_element_type_37 = amax_1 = None
        exp_1: "f32[1, 1, s0, s10]" = torch.ops.aten.exp.default(sub_3);  sub_3 = None
        sum_2: "f32[1, 1, s0, 1]" = torch.ops.aten.sum.dim_IntList(exp_1, [-1], True)
        div_3: "f32[1, 1, s0, s10]" = torch.ops.aten.div.Tensor(exp_1, sum_2);  exp_1 = sum_2 = None
        
        # File: /opt/conda/lib/python3.11/site-packages/torch/nn/modules/dropout.py:59 in forward, code: return F.dropout(input, self.p, self.training, self.inplace)
        clone_3: "f32[1, 1, s0, s10]" = torch.ops.aten.clone.default(div_3);  div_3 = None
        
        # File: /workspace/networks/layers/attention.py:14 in multiply_by_xchunks, code: return x @ y
        convert_element_type_38: "f16[1, 1, s0, s10]" = torch.ops.prims.convert_element_type.default(clone_3, torch.float16);  clone_3 = None
        expand_6: "f16[1, 1, s0, s10]" = torch.ops.aten.expand.default(convert_element_type_38, [1, 1, arg2_1, arg22_1]);  convert_element_type_38 = None
        view_27: "f16[1, s0, s10]" = torch.ops.aten.view.default(expand_6, [1, arg2_1, arg22_1]);  expand_6 = None
        expand_7: "f16[1, 1, s10, 256]" = torch.ops.aten.expand.default(permute_12, [1, 1, arg22_1, 256]);  permute_12 = None
        view_28: "f16[1, s10, 256]" = torch.ops.aten.view.default(expand_7, [1, arg22_1, 256]);  expand_7 = arg22_1 = None
        bmm_3: "f16[1, s0, 256]" = torch.ops.aten.bmm.default(view_27, view_28);  view_27 = view_28 = None
        view_29: "f16[1, 1, s0, 256]" = torch.ops.aten.view.default(bmm_3, [1, 1, arg2_1, 256]);  bmm_3 = None
        
        # File: /workspace/networks/layers/attention.py:120 in forward, code: self.qk_chunks).permute(2, 0, 1, 3)
        permute_13: "f16[s0, 1, 1, 256]" = torch.ops.aten.permute.default(view_29, [2, 0, 1, 3]);  view_29 = None
        
        # File: /workspace/networks/layers/attention.py:122 in forward, code: outputs = outputs.reshape(-1, bs, self.d_model)
        view_30: "f16[s0, 1, 256]" = torch.ops.aten.view.default(permute_13, [-1, 1, 256]);  permute_13 = None
        
        # File: /opt/conda/lib/python3.11/site-packages/torch/nn/modules/linear.py:116 in forward, code: return F.linear(input, self.weight, self.bias)
        convert_element_type_41: "f16[256]" = torch.ops.prims.convert_element_type.default(arg26_1, torch.float16);  arg26_1 = None
        convert_element_type_42: "f16[256, 256]" = torch.ops.prims.convert_element_type.default(arg25_1, torch.float16);  arg25_1 = None
        view_31: "f16[s0, 256]" = torch.ops.aten.view.default(view_30, [arg2_1, 256]);  view_30 = None
        permute_14: "f16[256, 256]" = torch.ops.aten.permute.default(convert_element_type_42, [1, 0]);  convert_element_type_42 = None
        addmm_5: "f16[s0, 256]" = torch.ops.aten.addmm.default(convert_element_type_41, view_31, permute_14);  convert_element_type_41 = view_31 = permute_14 = None
        view_32: "f16[s0, 1, 256]" = torch.ops.aten.view.default(addmm_5, [arg2_1, 1, 256]);  addmm_5 = arg2_1 = None
        return (clone_2, add_3, add_5, view_32, getitem_4, getitem_5)