Upsample exports to a very big subgraph in dynamo

I'm looking at some models in MONAI which involves torch.nn.Upsample. I notice that torchscript exports the Upsample module to a Resize node but dynamo exports it to a very big graph and has a perf impact.
An example is the SegResNet  model.
torchscript:

dynamo:

expanding the dynamo subgraph:

Here's the export script:
import torch
from monai.networks.nets import SegResNet

model = lambda : SegResNet(
    blocks_down=(1, 2, 2, 4),
    blocks_up=(1, 1, 1),
    init_filters=16,
    in_channels=4,
    out_channels=3,
    dropout_prob=0.2
)
model = model().eval().to('cuda')
data = torch.randn(1,4,224,224,128).to("cuda")

dynamo_export = True
if dynamo_export:
    export_output = torch.onnx.dynamo_export(
        model,
        data,
    )
    export_output.save('Clara_SegResNet_dynamo.onnx')
else:
    torch.onnx.export(model, (data,), 'Clara_SegResNet.onnx')

Relevant versions:
onnx==1.16.0
onnxscript==0.1.0.dev20240513
torch==2.4.0.dev20240513+cu121
monai 1.3.0

I also tried to get more info on which part of dynamo/onnxscript might be responsible for this.
If I run
scripted_model = torch.jit.script(model)
print(scripted_model.graph)

I get this:
graph(%self : __torch__.monai.networks.nets.segresnet.SegResNet,
      %x.1 : Tensor):
  %3 : (Tensor, Tensor[]) = prim::CallMethod[name="encode"](%self, %x.1) # /usr/local/lib/python3.10/dist-packages/monai/networks/nets/segresnet.py:180:20
  %x.5 : Tensor, %down_x.1 : Tensor[] = prim::TupleUnpack(%3)
   = aten::reverse(%down_x.1) # /usr/local/lib/python3.10/dist-packages/monai/networks/nets/segresnet.py:181:8
  %x.9 : Tensor = prim::CallMethod[name="decode"](%self, %x.5, %down_x.1) # /usr/local/lib/python3.10/dist-packages/monai/networks/nets/segresnet.py:183:12
  return (%x.9)

If I run
gm, _ = torch._dynamo.export(model)(data)
gm = torch.fx.experimental.proxy_tensor.make_fx(torch.func.functionalize(gm))(data)
gm.print_readable()

I get an error:
Traceback (most recent call last):
  File "/ws/dynamo/0501/export_SegResNet.py", line 31, in <module>
    gm, _ = torch._dynamo.export(model)(data)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py", line 1282, in inner
    dim_constraints.solve()
  File "/usr/local/lib/python3.10/dist-packages/torch/fx/experimental/symbolic_shapes.py", line 1772, in solve
    tmp_name = f"_{self._dcp.source_name_to_debug_name[self._dcp.symbol_to_source[s][0].name()]}"
KeyError: "L['x'].size()[4]"

Thanks for catching this. Very intriguing. Will take a look!

cc @xiaowuhu @fatcat-z

@yuanyao-nv could you obtain the graph module from torch.export.export and post it here?

@justinchuby Is this what you mean?
    exported_program = torch.export.export(model, (data,))
    exported_program._graph_module.print_readable()

which gives
class GraphModule(torch.nn.Module):
    def forward(self, p_convinit_conv_weight: "f32[16, 4, 3, 3, 3]", p_getattr_l__self___down_layers_0___1___norm1_weight: "f32[16]", p_getattr_l__self___down_layers_0___1___norm1_bias: "f32[16]", p_getattr_l__self___down_layers_0___1___conv1_conv_weight: "f32[16, 16, 3, 3, 3]", p_getattr_l__self___down_layers_0___1___norm2_weight: "f32[16]", p_getattr_l__self___down_layers_0___1___norm2_bias: "f32[16]", p_getattr_l__self___down_layers_0___1___conv2_conv_weight: "f32[16, 16, 3, 3, 3]", p_down_layers_1_0_conv_weight: "f32[32, 16, 3, 3, 3]", p_getattr_l__self___down_layers_1___1___norm1_weight: "f32[32]", p_getattr_l__self___down_layers_1___1___norm1_bias: "f32[32]", p_getattr_l__self___down_layers_1___1___conv1_conv_weight: "f32[32, 32, 3, 3, 3]", p_getattr_l__self___down_layers_1___1___norm2_weight: "f32[32]", p_getattr_l__self___down_layers_1___1___norm2_bias: "f32[32]", p_getattr_l__self___down_layers_1___1___conv2_conv_weight: "f32[32, 32, 3, 3, 3]", p_getattr_l__self___down_layers_1___2___norm1_weight: "f32[32]", p_getattr_l__self___down_layers_1___2___norm1_bias: "f32[32]", p_getattr_l__self___down_layers_1___2___conv1_conv_weight: "f32[32, 32, 3, 3, 3]", p_getattr_l__self___down_layers_1___2___norm2_weight: "f32[32]", p_getattr_l__self___down_layers_1___2___norm2_bias: "f32[32]", p_getattr_l__self___down_layers_1___2___conv2_conv_weight: "f32[32, 32, 3, 3, 3]", p_down_layers_2_0_conv_weight: "f32[64, 32, 3, 3, 3]", p_getattr_l__self___down_layers_2___1___norm1_weight: "f32[64]", p_getattr_l__self___down_layers_2___1___norm1_bias: "f32[64]", p_getattr_l__self___down_layers_2___1___conv1_conv_weight: "f32[64, 64, 3, 3, 3]", p_getattr_l__self___down_layers_2___1___norm2_weight: "f32[64]", p_getattr_l__self___down_layers_2___1___norm2_bias: "f32[64]", p_getattr_l__self___down_layers_2___1___conv2_conv_weight: "f32[64, 64, 3, 3, 3]", p_getattr_l__self___down_layers_2___2___norm1_weight: "f32[64]", p_getattr_l__self___down_layers_2___2___norm1_bias: "f32[64]", p_getattr_l__self___down_layers_2___2___conv1_conv_weight: "f32[64, 64, 3, 3, 3]", p_getattr_l__self___down_layers_2___2___norm2_weight: "f32[64]", p_getattr_l__self___down_layers_2___2___norm2_bias: "f32[64]", p_getattr_l__self___down_layers_2___2___conv2_conv_weight: "f32[64, 64, 3, 3, 3]", p_down_layers_3_0_conv_weight: "f32[128, 64, 3, 3, 3]", p_getattr_l__self___down_layers_3___1___norm1_weight: "f32[128]", p_getattr_l__self___down_layers_3___1___norm1_bias: "f32[128]", p_getattr_l__self___down_layers_3___1___conv1_conv_weight: "f32[128, 128, 3, 3, 3]", p_getattr_l__self___down_layers_3___1___norm2_weight: "f32[128]", p_getattr_l__self___down_layers_3___1___norm2_bias: "f32[128]", p_getattr_l__self___down_layers_3___1___conv2_conv_weight: "f32[128, 128, 3, 3, 3]", p_getattr_l__self___down_layers_3___2___norm1_weight: "f32[128]", p_getattr_l__self___down_layers_3___2___norm1_bias: "f32[128]", p_getattr_l__self___down_layers_3___2___conv1_conv_weight: "f32[128, 128, 3, 3, 3]", p_getattr_l__self___down_layers_3___2___norm2_weight: "f32[128]", p_getattr_l__self___down_layers_3___2___norm2_bias: "f32[128]", p_getattr_l__self___down_layers_3___2___conv2_conv_weight: "f32[128, 128, 3, 3, 3]", p_getattr_l__self___down_layers_3___3___norm1_weight: "f32[128]", p_getattr_l__self___down_layers_3___3___norm1_bias: "f32[128]", p_getattr_l__self___down_layers_3___3___conv1_conv_weight: "f32[128, 128, 3, 3, 3]", p_getattr_l__self___down_layers_3___3___norm2_weight: "f32[128]", p_getattr_l__self___down_layers_3___3___norm2_bias: "f32[128]", p_getattr_l__self___down_layers_3___3___conv2_conv_weight: "f32[128, 128, 3, 3, 3]", p_getattr_l__self___down_layers_3___4___norm1_weight: "f32[128]", p_getattr_l__self___down_layers_3___4___norm1_bias: "f32[128]", p_getattr_l__self___down_layers_3___4___conv1_conv_weight: "f32[128, 128, 3, 3, 3]", p_getattr_l__self___down_layers_3___4___norm2_weight: "f32[128]", p_getattr_l__self___down_layers_3___4___norm2_bias: "f32[128]", p_getattr_l__self___down_layers_3___4___conv2_conv_weight: "f32[128, 128, 3, 3, 3]", p_up_samples_0_0_conv_weight: "f32[64, 128, 1, 1, 1]", p_getattr_l__self___up_layers_0___0___norm1_weight: "f32[64]", p_getattr_l__self___up_layers_0___0___norm1_bias: "f32[64]", p_getattr_l__self___up_layers_0___0___conv1_conv_weight: "f32[64, 64, 3, 3, 3]", p_getattr_l__self___up_layers_0___0___norm2_weight: "f32[64]", p_getattr_l__self___up_layers_0___0___norm2_bias: "f32[64]", p_getattr_l__self___up_layers_0___0___conv2_conv_weight: "f32[64, 64, 3, 3, 3]", p_up_samples_1_0_conv_weight: "f32[32, 64, 1, 1, 1]", p_getattr_l__self___up_layers_1___0___norm1_weight: "f32[32]", p_getattr_l__self___up_layers_1___0___norm1_bias: "f32[32]", p_getattr_l__self___up_layers_1___0___conv1_conv_weight: "f32[32, 32, 3, 3, 3]", p_getattr_l__self___up_layers_1___0___norm2_weight: "f32[32]", p_getattr_l__self___up_layers_1___0___norm2_bias: "f32[32]", p_getattr_l__self___up_layers_1___0___conv2_conv_weight: "f32[32, 32, 3, 3, 3]", p_up_samples_2_0_conv_weight: "f32[16, 32, 1, 1, 1]", p_getattr_l__self___up_layers_2___0___norm1_weight: "f32[16]", p_getattr_l__self___up_layers_2___0___norm1_bias: "f32[16]", p_getattr_l__self___up_layers_2___0___conv1_conv_weight: "f32[16, 16, 3, 3, 3]", p_getattr_l__self___up_layers_2___0___norm2_weight: "f32[16]", p_getattr_l__self___up_layers_2___0___norm2_bias: "f32[16]", p_getattr_l__self___up_layers_2___0___conv2_conv_weight: "f32[16, 16, 3, 3, 3]", p_conv_final_0_weight: "f32[16]", p_conv_final_0_bias: "f32[16]", p_conv_final_2_conv_weight: "f32[3, 16, 1, 1, 1]", p_conv_final_2_conv_bias: "f32[3]", x: "f32[1, 4, 224, 224, 128]"):
        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/nets/segresnet.py:157 in encode, code: x = self.convInit(x)
        conv3d: "f32[1, 16, 224, 224, 128]" = torch.ops.aten.conv3d.default(x, p_convinit_conv_weight, None, [1, 1, 1], [1, 1, 1]);  x = p_convinit_conv_weight = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/nets/segresnet.py:159 in encode, code: x = self.dropout(x)
        feature_dropout: "f32[1, 16, 224, 224, 128]" = torch.ops.aten.feature_dropout.default(conv3d, 0.2, False);  conv3d = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:86 in forward, code: x = self.norm1(x)
        group_norm: "f32[1, 16, 224, 224, 128]" = torch.ops.aten.group_norm.default(feature_dropout, 8, p_getattr_l__self___down_layers_0___1___norm1_weight, p_getattr_l__self___down_layers_0___1___norm1_bias);  p_getattr_l__self___down_layers_0___1___norm1_weight = p_getattr_l__self___down_layers_0___1___norm1_bias = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:87 in forward, code: x = self.act(x)
        relu: "f32[1, 16, 224, 224, 128]" = torch.ops.aten.relu.default(group_norm);  group_norm = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:88 in forward, code: x = self.conv1(x)
        conv3d_1: "f32[1, 16, 224, 224, 128]" = torch.ops.aten.conv3d.default(relu, p_getattr_l__self___down_layers_0___1___conv1_conv_weight, None, [1, 1, 1], [1, 1, 1]);  relu = p_getattr_l__self___down_layers_0___1___conv1_conv_weight = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:90 in forward, code: x = self.norm2(x)
        group_norm_1: "f32[1, 16, 224, 224, 128]" = torch.ops.aten.group_norm.default(conv3d_1, 8, p_getattr_l__self___down_layers_0___1___norm2_weight, p_getattr_l__self___down_layers_0___1___norm2_bias);  conv3d_1 = p_getattr_l__self___down_layers_0___1___norm2_weight = p_getattr_l__self___down_layers_0___1___norm2_bias = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:91 in forward, code: x = self.act(x)
        relu_1: "f32[1, 16, 224, 224, 128]" = torch.ops.aten.relu.default(group_norm_1);  group_norm_1 = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:92 in forward, code: x = self.conv2(x)
        conv3d_2: "f32[1, 16, 224, 224, 128]" = torch.ops.aten.conv3d.default(relu_1, p_getattr_l__self___down_layers_0___1___conv2_conv_weight, None, [1, 1, 1], [1, 1, 1]);  relu_1 = p_getattr_l__self___down_layers_0___1___conv2_conv_weight = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:94 in forward, code: x += identity
        add: "f32[1, 16, 224, 224, 128]" = torch.ops.aten.add.Tensor(conv3d_2, feature_dropout);  conv3d_2 = feature_dropout = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/nets/segresnet.py:164 in encode, code: x = down(x)
        conv3d_3: "f32[1, 32, 112, 112, 64]" = torch.ops.aten.conv3d.default(add, p_down_layers_1_0_conv_weight, None, [2, 2, 2], [1, 1, 1]);  p_down_layers_1_0_conv_weight = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:86 in forward, code: x = self.norm1(x)
        group_norm_2: "f32[1, 32, 112, 112, 64]" = torch.ops.aten.group_norm.default(conv3d_3, 8, p_getattr_l__self___down_layers_1___1___norm1_weight, p_getattr_l__self___down_layers_1___1___norm1_bias);  p_getattr_l__self___down_layers_1___1___norm1_weight = p_getattr_l__self___down_layers_1___1___norm1_bias = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:87 in forward, code: x = self.act(x)
        relu_2: "f32[1, 32, 112, 112, 64]" = torch.ops.aten.relu.default(group_norm_2);  group_norm_2 = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:88 in forward, code: x = self.conv1(x)
        conv3d_4: "f32[1, 32, 112, 112, 64]" = torch.ops.aten.conv3d.default(relu_2, p_getattr_l__self___down_layers_1___1___conv1_conv_weight, None, [1, 1, 1], [1, 1, 1]);  relu_2 = p_getattr_l__self___down_layers_1___1___conv1_conv_weight = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:90 in forward, code: x = self.norm2(x)
        group_norm_3: "f32[1, 32, 112, 112, 64]" = torch.ops.aten.group_norm.default(conv3d_4, 8, p_getattr_l__self___down_layers_1___1___norm2_weight, p_getattr_l__self___down_layers_1___1___norm2_bias);  conv3d_4 = p_getattr_l__self___down_layers_1___1___norm2_weight = p_getattr_l__self___down_layers_1___1___norm2_bias = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:91 in forward, code: x = self.act(x)
        relu_3: "f32[1, 32, 112, 112, 64]" = torch.ops.aten.relu.default(group_norm_3);  group_norm_3 = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:92 in forward, code: x = self.conv2(x)
        conv3d_5: "f32[1, 32, 112, 112, 64]" = torch.ops.aten.conv3d.default(relu_3, p_getattr_l__self___down_layers_1___1___conv2_conv_weight, None, [1, 1, 1], [1, 1, 1]);  relu_3 = p_getattr_l__self___down_layers_1___1___conv2_conv_weight = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:94 in forward, code: x += identity
        add_1: "f32[1, 32, 112, 112, 64]" = torch.ops.aten.add.Tensor(conv3d_5, conv3d_3);  conv3d_5 = conv3d_3 = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:86 in forward, code: x = self.norm1(x)
        group_norm_4: "f32[1, 32, 112, 112, 64]" = torch.ops.aten.group_norm.default(add_1, 8, p_getattr_l__self___down_layers_1___2___norm1_weight, p_getattr_l__self___down_layers_1___2___norm1_bias);  p_getattr_l__self___down_layers_1___2___norm1_weight = p_getattr_l__self___down_layers_1___2___norm1_bias = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:87 in forward, code: x = self.act(x)
        relu_4: "f32[1, 32, 112, 112, 64]" = torch.ops.aten.relu.default(group_norm_4);  group_norm_4 = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:88 in forward, code: x = self.conv1(x)
        conv3d_6: "f32[1, 32, 112, 112, 64]" = torch.ops.aten.conv3d.default(relu_4, p_getattr_l__self___down_layers_1___2___conv1_conv_weight, None, [1, 1, 1], [1, 1, 1]);  relu_4 = p_getattr_l__self___down_layers_1___2___conv1_conv_weight = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:90 in forward, code: x = self.norm2(x)
        group_norm_5: "f32[1, 32, 112, 112, 64]" = torch.ops.aten.group_norm.default(conv3d_6, 8, p_getattr_l__self___down_layers_1___2___norm2_weight, p_getattr_l__self___down_layers_1___2___norm2_bias);  conv3d_6 = p_getattr_l__self___down_layers_1___2___norm2_weight = p_getattr_l__self___down_layers_1___2___norm2_bias = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:91 in forward, code: x = self.act(x)
        relu_5: "f32[1, 32, 112, 112, 64]" = torch.ops.aten.relu.default(group_norm_5);  group_norm_5 = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:92 in forward, code: x = self.conv2(x)
        conv3d_7: "f32[1, 32, 112, 112, 64]" = torch.ops.aten.conv3d.default(relu_5, p_getattr_l__self___down_layers_1___2___conv2_conv_weight, None, [1, 1, 1], [1, 1, 1]);  relu_5 = p_getattr_l__self___down_layers_1___2___conv2_conv_weight = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:94 in forward, code: x += identity
        add_2: "f32[1, 32, 112, 112, 64]" = torch.ops.aten.add.Tensor(conv3d_7, add_1);  conv3d_7 = add_1 = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/nets/segresnet.py:164 in encode, code: x = down(x)
        conv3d_8: "f32[1, 64, 56, 56, 32]" = torch.ops.aten.conv3d.default(add_2, p_down_layers_2_0_conv_weight, None, [2, 2, 2], [1, 1, 1]);  p_down_layers_2_0_conv_weight = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:86 in forward, code: x = self.norm1(x)
        group_norm_6: "f32[1, 64, 56, 56, 32]" = torch.ops.aten.group_norm.default(conv3d_8, 8, p_getattr_l__self___down_layers_2___1___norm1_weight, p_getattr_l__self___down_layers_2___1___norm1_bias);  p_getattr_l__self___down_layers_2___1___norm1_weight = p_getattr_l__self___down_layers_2___1___norm1_bias = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:87 in forward, code: x = self.act(x)
        relu_6: "f32[1, 64, 56, 56, 32]" = torch.ops.aten.relu.default(group_norm_6);  group_norm_6 = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:88 in forward, code: x = self.conv1(x)
        conv3d_9: "f32[1, 64, 56, 56, 32]" = torch.ops.aten.conv3d.default(relu_6, p_getattr_l__self___down_layers_2___1___conv1_conv_weight, None, [1, 1, 1], [1, 1, 1]);  relu_6 = p_getattr_l__self___down_layers_2___1___conv1_conv_weight = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:90 in forward, code: x = self.norm2(x)
        group_norm_7: "f32[1, 64, 56, 56, 32]" = torch.ops.aten.group_norm.default(conv3d_9, 8, p_getattr_l__self___down_layers_2___1___norm2_weight, p_getattr_l__self___down_layers_2___1___norm2_bias);  conv3d_9 = p_getattr_l__self___down_layers_2___1___norm2_weight = p_getattr_l__self___down_layers_2___1___norm2_bias = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:91 in forward, code: x = self.act(x)
        relu_7: "f32[1, 64, 56, 56, 32]" = torch.ops.aten.relu.default(group_norm_7);  group_norm_7 = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:92 in forward, code: x = self.conv2(x)
        conv3d_10: "f32[1, 64, 56, 56, 32]" = torch.ops.aten.conv3d.default(relu_7, p_getattr_l__self___down_layers_2___1___conv2_conv_weight, None, [1, 1, 1], [1, 1, 1]);  relu_7 = p_getattr_l__self___down_layers_2___1___conv2_conv_weight = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:94 in forward, code: x += identity
        add_3: "f32[1, 64, 56, 56, 32]" = torch.ops.aten.add.Tensor(conv3d_10, conv3d_8);  conv3d_10 = conv3d_8 = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:86 in forward, code: x = self.norm1(x)
        group_norm_8: "f32[1, 64, 56, 56, 32]" = torch.ops.aten.group_norm.default(add_3, 8, p_getattr_l__self___down_layers_2___2___norm1_weight, p_getattr_l__self___down_layers_2___2___norm1_bias);  p_getattr_l__self___down_layers_2___2___norm1_weight = p_getattr_l__self___down_layers_2___2___norm1_bias = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:87 in forward, code: x = self.act(x)
        relu_8: "f32[1, 64, 56, 56, 32]" = torch.ops.aten.relu.default(group_norm_8);  group_norm_8 = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:88 in forward, code: x = self.conv1(x)
        conv3d_11: "f32[1, 64, 56, 56, 32]" = torch.ops.aten.conv3d.default(relu_8, p_getattr_l__self___down_layers_2___2___conv1_conv_weight, None, [1, 1, 1], [1, 1, 1]);  relu_8 = p_getattr_l__self___down_layers_2___2___conv1_conv_weight = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:90 in forward, code: x = self.norm2(x)
        group_norm_9: "f32[1, 64, 56, 56, 32]" = torch.ops.aten.group_norm.default(conv3d_11, 8, p_getattr_l__self___down_layers_2___2___norm2_weight, p_getattr_l__self___down_layers_2___2___norm2_bias);  conv3d_11 = p_getattr_l__self___down_layers_2___2___norm2_weight = p_getattr_l__self___down_layers_2___2___norm2_bias = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:91 in forward, code: x = self.act(x)
        relu_9: "f32[1, 64, 56, 56, 32]" = torch.ops.aten.relu.default(group_norm_9);  group_norm_9 = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:92 in forward, code: x = self.conv2(x)
        conv3d_12: "f32[1, 64, 56, 56, 32]" = torch.ops.aten.conv3d.default(relu_9, p_getattr_l__self___down_layers_2___2___conv2_conv_weight, None, [1, 1, 1], [1, 1, 1]);  relu_9 = p_getattr_l__self___down_layers_2___2___conv2_conv_weight = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:94 in forward, code: x += identity
        add_4: "f32[1, 64, 56, 56, 32]" = torch.ops.aten.add.Tensor(conv3d_12, add_3);  conv3d_12 = add_3 = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/nets/segresnet.py:164 in encode, code: x = down(x)
        conv3d_13: "f32[1, 128, 28, 28, 16]" = torch.ops.aten.conv3d.default(add_4, p_down_layers_3_0_conv_weight, None, [2, 2, 2], [1, 1, 1]);  p_down_layers_3_0_conv_weight = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:86 in forward, code: x = self.norm1(x)
        group_norm_10: "f32[1, 128, 28, 28, 16]" = torch.ops.aten.group_norm.default(conv3d_13, 8, p_getattr_l__self___down_layers_3___1___norm1_weight, p_getattr_l__self___down_layers_3___1___norm1_bias);  p_getattr_l__self___down_layers_3___1___norm1_weight = p_getattr_l__self___down_layers_3___1___norm1_bias = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:87 in forward, code: x = self.act(x)
        relu_10: "f32[1, 128, 28, 28, 16]" = torch.ops.aten.relu.default(group_norm_10);  group_norm_10 = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:88 in forward, code: x = self.conv1(x)
        conv3d_14: "f32[1, 128, 28, 28, 16]" = torch.ops.aten.conv3d.default(relu_10, p_getattr_l__self___down_layers_3___1___conv1_conv_weight, None, [1, 1, 1], [1, 1, 1]);  relu_10 = p_getattr_l__self___down_layers_3___1___conv1_conv_weight = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:90 in forward, code: x = self.norm2(x)
        group_norm_11: "f32[1, 128, 28, 28, 16]" = torch.ops.aten.group_norm.default(conv3d_14, 8, p_getattr_l__self___down_layers_3___1___norm2_weight, p_getattr_l__self___down_layers_3___1___norm2_bias);  conv3d_14 = p_getattr_l__self___down_layers_3___1___norm2_weight = p_getattr_l__self___down_layers_3___1___norm2_bias = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:91 in forward, code: x = self.act(x)
        relu_11: "f32[1, 128, 28, 28, 16]" = torch.ops.aten.relu.default(group_norm_11);  group_norm_11 = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:92 in forward, code: x = self.conv2(x)
        conv3d_15: "f32[1, 128, 28, 28, 16]" = torch.ops.aten.conv3d.default(relu_11, p_getattr_l__self___down_layers_3___1___conv2_conv_weight, None, [1, 1, 1], [1, 1, 1]);  relu_11 = p_getattr_l__self___down_layers_3___1___conv2_conv_weight = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:94 in forward, code: x += identity
        add_5: "f32[1, 128, 28, 28, 16]" = torch.ops.aten.add.Tensor(conv3d_15, conv3d_13);  conv3d_15 = conv3d_13 = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:86 in forward, code: x = self.norm1(x)
        group_norm_12: "f32[1, 128, 28, 28, 16]" = torch.ops.aten.group_norm.default(add_5, 8, p_getattr_l__self___down_layers_3___2___norm1_weight, p_getattr_l__self___down_layers_3___2___norm1_bias);  p_getattr_l__self___down_layers_3___2___norm1_weight = p_getattr_l__self___down_layers_3___2___norm1_bias = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:87 in forward, code: x = self.act(x)
        relu_12: "f32[1, 128, 28, 28, 16]" = torch.ops.aten.relu.default(group_norm_12);  group_norm_12 = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:88 in forward, code: x = self.conv1(x)
        conv3d_16: "f32[1, 128, 28, 28, 16]" = torch.ops.aten.conv3d.default(relu_12, p_getattr_l__self___down_layers_3___2___conv1_conv_weight, None, [1, 1, 1], [1, 1, 1]);  relu_12 = p_getattr_l__self___down_layers_3___2___conv1_conv_weight = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:90 in forward, code: x = self.norm2(x)
        group_norm_13: "f32[1, 128, 28, 28, 16]" = torch.ops.aten.group_norm.default(conv3d_16, 8, p_getattr_l__self___down_layers_3___2___norm2_weight, p_getattr_l__self___down_layers_3___2___norm2_bias);  conv3d_16 = p_getattr_l__self___down_layers_3___2___norm2_weight = p_getattr_l__self___down_layers_3___2___norm2_bias = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:91 in forward, code: x = self.act(x)
        relu_13: "f32[1, 128, 28, 28, 16]" = torch.ops.aten.relu.default(group_norm_13);  group_norm_13 = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:92 in forward, code: x = self.conv2(x)
        conv3d_17: "f32[1, 128, 28, 28, 16]" = torch.ops.aten.conv3d.default(relu_13, p_getattr_l__self___down_layers_3___2___conv2_conv_weight, None, [1, 1, 1], [1, 1, 1]);  relu_13 = p_getattr_l__self___down_layers_3___2___conv2_conv_weight = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:94 in forward, code: x += identity
        add_6: "f32[1, 128, 28, 28, 16]" = torch.ops.aten.add.Tensor(conv3d_17, add_5);  conv3d_17 = add_5 = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:86 in forward, code: x = self.norm1(x)
        group_norm_14: "f32[1, 128, 28, 28, 16]" = torch.ops.aten.group_norm.default(add_6, 8, p_getattr_l__self___down_layers_3___3___norm1_weight, p_getattr_l__self___down_layers_3___3___norm1_bias);  p_getattr_l__self___down_layers_3___3___norm1_weight = p_getattr_l__self___down_layers_3___3___norm1_bias = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:87 in forward, code: x = self.act(x)
        relu_14: "f32[1, 128, 28, 28, 16]" = torch.ops.aten.relu.default(group_norm_14);  group_norm_14 = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:88 in forward, code: x = self.conv1(x)
        conv3d_18: "f32[1, 128, 28, 28, 16]" = torch.ops.aten.conv3d.default(relu_14, p_getattr_l__self___down_layers_3___3___conv1_conv_weight, None, [1, 1, 1], [1, 1, 1]);  relu_14 = p_getattr_l__self___down_layers_3___3___conv1_conv_weight = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:90 in forward, code: x = self.norm2(x)
        group_norm_15: "f32[1, 128, 28, 28, 16]" = torch.ops.aten.group_norm.default(conv3d_18, 8, p_getattr_l__self___down_layers_3___3___norm2_weight, p_getattr_l__self___down_layers_3___3___norm2_bias);  conv3d_18 = p_getattr_l__self___down_layers_3___3___norm2_weight = p_getattr_l__self___down_layers_3___3___norm2_bias = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:91 in forward, code: x = self.act(x)
        relu_15: "f32[1, 128, 28, 28, 16]" = torch.ops.aten.relu.default(group_norm_15);  group_norm_15 = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:92 in forward, code: x = self.conv2(x)
        conv3d_19: "f32[1, 128, 28, 28, 16]" = torch.ops.aten.conv3d.default(relu_15, p_getattr_l__self___down_layers_3___3___conv2_conv_weight, None, [1, 1, 1], [1, 1, 1]);  relu_15 = p_getattr_l__self___down_layers_3___3___conv2_conv_weight = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:94 in forward, code: x += identity
        add_7: "f32[1, 128, 28, 28, 16]" = torch.ops.aten.add.Tensor(conv3d_19, add_6);  conv3d_19 = add_6 = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:86 in forward, code: x = self.norm1(x)
        group_norm_16: "f32[1, 128, 28, 28, 16]" = torch.ops.aten.group_norm.default(add_7, 8, p_getattr_l__self___down_layers_3___4___norm1_weight, p_getattr_l__self___down_layers_3___4___norm1_bias);  p_getattr_l__self___down_layers_3___4___norm1_weight = p_getattr_l__self___down_layers_3___4___norm1_bias = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:87 in forward, code: x = self.act(x)
        relu_16: "f32[1, 128, 28, 28, 16]" = torch.ops.aten.relu.default(group_norm_16);  group_norm_16 = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:88 in forward, code: x = self.conv1(x)
        conv3d_20: "f32[1, 128, 28, 28, 16]" = torch.ops.aten.conv3d.default(relu_16, p_getattr_l__self___down_layers_3___4___conv1_conv_weight, None, [1, 1, 1], [1, 1, 1]);  relu_16 = p_getattr_l__self___down_layers_3___4___conv1_conv_weight = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:90 in forward, code: x = self.norm2(x)
        group_norm_17: "f32[1, 128, 28, 28, 16]" = torch.ops.aten.group_norm.default(conv3d_20, 8, p_getattr_l__self___down_layers_3___4___norm2_weight, p_getattr_l__self___down_layers_3___4___norm2_bias);  conv3d_20 = p_getattr_l__self___down_layers_3___4___norm2_weight = p_getattr_l__self___down_layers_3___4___norm2_bias = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:91 in forward, code: x = self.act(x)
        relu_17: "f32[1, 128, 28, 28, 16]" = torch.ops.aten.relu.default(group_norm_17);  group_norm_17 = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:92 in forward, code: x = self.conv2(x)
        conv3d_21: "f32[1, 128, 28, 28, 16]" = torch.ops.aten.conv3d.default(relu_17, p_getattr_l__self___down_layers_3___4___conv2_conv_weight, None, [1, 1, 1], [1, 1, 1]);  relu_17 = p_getattr_l__self___down_layers_3___4___conv2_conv_weight = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:94 in forward, code: x += identity
        add_8: "f32[1, 128, 28, 28, 16]" = torch.ops.aten.add.Tensor(conv3d_21, add_7);  conv3d_21 = add_7 = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/nets/segresnet.py:171 in decode, code: x = up(x) + down_x[i + 1]
        conv3d_22: "f32[1, 64, 28, 28, 16]" = torch.ops.aten.conv3d.default(add_8, p_up_samples_0_0_conv_weight);  add_8 = p_up_samples_0_0_conv_weight = None
        upsample_trilinear3d: "f32[1, 64, 56, 56, 32]" = torch.ops.aten.upsample_trilinear3d.vec(conv3d_22, None, False, [2.0, 2.0, 2.0]);  conv3d_22 = None
        add_9: "f32[1, 64, 56, 56, 32]" = torch.ops.aten.add.Tensor(upsample_trilinear3d, add_4);  upsample_trilinear3d = add_4 = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:86 in forward, code: x = self.norm1(x)
        group_norm_18: "f32[1, 64, 56, 56, 32]" = torch.ops.aten.group_norm.default(add_9, 8, p_getattr_l__self___up_layers_0___0___norm1_weight, p_getattr_l__self___up_layers_0___0___norm1_bias);  p_getattr_l__self___up_layers_0___0___norm1_weight = p_getattr_l__self___up_layers_0___0___norm1_bias = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:87 in forward, code: x = self.act(x)
        relu_18: "f32[1, 64, 56, 56, 32]" = torch.ops.aten.relu.default(group_norm_18);  group_norm_18 = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:88 in forward, code: x = self.conv1(x)
        conv3d_23: "f32[1, 64, 56, 56, 32]" = torch.ops.aten.conv3d.default(relu_18, p_getattr_l__self___up_layers_0___0___conv1_conv_weight, None, [1, 1, 1], [1, 1, 1]);  relu_18 = p_getattr_l__self___up_layers_0___0___conv1_conv_weight = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:90 in forward, code: x = self.norm2(x)
        group_norm_19: "f32[1, 64, 56, 56, 32]" = torch.ops.aten.group_norm.default(conv3d_23, 8, p_getattr_l__self___up_layers_0___0___norm2_weight, p_getattr_l__self___up_layers_0___0___norm2_bias);  conv3d_23 = p_getattr_l__self___up_layers_0___0___norm2_weight = p_getattr_l__self___up_layers_0___0___norm2_bias = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:91 in forward, code: x = self.act(x)
        relu_19: "f32[1, 64, 56, 56, 32]" = torch.ops.aten.relu.default(group_norm_19);  group_norm_19 = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:92 in forward, code: x = self.conv2(x)
        conv3d_24: "f32[1, 64, 56, 56, 32]" = torch.ops.aten.conv3d.default(relu_19, p_getattr_l__self___up_layers_0___0___conv2_conv_weight, None, [1, 1, 1], [1, 1, 1]);  relu_19 = p_getattr_l__self___up_layers_0___0___conv2_conv_weight = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:94 in forward, code: x += identity
        add_10: "f32[1, 64, 56, 56, 32]" = torch.ops.aten.add.Tensor(conv3d_24, add_9);  conv3d_24 = add_9 = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/nets/segresnet.py:171 in decode, code: x = up(x) + down_x[i + 1]
        conv3d_25: "f32[1, 32, 56, 56, 32]" = torch.ops.aten.conv3d.default(add_10, p_up_samples_1_0_conv_weight);  add_10 = p_up_samples_1_0_conv_weight = None
        upsample_trilinear3d_1: "f32[1, 32, 112, 112, 64]" = torch.ops.aten.upsample_trilinear3d.vec(conv3d_25, None, False, [2.0, 2.0, 2.0]);  conv3d_25 = None
        add_11: "f32[1, 32, 112, 112, 64]" = torch.ops.aten.add.Tensor(upsample_trilinear3d_1, add_2);  upsample_trilinear3d_1 = add_2 = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:86 in forward, code: x = self.norm1(x)
        group_norm_20: "f32[1, 32, 112, 112, 64]" = torch.ops.aten.group_norm.default(add_11, 8, p_getattr_l__self___up_layers_1___0___norm1_weight, p_getattr_l__self___up_layers_1___0___norm1_bias);  p_getattr_l__self___up_layers_1___0___norm1_weight = p_getattr_l__self___up_layers_1___0___norm1_bias = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:87 in forward, code: x = self.act(x)
        relu_20: "f32[1, 32, 112, 112, 64]" = torch.ops.aten.relu.default(group_norm_20);  group_norm_20 = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:88 in forward, code: x = self.conv1(x)
        conv3d_26: "f32[1, 32, 112, 112, 64]" = torch.ops.aten.conv3d.default(relu_20, p_getattr_l__self___up_layers_1___0___conv1_conv_weight, None, [1, 1, 1], [1, 1, 1]);  relu_20 = p_getattr_l__self___up_layers_1___0___conv1_conv_weight = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:90 in forward, code: x = self.norm2(x)
        group_norm_21: "f32[1, 32, 112, 112, 64]" = torch.ops.aten.group_norm.default(conv3d_26, 8, p_getattr_l__self___up_layers_1___0___norm2_weight, p_getattr_l__self___up_layers_1___0___norm2_bias);  conv3d_26 = p_getattr_l__self___up_layers_1___0___norm2_weight = p_getattr_l__self___up_layers_1___0___norm2_bias = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:91 in forward, code: x = self.act(x)
        relu_21: "f32[1, 32, 112, 112, 64]" = torch.ops.aten.relu.default(group_norm_21);  group_norm_21 = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:92 in forward, code: x = self.conv2(x)
        conv3d_27: "f32[1, 32, 112, 112, 64]" = torch.ops.aten.conv3d.default(relu_21, p_getattr_l__self___up_layers_1___0___conv2_conv_weight, None, [1, 1, 1], [1, 1, 1]);  relu_21 = p_getattr_l__self___up_layers_1___0___conv2_conv_weight = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:94 in forward, code: x += identity
        add_12: "f32[1, 32, 112, 112, 64]" = torch.ops.aten.add.Tensor(conv3d_27, add_11);  conv3d_27 = add_11 = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/nets/segresnet.py:171 in decode, code: x = up(x) + down_x[i + 1]
        conv3d_28: "f32[1, 16, 112, 112, 64]" = torch.ops.aten.conv3d.default(add_12, p_up_samples_2_0_conv_weight);  add_12 = p_up_samples_2_0_conv_weight = None
        upsample_trilinear3d_2: "f32[1, 16, 224, 224, 128]" = torch.ops.aten.upsample_trilinear3d.vec(conv3d_28, None, False, [2.0, 2.0, 2.0]);  conv3d_28 = None
        add_13: "f32[1, 16, 224, 224, 128]" = torch.ops.aten.add.Tensor(upsample_trilinear3d_2, add);  upsample_trilinear3d_2 = add = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:86 in forward, code: x = self.norm1(x)
        group_norm_22: "f32[1, 16, 224, 224, 128]" = torch.ops.aten.group_norm.default(add_13, 8, p_getattr_l__self___up_layers_2___0___norm1_weight, p_getattr_l__self___up_layers_2___0___norm1_bias);  p_getattr_l__self___up_layers_2___0___norm1_weight = p_getattr_l__self___up_layers_2___0___norm1_bias = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:87 in forward, code: x = self.act(x)
        relu_22: "f32[1, 16, 224, 224, 128]" = torch.ops.aten.relu.default(group_norm_22);  group_norm_22 = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:88 in forward, code: x = self.conv1(x)
        conv3d_29: "f32[1, 16, 224, 224, 128]" = torch.ops.aten.conv3d.default(relu_22, p_getattr_l__self___up_layers_2___0___conv1_conv_weight, None, [1, 1, 1], [1, 1, 1]);  relu_22 = p_getattr_l__self___up_layers_2___0___conv1_conv_weight = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:90 in forward, code: x = self.norm2(x)
        group_norm_23: "f32[1, 16, 224, 224, 128]" = torch.ops.aten.group_norm.default(conv3d_29, 8, p_getattr_l__self___up_layers_2___0___norm2_weight, p_getattr_l__self___up_layers_2___0___norm2_bias);  conv3d_29 = p_getattr_l__self___up_layers_2___0___norm2_weight = p_getattr_l__self___up_layers_2___0___norm2_bias = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:91 in forward, code: x = self.act(x)
        relu_23: "f32[1, 16, 224, 224, 128]" = torch.ops.aten.relu.default(group_norm_23);  group_norm_23 = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:92 in forward, code: x = self.conv2(x)
        conv3d_30: "f32[1, 16, 224, 224, 128]" = torch.ops.aten.conv3d.default(relu_23, p_getattr_l__self___up_layers_2___0___conv2_conv_weight, None, [1, 1, 1], [1, 1, 1]);  relu_23 = p_getattr_l__self___up_layers_2___0___conv2_conv_weight = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/blocks/segresnet_block.py:94 in forward, code: x += identity
        add_14: "f32[1, 16, 224, 224, 128]" = torch.ops.aten.add.Tensor(conv3d_30, add_13);  conv3d_30 = add_13 = None

        # File: /usr/local/lib/python3.10/dist-packages/monai/networks/nets/segresnet.py:175 in decode, code: x = self.conv_final(x)
        group_norm_24: "f32[1, 16, 224, 224, 128]" = torch.ops.aten.group_norm.default(add_14, 8, p_conv_final_0_weight, p_conv_final_0_bias);  add_14 = p_conv_final_0_weight = p_conv_final_0_bias = None
        relu_24: "f32[1, 16, 224, 224, 128]" = torch.ops.aten.relu.default(group_norm_24);  group_norm_24 = None
        conv3d_31: "f32[1, 3, 224, 224, 128]" = torch.ops.aten.conv3d.default(relu_24, p_conv_final_2_conv_weight, p_conv_final_2_conv_bias);  relu_24 = p_conv_final_2_conv_weight = p_conv_final_2_conv_bias = None
        return (conv3d_31,)

Yes, thank you

That's very strange. If you run torch.onnx.dynamo_export(exported_program, ...), do you get the same graph?

@justinchuby Is this the procedure you're suggesting?
    exported_program = torch.export.export(model, (data,))
    exported_program._graph_module.print_readable()

    export_output = torch.onnx.dynamo_export(exported_program, data)
    export_output.save('Clara_SegResNet_dynamo1.onnx')

The exported UpSample module looks about the same as before, still a very big graph.
In addition, the weights in the model appear as extra inputs, giving rise to tens of extra model inputs. Similar to this bug https://github.com/pytorch/pytorch/issues/126071

I don't see any resize ops, which is puzzling. Could you share the onnx model? You may remove the weights if it is too big

I was expecting to see this function: https://github.com/microsoft/onnxscript/blob/2b6dc27b34f2e4e9fc4c3ad73635c5b157a4c714/onnxscript/function_libs/torch_lib/ops/nn.py#L2215

@justinchuby I uploaded the two versions of the model here: https://drive.google.com/drive/folders/1s1lhKRuG6fOZmD4IjZvN_zlWfIxPB_8w?usp=sharing

It's possible that the upsample op was somehow decomposed by PyTorch. I will look deeper.

I have found that in general case, one has to run exported_program.run_decompositions() before applying dynamo_export().
That may in fact fold some operations. @yuanyao-nv can you try that ?

Thanks. We will be creating a series of changes to the exporter to support ExportedPrograms properly, including handling of the weights.

@borisfom I tried running run_decompositions() but it didn't do anything for this particular subgraph.

There are two issues here:

Unsupported upsample_trilinear_vec op: #1592
Dynamo forces to decompose upsample related ops for some reasons (related issue: https://github.com/pytorch/pytorch/issues/115883 and https://github.com/pytorch/pytorch/issues/116684). I will add the op into our workaround on this kind of issue.

Hi @yuanyao-nv,
This one should be fixed when you call torch.onnx.dynamo_export with nn.Module. However, if you call torch.export.export first, it's going to be decomposed to the big subgraph you had. This decomposition is forced by dynamo for some reasons. Feel free to open an issue like https://github.com/pytorch/pytorch/issues/115883.
cc @gramalingam @justinchuby @xadupre This forcing decomposition would need us to maybe rewriting them as patterns. It will come back to us once we rely on torch.export.export.

@titaiwangms Thanks for the update.
What's a good way to test it out?
If I rerun the export script in the description using the latest torch nightly build (2.5.0.dev20240617+cu121) I actually hit another error
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/onnx/_internal/exporter.py", line 1509, in dynamo_export
    ).export()
  File "/usr/local/lib/python3.10/dist-packages/torch/onnx/_internal/exporter.py", line 1236, in export
    graph_module = self.options.fx_tracer.generate_fx(
  File "/usr/local/lib/python3.10/dist-packages/torch/onnx/_internal/fx/dynamo_graph_extractor.py", line 214, in generate_fx
    graph_module, graph_guard = torch._dynamo.export(
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py", line 1379, in inner
    result_traced = opt_f(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py", line 433, in _fn
    return fn(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/onnx/_internal/fx/dynamo_graph_extractor.py", line 169, in wrapped
    return output_adapter.apply(model_func(*args, **kwargs), model=model)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/convert_frame.py", line 1116, in __call__
    return self._torchdynamo_orig_callable(
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/convert_frame.py", line 472, in __call__
    return _compile(
  File "/usr/local/lib/python3.10/dist-packages/torch/_utils_internal.py", line 84, in wrapper_function
    return StrobelightCompileTimeProfiler.profile_compile_time(
  File "/usr/local/lib/python3.10/dist-packages/torch/_strobelight/compile_time_profiler.py", line 129, in profile_compile_time
    return func(*args, **kwargs)
  File "/usr/lib/python3.10/contextlib.py", line 79, in inner
    return func(*args, **kwds)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/convert_frame.py", line 817, in _compile
    guarded_code = compile_inner(code, one_graph, hooks, transform)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/utils.py", line 231, in time_wrapper
    r = func(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/convert_frame.py", line 636, in compile_inner
    out_code = transform_code_object(code, transform)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/bytecode_transformation.py", line 1185, in transform_code_object
    transformations(instructions, code_options)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/convert_frame.py", line 178, in _fn
    return fn(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/convert_frame.py", line 582, in transform
    tracer.run()
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 2462, in run
    super().run()
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 904, in run
    while self.step():
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 816, in step
    self.dispatch_table[inst.opcode](self, inst)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 510, in wrapper
    return inner_fn(self, inst)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 1470, in CALL_FUNCTION
    self.call_function(fn, args, {})
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 754, in call_function
    self.push(fn.call_function(self, args, kwargs))
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/functions.py", line 356, in call_function
    return super().call_function(tx, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/functions.py", line 298, in call_function
    return super().call_function(tx, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/functions.py", line 95, in call_function
    return tx.inline_user_function_return(self, [*self.self_args(), *args], kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 760, in inline_user_function_return
    return InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 2677, in inline_call
    return cls.inline_call_(parent, func, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 2793, in inline_call_
    tracer.run()
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 904, in run
    while self.step():
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 816, in step
    self.dispatch_table[inst.opcode](self, inst)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 234, in impl
    self.push(fn_var.call_function(self, self.popn(nargs), {}))
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/builtin.py", line 962, in call_function
    return handler(tx, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/builtin.py", line 941, in _handle_insert_op_in_graph
    return wrap_fx_proxy(tx, proxy)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/builder.py", line 1759, in wrap_fx_proxy
    return wrap_fx_proxy_cls(target_cls=TensorVariable, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/builder.py", line 1846, in wrap_fx_proxy_cls
    example_value = get_fake_value(proxy.node, tx, allow_non_graph_fake=True)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/utils.py", line 1853, in get_fake_value
    raise TorchRuntimeError(str(e)).with_traceback(e.__traceback__) from None
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/utils.py", line 1785, in get_fake_value
    ret_val = wrap_fake_exception(
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/utils.py", line 1300, in wrap_fake_exception
    return fn()
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/utils.py", line 1786, in <lambda>
    lambda: run_node(tx.output, node, args, kwargs, nnmodule)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/utils.py", line 1921, in run_node
    raise RuntimeError(make_error_message(e)).with_traceback(
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/utils.py", line 1903, in run_node
    return node.target(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/_stats.py", line 21, in wrapper
    return fn(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_subclasses/fake_tensor.py", line 1061, in __torch_dispatch__
    return self.dispatch(func, types, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_subclasses/fake_tensor.py", line 1450, in dispatch
    return self._cached_dispatch_impl(func, types, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_subclasses/fake_tensor.py", line 1145, in _cached_dispatch_impl
    output = self._dispatch_impl(func, types, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_subclasses/fake_tensor.py", line 1757, in _dispatch_impl
    r = func(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_ops.py", line 670, in __call__
    return self_._op(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_prims_common/wrappers.py", line 266, in _fn
    result = fn(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_prims_common/wrappers.py", line 138, in _fn
    result = fn(**bound.arguments)
  File "/usr/local/lib/python3.10/dist-packages/torch/_refs/__init__.py", line 1080, in add
    a, b = _maybe_broadcast(a, b)
  File "/usr/local/lib/python3.10/dist-packages/torch/_refs/__init__.py", line 419, in _maybe_broadcast
    common_shape = _broadcast_shapes(
  File "/usr/local/lib/python3.10/dist-packages/torch/_refs/__init__.py", line 408, in _broadcast_shapes
    raise RuntimeError(
torch._dynamo.exc.TorchRuntimeError: Failed running call_function <built-in function add>(*(FakeTensor(..., device='cuda:0', size=(1, 64, 28, 56, 56, 32),
           grad_fn=<WarnNotImplemented>), FakeTensor(..., device='cuda:0', size=(1, 64, 56, 56, 32),
           grad_fn=<AddBackward0>)), **{}):
Attempting to broadcast a dimension of length 64 at -4! Mismatching argument at index 1 had torch.Size([1, 64, 56, 56, 32]); but expected shape should be broadcastable to [1, 64, 28, 56, 56, 32]

from user code:
   File "/usr/local/lib/python3.10/dist-packages/monai/networks/nets/segresnet.py", line 183, in forward
    x = self.decode(x, down_x)
  File "/usr/local/lib/python3.10/dist-packages/monai/networks/nets/segresnet.py", line 171, in decode
    x = up(x) + down_x[i + 1]

Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information

Do you know why fake tensor is being used in the latest torch version?
I also tried exporting just a nn.Upsample function
def f(x):
    m = torch.nn.Upsample(size=(10), mode='linear')
    return m(x)

x = torch.randn(2, 5, 5)
export_output = torch.onnx.dynamo_export(f, x)
export_output.save('Upsample.onnx')

The exported graph looks reasonable. Is this what you'd expect?

Filed a separate issue to track the above fake tensor broadcast error https://github.com/pytorch/pytorch/issues/129534

Fixed by https://github.com/justinchuby/torch-onnx/pull/151

Apparently our upsample implementation is not accurate so this would harm accuracy. Next step is to fix that.

The rest of the work is tracked in #1816


It doesn't demangle PEF function names (classic mac): GCC < 3.0 / CodeWarrior format

Describe the bug
For instance, after autoanalysis with PEF debug enabled I see in decompiled code .scroll__10TTextPanelFUcsi instead of TTextPanel::scroll((unsigned char, short, int)) as the name of a function.
The file was almost certainly compiled with CodeWarrior on MacOS 8 or 9.

See issue 675

You can try turning off the Analysis->Demangler->OnlyDemangleKnownMangledSymbols. This can cause symbols that aren't mangled to demangle.
A simpler way is to run the DemangleSymbolScript on that symbol to see if the symbol can be demangled.  This should work as long as the demangle routine can either just keep the '.', or strip it off and the demangling is the same.
Normally a symbol has the _Z in front of it if it is mangled.  If this works, I suppose an option for the pre-characters that must be present could be added, or the '.' could be added as a pre-character that is accepted.

@emteere it doesn't work :(

The GNU c++filt utility partially does the job: it implements the old GCC format, very similar to the VisualAge one

The old GCC demangling format could be implemented using the C demangler, see here. It requires just a few changes to the demangle api.

Much has changed in demangler.   This seems to be working now.


Hybrid concept question

Is Azure Active Directory smart lockout designed to work with AD Connect using Password Hash Sync, when you don't have Exchange Hybrid deployment?
For instance, when the documentation states "Smart lockout can be integrated with hybrid deployments, using password hash sync or pass-through authentication to protect on-premises Active Directory accounts from being locked out by attackers" does hybrid deployments relate to Exchange Hybrid deployement? Is Exchange Hybrid deployement needed for the Smart lockout to work?

Document Details
⚠ Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.

ID: 9c7b90ca-a2b1-9cd8-9769-cd48c27f10ea
Version Independent ID: b8c29389-c435-b2f0-4186-1e9c47ab4f3e
Content: Preventing brute-force attacks using Azure AD smart lockout
Content Source: articles/active-directory/authentication/howto-password-smart-lockout.md
Service: active-directory
GitHub Login: @MicrosoftGuyJFlo
Microsoft Alias: joflore

#please-close


Can't import the named export 'LIST_NEST_MODE_HTML' from non EcmaScript module (only default export is available)

Not able to import into Nextjs app:
node -v: 14.18.1
yarn -v: 1.22
react: 17.0.1

The same for me in React app based on CRA

I cannot reproduce this. Which versions of the module are you using?
Minimal next.js page:
import {PortableText} from '@portabletext/react'

const Home = () => {
  return (
    <PortableText value={
      [{
        _type: 'block',
        children: [{_type: 'span', text: 'Hello from Portable Text.'}]
      }]
    } />
  )
}

export default Home

Minimal create react app:
import React from 'react';
import ReactDOM from 'react-dom';
import {PortableText} from '@portabletext/react';

ReactDOM.render(
  <React.StrictMode>
    <PortableText value={
      [{
        _type: 'block',
        children: [{_type: 'span', text: 'Hello from Portable Text.'}]
      }]
    } />
  </React.StrictMode>,
  document.getElementById('root')
);

This is happening for me as well. Using the Gatsby Blog Starter pulled down in April 2020.
"react": "16.13.1"
"gatsby": "2.24.49"
It's attempting to use the .mjs export since I'm using ESM import/export syntax in the project.
Here is the error when building:
ERROR #98123  WEBPACK
web: Generating development JavaScript bundle failed
web: Can't import the named export 'LIST_NEST_MODE_HTML' from non EcmaScript module (only default export is available)
web: File<EMAIL_ADDRESS>web:  ERROR #98123  WEBPACK
web: Generating development JavaScript bundle failed
web: Can't import the named export 'buildMarksTree' from non EcmaScript module (only default export is available)
web: File<EMAIL_ADDRESS>web:  ERROR #98123  WEBPACK
web: Generating development JavaScript bundle failed
web: Can't import the named export 'createContext' from non EcmaScript module (only default export is available)
web: File<EMAIL_ADDRESS>web:  ERROR #98123  WEBPACK
web: Generating development JavaScript bundle failed
web: Can't import the named export 'isPortableTextBlock' from non EcmaScript module (only default export is available)
web: File<EMAIL_ADDRESS>web:  ERROR #98123  WEBPACK
web: Generating development JavaScript bundle failed
web: Can't import the named export 'isPortableTextListItemBlock' from non EcmaScript module (only default export is available)
web: File<EMAIL_ADDRESS>web:  ERROR #98123  WEBPACK
web: Generating development JavaScript bundle failed
web: Can't import the named export 'isPortableTextToolkitList' from non EcmaScript module (only default export is available)
web: File<EMAIL_ADDRESS>web:  ERROR #98123  WEBPACK
web: Generating development JavaScript bundle failed
web: Can't import the named export 'isPortableTextToolkitSpan' from non EcmaScript module (only default export is available)
web: File<EMAIL_ADDRESS>web:  ERROR #98123  WEBPACK
web: Generating development JavaScript bundle failed
web: Can't import the named export 'isPortableTextToolkitTextNode' from non EcmaScript module (only default export is available)
web: File<EMAIL_ADDRESS>web:  ERROR #98123  WEBPACK
web: Generating development JavaScript bundle failed
web: Can't import the named export 'nestLists' from non EcmaScript module (only default export is available)
web: File<EMAIL_ADDRESS>web:  ERROR #98123  WEBPACK
web: Generating development JavaScript bundle failed
web: Can't import the named export 'spanToPlainText' from non EcmaScript module (only default export is available)
web: File<EMAIL_ADDRESS>web:  ERROR #98123  WEBPACK
web: Generating development JavaScript bundle failed
web: Can't import the named export 'useContext' from non EcmaScript module (only default export is available)
web: File<EMAIL_ADDRESS>web:  ERROR #98123  WEBPACK
web: Generating development JavaScript bundle failed
web: Can't import the named export 'useMemo' from non EcmaScript module (only default export is available)
web: File<EMAIL_ADDRESS>web:  ERROR #98123  WEBPACK
web: Generating development JavaScript bundle failed
web: Can't import the named export 'useMemo' from non EcmaScript module (only default export is available)
web: File<EMAIL_ADDRESS>web:  ERROR #98123  WEBPACK
web: Generating development JavaScript bundle failed
web: Can't import the named export 'useMemo' from non EcmaScript module (only default export is available)
web: File<EMAIL_ADDRESS>web:  ERROR #98123  WEBPACK
web: Generating development JavaScript bundle failed
web: Can't reexport the named export 'toPlainText' from non EcmaScript module (only default export is available)
web: File<EMAIL_ADDRESS>web: failed Re-building development bundle - 0.205s

@JGuest777 What version of next are you running and what do you see when you run npm ls webpack?

next v10.0.6
that npm command returns (empty)
Will upgrade to canary.6 and try!

Thanks for confirming. If I was on the right track then v10.0.6 should have worked fine, so I suspect something else. I'll keep looking.

@holdercp For your case, it might be related to the version of React. It appears React >= 17 is required.

@red-runner Found the solution here for Storybook. Worked for me.

Nice one @carlosganzerla worked for me!! 🥇 💐

node -v: 16.15.0
npm -v: 8.5.5
react: 17.0.2
Still the same for me

I've spin up a codesandbox here for an old CRA but I can't replicate the issue, however, I can just like others here in my local setup. Probably something to do with other packages or a mixed of other commonjs requires. Idk.

I ran into this issue with version 1.0.6 of @portabletext/react. Upgrading to 2.0.1 resolved the issue. Full error I was receiving:
Server Error

SyntaxError: Named export 'LIST_NEST_MODE_HTML' not found. The requested module '@portabletext/toolkit' is a CommonJS module, which may not support all module.exports as named exports.
CommonJS modules can always be imported via the default export, for example using:

import pkg from '@portabletext/toolkit';
const { nestLists, LIST_NEST_MODE_HTML, isPortableTextToolkitList, isPortableTextListItemBlock, isPortableTextToolkitSpan, spanToPlainText, isPortableTextBlock, isPortableTextToolkitTextNode, buildMarksTree } = pkg;
This error happened while generating the page. Any console logs will be displayed in the terminal window.
Call Stack
<unknown>
<EMAIL_ADDRESS>(32)
ModuleJob._instantiate
node:internal/modules/esm/module_job (123:21)
async ModuleJob.run
node:internal/modules/esm/module_job (189:5)
async ESMLoader.import
node:internal/modules/esm/loader (530:24)
async importModuleDynamicallyWrapper
node:internal/vm/module (438:15)

Should be fixed in the latest versions - please leave a comment if you're still having issues with the 2.x version.


Add request to Edit Handler

Passing in the request adds significant flexibility when developing new panels. This was developed to support adding multiple images to a Page model by drag and drop.

Will make these changes asap and update the PR

:+1:

🎉 merged! This will make its way to a Wagtail site near you in time for Wagtail 2.1. Thank you @rajeev.


@bentley/imodeljs-native 3.0.18

@nick4598 @kabentley before I rummage around your changes, please feel free to push fix for removal/rename/refactor of DownloadV2Checkpoint.
src/workspace/CloudSqlite.ts:93:48 - error TS2339: Property 'DownloadV2Checkpoint' does not exist on type 'typeof IModelJsNative'.                                                                                93     const downloader = new IModelHost.platform.DownloadV2Checkpoint({ ...db, ...props });

@aruniverse @calebmshafer it appears that the addon package now uses type definitions from the version of core-common that are specified in its package.json instead of those specified by core-backend.
When did that change? It must have been relatively recently as previous addon builds did not exhibit these errors.
Argument of type 'DynamicGraphicsRequest3dProps' is not assignable to parameter of type 'ElementGraphicsRequestProps'.                                                                                Type 'import("D:/j2/s/imodeljs/core/common/lib/cjs/tile/ElementGraphics").DynamicGraphicsRequest3dProps' is not assignable to type 'import("D:/j2/s/imodeljs/common/temp/node_modules/.pnpm/@itwin+core-common@3.0.0-extension.1_f2c3418fbeb77077b236020c30c9aae4/node_modules/@itwin/core-common/lib/cjs/tile/ElementGraphics").DynamicGraphicsRequest3dProps'.

@aruniverse @calebmshafer it appears that the addon package now uses type definitions from the version of core-common that are specified in its package.json instead of those specified by core-backend. When did that change? It must have been relatively recently as previous addon builds did not exhibit these errors.
Argument of type 'DynamicGraphicsRequest3dProps' is not assignable to parameter of type 'ElementGraphicsRequestProps'. Type 'import("D:/j2/s/imodeljs/core/common/lib/cjs/tile/ElementGraphics").DynamicGraphicsRequest3dProps' is not assignable to type 'import("D:/j2/s/imodeljs/common/temp/node_modules/.pnpm/@iTwin+core-common@3.0.0-extension.1_f2c3418fbeb77077b236020c30c9aae4/node_modules/@itwin/core-common/lib/cjs/tile/ElementGraphics").DynamicGraphicsRequest3dProps'.

@wgoehrig

I'm not too familiar with the native addon side (yet, id like to be one day) to be of much help here. But one thing that does stand out is it looks like it's pulling in an outdated ver of core coming from the extension pre-release we did, due to how we have the peer deps setup.
Paulius mentioned pinning down peer deps in this thread, https://github.com/iTwin/itwinjs-core/pull/2741#discussion_r753154387, which might fix what you're seeing.

@nick4598 @kabentley before I rummage around your changes, please feel free to push fix for removal/rename/refactor of DownloadV2Checkpoint.
src/workspace/CloudSqlite.ts:93:48 - error TS2339: Property 'DownloadV2Checkpoint' does not exist on type 'typeof IModelJsNative'.                                                                                93     const downloader = new IModelHost.platform.DownloadV2Checkpoint({ ...db, ...props });

Pushed the change.

I'm not too familiar with the native addon side (yet, id like to be one day) to be of much help here. But one thing that does stand out is it looks like it's pulling in an outdated ver of core coming from the extension pre-release we did, due to how we have the peer deps setup.
Paulius mentioned pinning down peer deps in this thread, #2741 (comment), which might fix what you're seeing.

I'll work around it for now by casting to any - one of those errors is caused by the definition of TreeFlags having been updated on the imodel02 branch, which the addon can't see until we publish a new core-common, which can't happen until this addon is published.


Add magnetometer sensor support for Android

From #4010. Supersedes #4154.
Tested on my device.
Closes #3921.

Nice, kudos for keeping @fluffrabbit as author since you reused his work :)
Could you open an issue about implementing magnetometer support on iOS too, so that we don't forget about it?

@vnen you are a freaking hero! Thank you for bringing this to a conclusive end finally

Now lets make pokemon go on godot :D


Fix image filter on groups table

Description
Fix image filter on groups table
Fixes # (issue)
Type of change
What is it?

[x] Bug fix (non-breaking change which fixes an issue)
[ ] New feature (non-breaking change which adds functionality)
[ ] Documentation update
[ ] Tests update

Checklist:

[ ] I have commented my code, particularly in hard-to-understand areas
[ ] I have made corresponding changes to the documentation
[ ] I have added tests that prove my fix is effective or that my feature works
[x] New and existing unit tests pass locally with my changes
[x] I run npm run lint:js:fix to check that my code is properly formatted

Codecov Report

Merging #376 (09de0a2) into master (2c8f890) will decrease coverage by 0.08%.
The diff coverage is 0.00%.

@@            Coverage Diff             @@
##           master     #376      +/-   ##
==========================================
- Coverage   35.70%   35.62%   -0.09%
==========================================
  Files         116      116
  Lines        2498     2504       +6
  Branches      731      737       +6
==========================================
  Hits          892      892
- Misses       1468     1474       +6
  Partials      138      138

Impacted Files
Coverage Δ

src/Routes/Groups/GroupTable.js
35.29% <0.00%> (-7.57%)
:arrow_down:

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update 2c8f890...09de0a2. Read the comment docs.


Fixed bug for Creating New Item

Still missing live updates (will need to track state of the inventory table between the table and the individual components like these via props, will implement shortly).
Doesn't include Photo Upload yet (will implement once photo storage is ready)


New package: wl-std.WLDownloader version 1.3.7

Checklist for Pull Requests

[x] Have you signed the Contributor License Agreement?
[ ] Is there a linked Issue?

Manifests

[x] Have you checked that there aren't other open pull requests for the same manifest update/change?
[x] This PR only modifies one (1) manifest
[x] Have you validated your manifest locally with winget validate --manifest <path>?

[x] Does your manifest conform to the 1.6 schema?

Note: <path> is the directory's name containing the manifest you're submitting.

Microsoft Reviewers: Open in CodeFlow

Manual Validation ended with:

2024-07-22 16:58:35.784 [CLI ] Package hash verification failed. SHA256 in manifest [4c418face7523f9dbef0de4cd7cd8c22edb500569126beddc70a8884081cd848] does not match download [8f9ede5919b4b7a2554043d9ad32b16dd812debc2b8991f3af458d4293644d5f]

(Automated response - build 886.)


TR2 - Multiple Dragons

With environment mods, it's possible to get multiple dragons in Ice Palace (at least). This doesn't appear to cause the crashes that were originally seen with this scenario, but there should remain controls on dragon counts i.e. perhaps only in no restrictions mode.

Resolved under #315, #319, #322.


Development work for Footer

Separating out Footer work from #65

@machikoyasuda Should we move this to the "Home Page" milestone instead? I think that makes more sense since this was split off from #65 which is on the "Home Page" milestone.
I plan to merge in #87 either today or tomorrow, and if we move this ticket to "Home Page", we would be done with the "Careers & Job Page (phase 1)" milestone 😄


Change statuses text " ... max privacy" to "... maximum anonymity" and "... max privacy (offline)" to "... (offline)"

Updated statuses https://zpl.io/b6xJ58q

All statuses with "... max privacy" should be with "... (maximum anonymity)" in brackets!
 Now icons have a single line, not double as on maximum anonymity icons.

For transactions sending and sent to own address statuses should be:
sending to own address (maximum anonymity)
sent to own address (maximum anonymity)
sending to own address (offline)
sent to own address (offline)

Compact view: status “sending to own address (maximum anonymity) doesn’t fit into the field

Icons for all offline transactions should have a single line, not double

For offline transaction with “waiting for receiver” status (vouchers request) status should be “waiting for receiver (offline)” and icon should have a single line

fixed with af084503f6442f81a8a04c21359a39c52bcf2881

Place the status in one line if it's fit

Checked


[Substrait to Velox] Support list literal

This PR supported the conversion from Substrait list literal to Velox constant expression. Added a round-trip test for array literal.

This PR is a draft now and @zhejiangxiaomai will help improve this PR.

This PR is a draft now and @zhejiangxiaomai will help improve this PR.

Yes, will do it this week.

Hi, @mbasmanova  Could you please take a look at this PR?  I rebase it just now. Thanks.

Hi, @mbasmanova I fix all comments now, could you please review again?

Hi, @mbasmanova would you please review this PR again?
The major change is add "null value in array" case in arrayLiteral.

Hi, @mbasmanova  sorry for the late reply. I iterate this PR.
There are several major updates：

use template function  toSubstraitNotNullLiteral to avoid twice type cast problem you mentioned before. https://github.com/facebookincubator/velox/pull/2450#discussion_r1003350739
use addNode to constuct array for UT test. https://github.com/facebookincubator/velox/pull/2450#discussion_r1003349360
clean some unuseful codes.

Could you please review again when you are free?

Hi, @mbasmanova. Sorry for late reply here. Actually, I've been thinking about how to implement multi-nested array. And I copy the style of fuzzArray function to implement it.
The current idea is:

Velox to Substrait:  every innerArray  will translate to inner List here.
Substrait to Velox:

use post-order traversal listLiteral to get sizes and type.
create a FlatVector(BaseVector), and set value. here
restore the whole nestedArrayVector by ConstructInnerArray Function.

I don't add assertPlanConversion for nested array case because duckdb can not create table by array data set. here And If you think this PR is too big to review, I will separate this PR to 2 commits. one is for normal array. another is for nested array.

Hi @mbasmanova, Could you please review this PR?

Hi, @mbasmanova would you review this PR again?

Hi, @mbasmanova Long time no see. would you have a look at this again when you have time?

Hi @mbasmanova , thank you for your review. I fixed all comments except dictionary encoding. The key change is move some functions into anonymous namespace.  would you have a look?
Maybe I can support dictionary encoding in next another PR？

Hi, @mbasmanova All checks have passed. would you merge this PR?

Hi, @mbasmanova  really sorry for my late reply. I was assigned some tricky tasks the other day and delayed this PR.
I fixed all comments include implementing Timestamp and Date and IntervalDayTime.
But I encountered some problems when verifying Timestamp/IntervalDayTime at ut test.
I would like to ask you for some suggestions.
Question : for TIMESTAMP type
When I use "array['1970-01-02T10:17:36.000123000'::TIMESTAMP]," , I will got error info as shown in below. I think it's caused by my wrong SQL, but I tried a lot and still can't solve it.  Example UT code
unknown file: Failure
C++ exception with description "INTERNAL Error: Unimplemented template type for Value::GetValue" thrown in the test body.

@zhejiangxiaomai Thank you for the update. Re: timestamp - do you have access to a debugger? Can you use it to debug this issue?

Thank you for the update. Re: timestamp - do you have access to a debugger? Can you use it to debug this issue?

Yes I use gdb to debug it. Haven't found the reason yet.

Hi @mbasmanova  would review again. I added timestamp test just now.


Remove make prepare target

As per the upgrade to go modules and https://github.com/edgexfoundry/edgex-go/issues/1275 we should confirm that the make prepare step isn't used by the jenkins jobs for this repo and remove the prepare target from the Makefile

Fixed.

@tingyuz what PR was this fixed with?

It is within #66. Reopen to track the status.

since #66 is merged, close this issue.


api: split at package level instead of graph level

What this PR does
This PR reworks api/explorer/api/ingest.py so that it exposes a new function (ingest_nix_package) to ingest an individual package instead of the whole graph at once. The previous function (ingest_nix_graph) remains there, as it still the one used in production, but it now uses ingest_nix_package under the hood.
This is the first step towards streaming ingestion at the package level (see @GuillaumeDesforges's request for this feature here on Slack).
How to trust this PR
Execute tests of API:
cd api
nix develop
poetry run pytest

Witness the tests still pass (and they do test this code, because an earlier version of this PR was failing the tests)
Note that I wanted to compare the state of the app before/after this PR locally, by using docker-compose --profile all up and playing with the app at localhost:9091 but I believe in both cases the app is empty and I could not find how to feed nixpkgs to it. Any pointer welcome!

@zz1874> yes I agree and that concerned me too, but I didn't change it in this PR, because I inherited this state from the previous organization of the code. But I'd be happy to change the multiprocessing code after this PR is merged.
My proposition would be to split the packages in a number of chunks equal to the number of processors, and send each chunk for parallel processing. Does that seem like a desirable follow-up PR to you? If yes I will do it after this one is merged.

I'm worried about "pending edges".
Upon edge addition, we could directly create all the nodes we need, because the ouputPath is a unique ID.

I'm worried about "pending edges".
Upon edge addition, we could directly create all the nodes we need, because the ouputPath is a unique ID.

@GuillaumeDesforges> I don't see how we could create the destination node. Right now we create nodes with safe_parse_package (of type model.Package -> graph.Package). Could we create a graph.Package value in the position where I'm right now adding a pending edge? Just looking at the available functions typing-wise, I don't think so. What would you suggest?

Indeed what I want needs some update in the database model.
Basically a node representing a derivation should have only "output_path" as a required field. All other fields should be optional.
This way you could create such a node when iterating over edges, because source and sink are identified by the output path.
Then later when data about this derivation (matching the output path) is met, the other fields can be updated.
What really matters, as the backbone of the graph, is that we have for sure the whole graph extracted in terms of output paths and their dependencies. From there, the other fields are here to enrich the graph.

@GuillaumeDesforges> Thanks for the additional explanation, I understand your plan now and agree with it :+1:


how to train on my dataset

Hi @manvirvirk
If you want to run training on your dataset, you should be able to do this by implementing pytorch Dataset interface and replace BrainSegmentationDataset with it.

i want to run training on simple dataset of retina images. can you please elaborate in detail???

hi , I will be grateful if you can help me with little more details. How to
do the above mentioned changes? Thanks
On Thu, Mar 19, 2020 at 8:41 PM mateuszbuda<EMAIL_ADDRESS>wrote:

Hi @manvirvirk https://github.com/manvirvirk
If you want to run training on your dataset, you should be able to do this
by implementing pytorch Dataset interface and replace
BrainSegmentationDataset
https://github.com/mateuszbuda/brain-segmentation-pytorch/blob/8ef2e2d423b67b53ec8113fc71a9b968bb0f66e7/dataset.py#L12
with it.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
https://github.com/mateuszbuda/brain-segmentation-pytorch/issues/13#issuecomment-601234048,
or unsubscribe
https://github.com/notifications/unsubscribe-auth/ANAEAQFTTAUSC6HQQDP5XGTRIIYZHANCNFSM4LPKQXTQ
.

Here is a tutorial on how to implement custom dataset in pytorch: https://pytorch.org/tutorials/beginner/data_loading_tutorial.html

i want to run on my database which contains 20 images and respective masks. What should be the dataset directory?


[JAV-435] starter to register spring cloud applications to service center

…nter
Follow this checklist to help us incorporate your contribution quickly and easily:

[x] Make sure there is a JIRA issue filed for the change (usually before you start working on it).  Trivial changes like typos do not require a JIRA issue.  Your pull request should address just this issue, without pulling in other changes.
[x] Each commit in the pull request should have a meaningful subject line and body.
[x] Format the pull request title like [JAV-XXX] Fixes bug in ApproximateQuantiles, where you replace JAV-XXX with the appropriate JIRA issue.
[x] Write a pull request description that is detailed enough to understand what the pull request does, how, and why.
[x] Run mvn clean install to make sure basic checks pass. A more thorough check will be performed on your pull request automatically.
[x] If this contribution is large, please file an Apache Individual Contributor License Agreement.

Coverage decreased (-0.03%) to 86.907% when pulling 6ccb2ffe90aee8a000a58a737e4288504ecaa46b on acsukesh:master into b162b317e0024f27053010a8aa018f265ccc2515 on ServiceComb:master.

Coverage decreased (-0.04%) to 86.895% when pulling e1b51fbab693d553b08a0d1f3faedd13692a0167 on acsukesh:master into b162b317e0024f27053010a8aa018f265ccc2515 on ServiceComb:master.

Coverage increased (+0.02%) to 86.958% when pulling 6c3e7c750f85e28b0e7cb9999a47b97253a030d6 on acsukesh:master into b162b317e0024f27053010a8aa018f265ccc2515 on ServiceComb:master.

Coverage increased (+0.007%) to 86.945% when pulling 9ebe6ab4fb6b841a99b18da76340b77d9b9e2b2b on acsukesh:master into b162b317e0024f27053010a8aa018f265ccc2515 on ServiceComb:master.

Coverage decreased (-0.07%) to 86.864% when pulling 53ffe9988b2894de22d1d81ae7245e5c62ebd252 on acsukesh:master into b162b317e0024f27053010a8aa018f265ccc2515 on ServiceComb:master.


Add Feedback form link

Maybe just "Feedback" instead of "Give Feedback"?

Maybe just "Feedback" instead of "Give Feedback"?

Sure works for me

I like just "Feedback" - also, add a tiny bit more space on the right rail, e.g., move text a bit over to the left.

Alternatively, what about the Boostrap envelope glyphicon?
https://getbootstrap.com/docs/3.3/components/
first row, second from right?

Updated with just "Feedback", not sure if any of the icons really indicates feedback clearly

I like just "Feedback" - also, add a tiny bit more space on the right rail, e.g., move text a bit over to the left.

did you mean the alignment? I just didn't screenshot the full page

I like just "Feedback" - also, add a tiny bit more space on the right rail, e.g., move text a bit over to the left.

did you mean the alignment? I just didn't screenshot the full page. new screenshot is of the entire page

Ah okay!


Specifically passing in empty arrays to methods expecting params Arrays to fix build break on corefx caused by upgrade on buildtools.

Specifically passing in empty arrays to methods expecting params Arrays to fix build break on corefx caused by upgrade on buildtools.
Whenever a method expects:
public void myMethod(params object[])...

and nothing gets passed in, the compiler will by default add a:
Array.Empty<object>()

which is not supported on frameworks lower than 4.6 or Mono msbuild. Given a recent change in buildtools to consume the nuget package "Microsoft.Build.Tasks.Core" (version: 0.1.0-preview-00005), builds in Razzle (uses .Net Framework 4.5) and linux are broken when trying to consume buildtools. The ask is to submit this code to generate a new package for Microsoft.Build.Tasks.Core and then reference that back from buildtools so that the build break is fixed.

cc @weshaggard @dsplaisted

It sucks that we have to do this but I don't see any easier way to do it right now. Thanks for fixing this @joperezr.
LGTM

@dotnet-bot test this please

Just trying to make sure I understand the situation:
There's a Roslyn optimization for assemblies that target 4.6+ that is (in #173) getting misapplied because MSBuild "rounds up" the reference assemblies.  Here it's applied because the .NET Core reference assemblies have Array.Empty<T>, but then that's failing when run on a .NET 4.5 host, meaning we can't produce a single assembly that's compatible with full-framework 4.5 AND .NET Core, because to target .NET Core we have to use fancy new reference assemblies that trigger the optimization, and to target 4.5 we have to use old ones that don't trigger it, and that's unresolvable.
So yeah, this seems like the best option for now.  Should we ask the Roslyn folks to reevaluate that optimization in light of this?
If that understanding is correct, LGTM.

It looks like the test failures are related to this change:
09:02:16      FailedResXReaderWithAllOutputResourcesSpecified [FAIL]
09:02:16         Microsoft.Build.Shared.InternalErrorException : MSB0001: Internal MSBuild Error: Invalid resource parameter type, was System.Object[]
09:02:16         Stack Trace:
09:02:17            d:\j\workspace\Microsoft_msbuild\innerloop_prtest\src\Shared\ErrorUtilities.cs(60,0): at Microsoft.Build.Shared.ErrorUtilities.ThrowInternalError(String message, Object[] args)
09:02:17            d:\j\workspace\Microsoft_msbuild\innerloop_prtest\src\Shared\ResourceUtilities.cs(263,0): at Microsoft.Build.Shared.ResourceUtilities.FormatString(String unformatted, Object[] args)
09:02:17            d:\j\workspace\Microsoft_msbuild\innerloop_prtest\src\Shared\TaskLoggingHelper.cs(267,0): at Microsoft.Build.Utilities.TaskLoggingHelper.FormatString(String unformatted, Object[] args)
09:02:17            d:\j\workspace\Microsoft_msbuild\innerloop_prtest\src\Shared\TaskLoggingHelperExtension.cs(114,0): at Microsoft.Build.Tasks.TaskLoggingHelperExtension.FormatResourceString(String resourceName, Object[] args)
09:02:17            d:\j\workspace\Microsoft_msbuild\innerloop_prtest\src\Shared\TaskLoggingHelper.cs(509,0): at Microsoft.Build.Utilities.TaskLoggingHelper.LogMessageFromResources(MessageImportance importance, String messageResourceName, Object[] messageArgs)
09:02:17            d:\j\workspace\Microsoft_msbuild\innerloop_prtest\src\Shared\TaskLoggingHelper.cs(489,0): at Microsoft.Build.Utilities.TaskLoggingHelper.LogMessageFromResources(String messageResourceName, Object[] messageArgs)
09:02:17            d:\j\workspace\Microsoft_msbuild\innerloop_prtest\src\XMakeTasks\GenerateResource.cs(2476,0): at Microsoft.Build.Tasks.ProcessResourceFiles.ProcessFile(String inFile, String outFileOrDir)
09:02:17            d:\j\workspace\Microsoft_msbuild\innerloop_prtest\src\XMakeTasks\GenerateResource.cs(2340,0): at Microsoft.Build.Tasks.ProcessResourceFiles.Run(TaskLoggingHelper log, ITaskItem[] assemblyFilesList, List`1 inputs, List`1 satelliteInputs, List`1 outputs, Boolean sourcePath, String language, String namespacename, String resourcesNamespace, String filename, String classname, Boolean publicClass, Boolean extractingResWFiles, String resWOutputDirectory)
09:02:17            d:\j\workspace\Microsoft_msbuild\innerloop_prtest\src\XMakeTasks\GenerateResource.cs(771,0): at Microsoft.Build.Tasks.GenerateResource.Execute()
09:02:17            GenerateResource_Tests.cs(1405,0): at Microsoft.Build.UnitTests.GenerateResource_Tests.InProc.TransformationErrors.FailedResXReaderWithAllOutputResourcesSpecified()

I'm clossing this PR since we won't be doing this to fix the issue anymore. Instead, we have made some changes in the buildtools package to address this problem.

@joperezr Can you link the buildtools changes here?  Mostly I'm just curious.

Sure, basically we had to create two versions of the task library, one that runs in .Net Core, and one that targets .Net 4.5. This way, when we load buildtools in an environment that doesn't support 4.6 we will simply use the targets compiled for 4.5. The merged PR is: dotnet/buildtools#342


'A Vigilante's Tale' Stage 7 not progressing

'A Vigilante's Tale' stage 7 - 'Return to Xuman's Hideout' would not trigger when tested.

If this is related to https://github.com/ObliqueNET/Server/issues/847 , please confirm and close this ticket, leaving only https://github.com/ObliqueNET/Server/issues/847 open. Make sure to test separately, could be just a mistype in 'A Vigilante's Tale'.

If this is related to https://github.com/ObliqueNET/Server/issues/847 , please confirm and close this ticket, leaving only https://github.com/ObliqueNET/Server/issues/847 open. Make sure to test separately, could be just a mistype in 'A Vigilante's Tale'.

"A Vigilante's Tale" doesn't make you return to Xuman's Hideout. Either you're talking about step 17 of "Vengeance at its Finest", or "For the Greater Good" 's step 13. Both work perfectly.

"A Vigilante's Tale" doesn't make you return to Xuman's Hideout. Either you're talking about step 17 of "Vengeance at its Finest", or "For the Greater Good" 's step 13. Both work perfectly.


Is it possible to suppress the anonymous collection input during 'npm run scully'

🧩 Feature request
Description
 A clear and concise description of the problem or missing capability...
While running npm run scully
><EMAIL_ADDRESS>scully /xxxx/scully
> scully

 ☺   new Angular build imported
⠙ Background servers already running.
Would you allow Scully to collect anonymous errors to improve our services? (Y/n)n

We are asked to choose if we want to allow anonymous error collection. Is there a way to disable that option, since im running in an automated fashion.
Describe the solution you'd like
 If you have a solution in mind, please describe it.
Is it possible to pass arguments to scully, to suppress the message. Eg, scully --disable-error-collection
Describe alternatives you've considered
 Have you considered any alternative solutions or workarounds?

@koshyviv You can use the --noPrompt flag, and there will be no user-prompt and no error collection being done.
I'll double-check the docs if this is listed there.


How to keep toast and DropdownMenu open while processing a request?

If I click on a ToastAction to make a request in my database, the toast disappears immediately. I'm trying to keep the toast open until the request is completed.
In the same vein, I have a DropdownMenu with buttons to make requests in my database, but after clicking on one of them, the DropdownMenu closes without waiting for the request to be completed.
Thank you in advance!

Have you tried setting manually the open and onOpenChange while you are waiting for a response from the database?
You can find the api documentation here: https://www.radix-ui.com/primitives/docs/components/toast#root https://www.radix-ui.com/primitives/docs/components/dropdown-menu#api-reference
Many shadcn/ui components are extending the Radix UI components, so check out their docs too

This issue has been automatically closed because it received no activity for a while. If you think it was closed by accident, please leave a comment. Thank you.

This issue has been automatically closed because it received no activity for a while. If you think it was closed by accident, please leave a comment. Thank you.

+1


Add support for axios 0.22.* 0.23.* and 0.24.*

Current axios version is now 0.23.0

I'm not confident with 0.23.0 yet as i'm not sure if the 'breaking changes' section of the axios change log affects the cache adapter in any way

The 0.23.0 release only seem to include TS type declarations updates and test improvements. 0.24.0 is also only type declarations according to the change log.

Thanks @pstenstrm I've updated the semver in the PR to >=0.21.1 <0.25.0 to include all of 0.23 and 0.24

Current version is 0.25, peerDependency constraint should be >= 0.21.1 <1 considering that none of them will have breaking changes should not be a reason to strictly block versions below 1.


Chart with data left Line and data right Barchart

Describe the isssue
I want to show my chart with LineChart data left and Bar chart with data right as below image. Have you any idea for this?
To ReproduceSteps to reproduce the

Hi @ducsilva 👋
Thanks for requesting this feature. It is now implemented in the latest version of the library. Please use version 1.3.8 of react-native-gifted-charts
I have added support for a new property named isSecondary in the lineConfig object. Setting isSecondary to true results in making the line correspond to the secondary Y axis.
Here's an example-

The code for the above chart is-
const data = [10, 20, 30, 20, 15, 12];
const lineData = [12, 5, 8, 15, 8, 10];

return (
  <View style={{backgroundColor: 'black'}}>
    <BarChart
      data={data.map(i => {
        return {value: i};
      })}
      showGradient
      frontColor={'#8BC9CB'}
      gradientColor={'#07A0A3'}
      barBorderTopLeftRadius={4}
      barBorderTopRightRadius={4}
      hideRules
      yAxisColor={'gray'}
      xAxisColor={'gray'}
      yAxisTextStyle={{color: 'gray'}}
      secondaryYAxis={{
        yAxisLabelPrefix:'$'
      }}
      showLine
      lineData={lineData.map(i => {
        return {value: i};
      })}
      lineConfig={{
        thickness: 4,
        color: 'yellow',
        hideDataPoints: true,
        isSecondary: true, // this is the newly added property
      }}
    />
  </View>
);


Updated for NetBeans 10

I've updated the instructions and setup to cover NetBeans 10.

thx for the contribution @HayoBaan

You're welcome! As it turns out NetBeans 11.0 is out now too and it comes with a proper installer (albeit “experimental” at this moment), so we will no longer be needing this in the future 😉
By the way, NetBeans 10.0 was not working well at my end at all, it kept (intermittently) complaining about nb-javac not being there (even though it was installed). NetBeans 11.0 solved that at least so that's what I will be using from now on.


Not working On connection listner in android

I want to get total live connection in current session so i am using below interface but this is not working..
mSession?.setConnectionListener(object :Session.ConnectionListener{
override fun onConnectionDestroyed(p0: Session?, p1: Connection?) {
totalConnection--
println("Connected users:::" + totalConnection)
}
        override fun onConnectionCreated(p0: Session?, p1: Connection?) {
            totalConnection++
           println("Connected users:::" + totalConnection)
        }
    })

Hi @chiragdabhi0007
Callbacks works as expected in the SDK. Problem might be anywhere else in your code.
Going to close the ticket. Please reopen it if you still are having the problem.
Please specify SDK version you are using.


CORE-3106: DROP INDEX has to be specify the tablename AND indexname

please see CORE-3106

It seems to be a general problem with the build-system!?


feat: duplicate selected/all saved objects UI

Description
duplicate selected/all saved objects UI
Screenshot
Testing the changes
Check List

[ ] All tests pass

[ ] yarn test:jest
[ ] yarn test:jest_integration

[ ] New functionality includes testing.
[ ] New functionality has been documented.
[ ] Update CHANGELOG.md
[ ] Commits are signed per the DCO using --signoff

Codecov Report
Attention: Patch coverage is 0% with 4 lines in your changes are missing coverage. Please review.

Project coverage is 55.39%. Comparing base (431fcdb) to head (f7c18b4).
Report is 1 commits behind head on workspace-pr-integr.

Files
Patch %
Lines

src/core/server/saved_objects/routes/copy.ts
0.00%
4 Missing :warning:

Additional details and impacted files
@@                   Coverage Diff                    @@
##           workspace-pr-integr     #305       +/-   ##
========================================================
- Coverage                67.04%   55.39%   -11.66%
========================================================
  Files                     3344     1193     -2151
  Lines                    65011    24121    -40890
  Branches                 10496     4060     -6436
========================================================
- Hits                     43588    13361    -30227
+ Misses                   18845    10118     -8727
+ Partials                  2578      642     -1936

Flag
Coverage Δ

Linux_
?

_1
?

_2
55.39% <0.00%> (ø)

_3
?

_4
?

Flags with carried forward coverage won't be shown. Click here to find out more.

:umbrella: View full report in Codecov by Sentry.
:loudspeaker: Have feedback on the report? Share it here.


Add support for Qtum

Description
Added support for send/receive Qtum coins.
Support exist for P2PKH and bech32.
Support for P2SH added but disabled due to Litecoin and Qtum have the same prefix for it, more description in the commits.
Support for smart contracts not added, due to no such functionalities in the trust wallet.
Tested only with unit tests.
Testing instructions
Run ./bootstrap.sh to execute the unit tests for Qtum.
Types of changes
New feature (non-breaking change which adds functionality)
Checklist

[ ] Prefix PR title with [WIP] if necessary.
[x] Add tests to cover changes as needed.
[ ] Update documentation as needed.

Can you please resolve the conflicts?

Sort prefix and rebased

@timemarkovqtum could you please confirm the derivation path is compatible with other mobile wallets? sorry for another rebase request, we just re-enabled iOS CI (for Swift 5)

@hewigovens  According to the source code check both Android and iOS Qtum wallets are using the derivation path used in Qtum Desktop Wallet m/88'/0'/
https://github.com/qtumproject/qtum-ios/blob/develop/qtum wallet/Spendable Controll Flow/Wallet/Wallet.m#L265
https://github.com/qtumproject/qtum-android/blob/develop/app/src/main/java/org/qtum/wallet/datastorage/KeyStorage.java#L108-L109

@timemarkovqtum what kind of standard are you guys following? It does not look like BIP44 standard to me.

The standards are inherited from Bitcoin:
https://github.com/bitcoin/bitcoin/blob/master/src/wallet/wallet.cpp#L271
There is just change from 0 to 88 in the first index.

why not use bip44 and slip44? 88 doesn't make any sense
https://github.com/bitcoin/bips/blob/master/bip-0044.mediawiki
https://github.com/satoshilabs/slips/blob/master/slip-0044.md
and looks like qtum supports segwit, how do you guys deal with bip84?

According to bip checks, the derivation scheme correspond to:
https://github.com/bitcoin/bips/blob/master/bip-0032/derivation.png
Bitcoin desktop wallet looks like is using that derivation, probably due to backward compatibility, that result in using other desktop wallets too.
BIP44, BIP49, and BIP84 are all based on BIP32.


e2e: enable ISS validation for Vault

Disabling ISS validation while configuring Kubernetes Auth for HashiCorp
Vault is not very nice. It seems that passing the 'issuer' option should
make it possible to keep ISS validation enabled.
See-also: https://github.com/external-secrets/kubernetes-external-secrets/issues/721#issuecomment-924896198

Show available bot commands
These commands are normally not required, but in case of issues, leave any of
the following bot commands in an otherwise empty comment in this PR:

/retest ci/centos/<job-name>: retest the <job-name> after unrelated
failure (please report the failure too!)
/retest all: run this in case the CentOS CI failed to start/report any test
progress or results

@nixpanic iic, this was already there and we removed it in between ? I could be wrong though.

How do you mean removed? #2203 added the disable_iss_validation option, this PR removes it in favour of passing the issuer parameter.

Thanks !


the knowledge attention mask had not implemente in the code

Hi, it's my honor to read your paper. I'm very interested in your work.
But I found the knowledge attention mask mentioned in the paper has not implemente in your code. I just found as followings:
def prepare_attention_matrix(self, kg, weights):         attention_mat = np.array((5,5))         return attention_mat
I don't know whether it was my negligence or what the reason was. I hope to receive your reply. Thanks.

I'm very sorry for replying to your reply so late, because I'm busy taking the postgraduate entrance examination these days.
I will review the paper and code again after I finish this period of work.