Converter fails when using a channel first model and batchnorm

System information

OS Platform and Distribution: Linux Ubuntu 18:04
TensorFlow installed from: binary (mini conda)
TensorFlow version: 2.2

Command used to run the converter or code if you’re using the Python API
#  A VERY SIMPLE TWO LAYER MODEL WHICH FAILS ONLY WHEN CHANNELS FIRST

DATA_FORMAT = "channels_first"
backend.set_image_data_format(DATA_FORMAT)
img_inputs = keras.Input(shape=(3, 384, 384))
x = layers.Conv2D(8, kernel_size=3, strides=1, padding='same', use_bias=False, data_format=DATA_FORMAT,
                  activation=None, name='conv')(img_inputs)
x = layers.BatchNormalization(axis=1, epsilon=1e-3, momentum=0.999, name='BN')(x)
model = keras.Model(inputs=img_inputs, outputs=x, name="channel_first_test")
model.summary()

converter = tf.lite.TFLiteConverter.from_keras_model(model)

# fails with and without these next options, (but without even just Conv2D also fails with channels first)
converter.target_ops = [tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS]
converter.allow_custom_ops=True
converter.experimental_new_converter =True

tflite_model = converter.convert()

The output from the converter invocation

2020-09-30 17:26:45.007381: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2020-09-30 17:26:45.037941: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-09-30 17:26:45.038216: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce RTX 2080 Ti computeCapability: 7.5
coreClock: 1.65GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s
2020-09-30 17:26:45.038315: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-09-30 17:26:45.039124: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-09-30 17:26:45.039929: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2020-09-30 17:26:45.040053: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2020-09-30 17:26:45.040893: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2020-09-30 17:26:45.041375: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2020-09-30 17:26:45.043242: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-09-30 17:26:45.043302: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-09-30 17:26:45.043595: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-09-30 17:26:45.043841: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2020-09-30 17:26:45.044038: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
2020-09-30 17:26:45.047755: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency:<PHONE_NUMBER> Hz
2020-09-30 17:26:45.048058: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x557076b6a340 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-09-30 17:26:45.048068: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2020-09-30 17:26:45.048152: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-09-30 17:26:45.048409: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce RTX 2080 Ti computeCapability: 7.5
coreClock: 1.65GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s
2020-09-30 17:26:45.048426: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-09-30 17:26:45.048433: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-09-30 17:26:45.048439: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2020-09-30 17:26:45.048445: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2020-09-30 17:26:45.048451: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2020-09-30 17:26:45.048456: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2020-09-30 17:26:45.048462: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-09-30 17:26:45.048488: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-09-30 17:26:45.048749: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-09-30 17:26:45.048988: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2020-09-30 17:26:45.049004: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-09-30 17:26:45.104115: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-09-30 17:26:45.104128: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0
2020-09-30 17:26:45.104132: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N
2020-09-30 17:26:45.104216: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-09-30 17:26:45.104484: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-09-30 17:26:45.104733: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-09-30 17:26:45.104974: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9708 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2080 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5)
2020-09-30 17:26:45.106002: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55707a4518d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2020-09-30 17:26:45.106009: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): GeForce RTX 2080 Ti, Compute Capability 7.5
Model: "channel_first_test"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
input_1 (InputLayer)         [(None, 3, 384, 384)]     0
_________________________________________________________________
conv (Conv2D)                (None, 8, 384, 384)       216
_________________________________________________________________
BN (BatchNormalization)      (None, 8, 384, 384)       32
=================================================================
Total params: 248
Trainable params: 232
Non-trainable params: 16
_________________________________________________________________
2020-09-30 17:26:45.464048: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-09-30 17:26:45.464203: I tensorflow/core/grappler/devices.cc:55] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 1
2020-09-30 17:26:45.464263: I tensorflow/core/grappler/clusters/single_machine.cc:356] Starting new session
2020-09-30 17:26:45.464463: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-09-30 17:26:45.464599: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce RTX 2080 Ti computeCapability: 7.5
coreClock: 1.65GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s
2020-09-30 17:26:45.464618: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-09-30 17:26:45.464625: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-09-30 17:26:45.464631: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2020-09-30 17:26:45.464637: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2020-09-30 17:26:45.464643: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2020-09-30 17:26:45.464649: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2020-09-30 17:26:45.464655: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-09-30 17:26:45.464680: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-09-30 17:26:45.464824: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-09-30 17:26:45.464947: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2020-09-30 17:26:45.464961: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-09-30 17:26:45.464964: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0
2020-09-30 17:26:45.464966: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N
2020-09-30 17:26:45.465005: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-09-30 17:26:45.465150: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-09-30 17:26:45.465279: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9708 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2080 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5)
2020-09-30 17:26:45.466142: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:797] Optimization results for grappler item: graph_to_optimize
2020-09-30 17:26:45.466150: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:799]   function_optimizer: function_optimizer did nothing. time = 0.002ms.
2020-09-30 17:26:45.466152: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:799]   function_optimizer: function_optimizer did nothing. time = 0ms.
2020-09-30 17:26:45.474700: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-09-30 17:26:45.474845: I tensorflow/core/grappler/devices.cc:55] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 1
2020-09-30 17:26:45.474873: I tensorflow/core/grappler/clusters/single_machine.cc:356] Starting new session
2020-09-30 17:26:45.475055: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-09-30 17:26:45.475192: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce RTX 2080 Ti computeCapability: 7.5
coreClock: 1.65GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s
2020-09-30 17:26:45.475208: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-09-30 17:26:45.475214: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-09-30 17:26:45.475220: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2020-09-30 17:26:45.475225: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2020-09-30 17:26:45.475231: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2020-09-30 17:26:45.475237: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2020-09-30 17:26:45.475243: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-09-30 17:26:45.475267: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-09-30 17:26:45.475409: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-09-30 17:26:45.475530: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2020-09-30 17:26:45.475541: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-09-30 17:26:45.475544: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0
2020-09-30 17:26:45.475546: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N
2020-09-30 17:26:45.475583: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-09-30 17:26:45.475725: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-09-30 17:26:45.475852: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9708 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2080 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5)
2020-09-30 17:26:45.477438: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:797] Optimization results for grappler item: graph_to_optimize
2020-09-30 17:26:45.477447: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:799]   constant_folding: Graph size after: 9 nodes (-5), 8 edges (-5), time = 0.361ms.
2020-09-30 17:26:45.477450: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:799]   constant_folding: Graph size after: 9 nodes (0), 8 edges (0), time = 0.108ms.
Traceback (most recent call last):
  File "/home/m/PycharmProjects/test/model/tflite_test.py", line 43, in <module>
    tflite_model = converter.convert()
  File "/home/m/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/lite/python/lite.py", line 514, in convert
    result = _toco_convert_impl(
  File "/home/m/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/lite/python/convert.py", line 491, in toco_convert_impl
    data = toco_convert_protos(
  File "/home/mn/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/lite/python/convert.py", line 227, in toco_convert_protos
    raise ConverterError("See console for info.\n%s\n%s\n" % (stdout, stderr))
tensorflow.lite.python.convert.ConverterError: See console for info.
2020-09-30 17:26:46.253082: W tensorflow/compiler/mlir/lite/python/graphdef_to_tfl_flatbuffer.cc:144] Ignored output_format.
2020-09-30 17:26:46.253096: W tensorflow/compiler/mlir/lite/python/graphdef_to_tfl_flatbuffer.cc:147] Ignored drop_control_dependency.
2020-09-30 17:26:46.257083: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
2020-09-30 17:26:46.279406: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency:<PHONE_NUMBER> Hz
2020-09-30 17:26:46.279736: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x560c77ff6240 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-09-30 17:26:46.279747: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2020-09-30 17:26:46.280153: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2020-09-30 17:26:46.282831: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-09-30 17:26:46.282979: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce RTX 2080 Ti computeCapability: 7.5
coreClock: 1.65GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s
2020-09-30 17:26:46.283066: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-09-30 17:26:46.283843: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-09-30 17:26:46.284635: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2020-09-30 17:26:46.284757: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2020-09-30 17:26:46.285563: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2020-09-30 17:26:46.286029: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2020-09-30 17:26:46.287805: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-09-30 17:26:46.287859: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-09-30 17:26:46.288029: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-09-30 17:26:46.288155: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2020-09-30 17:26:46.288174: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-09-30 17:26:46.323961: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-09-30 17:26:46.323977: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0
2020-09-30 17:26:46.323981: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N
2020-09-30 17:26:46.324065: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-09-30 17:26:46.324234: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-09-30 17:26:46.324384: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-09-30 17:26:46.324525: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 105 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2080 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5)
2020-09-30 17:26:46.325513: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x560c78d5dc30 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2020-09-30 17:26:46.325522: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): GeForce RTX 2080 Ti, Compute Capability 7.5
Fatal Python error: Segmentation fault

Current thread 0x00007f70cdd84740 (most recent call first):
  File "/home/m/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/lite/toco/python/toco_from_protos.py", line 50 in execute
  File "/home/m/miniconda3/envs/tf/lib/python3.8/site-packages/absl/app.py", line 251 in _run_main
  File "/home/m/miniconda3/envs/tf/lib/python3.8/site-packages/absl/app.py", line 300 in run
  File "/home/m/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/platform/app.py", line 40 in run
  File "/home/m/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/lite/toco/python/toco_from_protos.py", line 93 in main
  File "/home/m/miniconda3/envs/tf/bin/toco_from_protos", line 11 in <module>
Segmentation fault (core dumped)

Process finished with exit code 1

Also, please include a link to the saved model or GraphDef
# Put link here or attach to the issue.

Failure details
Expect the tflite converter to work on channel-first models
RNN conversion support
If converting TF RNN to TFLite fused RNN ops, please prefix [RNN] in the title.
Any other info / logs
Same two layer test model successfully converts when set up as channel last
Removing the batchnorm layer succesfully converts with channel first.
However, even just a lone conv2d layer fails with channel first if I do not include the extra 'converter.' parameters

@PoseAI When I used tf-nightly, I was not able to reproduce the issue. Please check the gist here.
Please verify once and close the issue if this was resolved for you. Thanks!

hi, i confirm current tf-nightly resolves the issue.  hopefully conda upgrades soon as i try to avoid nightly builds.
thanks


Ensure embedded context exists in H5PIntegrations.contents

@sdreher I created a Makefile to make it easier to coral the various dependencies.  I updated package.json so that npm will manage the Bower and Gulp tasks within the project rather than depending on a user to install them globally.  You should be able to clone this and run make build.
Both Bower and Gulp should be removed in favor of into npm scripts, or a Webpack, but this should get us going for now.

We found that the patch needs to be on an upstream dependency; h5p itself.  I'll close this for now, and make a patch for the Makefile to pr h5p-standalone.


Filter by deviceID improvements

As a admin or dataviewer, I want to quickly select a deviceID to filter upon.
Couple ideas for filter by device ID:

Make table selectable (copy and paste doesn't work at the moment)
Dump available deviceIDs into a dropdown so a user can select

Dump available deviceIDs into a dropdown so a user can select

done in https://github.com/hotosm/OpenMapKitServer/commit/35360ff36e46b021582afae91d713c310af6a312


Windows Hello for Kiosk-like apps?

Is it possible to use Windows Hello to authenticate users in a kiosk app? I would like to achieve something similar to the greeting kiosk [1], but using Windows Hello instead of the cognitive services: The app runs fullscreen and uses Windows Hello to identify users, very similar to the Windows logon screen. When a user of the camera comes within reach, I would like to access his identity and navigate to further UI.
For that, I can assume that all users that will use the kiosk have an account that is set up with Windows Hello. However, the passport sample from this repo [2] always tries to authenticate the current user that is running the kiosk app. If a different user is seen in front of the camera, the camera detects ther user, this user is greeted ("Hello "), but the Windows Hello popup only allows to click the cancel button. Did I miss something or is this a scenario that simply is not supported by the current API?
Thanks for the help in advance
[1] https://github.com/Microsoft/Cognitive-Samples-IntelligentKiosk/blob/master/Documentation/GreetingKiosk.md
[2] https://github.com/Microsoft/Windows-universal-samples/tree/master/Samples/MicrosoftPassport

This is not an issue with a sample in this repo. You can open a support case with our Developer Support team:

Visit http://aka.ms/storesupport and sign-in with your Microsoft account
Under the “App Development” section choose "Windows 10 universal app development", then choose the appropriate problem type and category to get engaged through a formal support channel.


Out of memory when training with a TITAN X

I met with the OOM problem when training with a TITAN X graphic card. The log is as follows:
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (256): 	Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (512): 	Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (1024): 	Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (2048): 	Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (4096): 	Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (8192): 	Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (16384): 	Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (32768): 	Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (65536): 	Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (131072): 	Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (262144): 	Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (524288): 	Total Chunks: 1, Chunks in use: 0 905.2KiB allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (1048576): 	Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (2097152): 	Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (4194304): 	Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (8388608): 	Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (16777216): 	Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (33554432): 	Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (67108864): 	Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (134217728): 	Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (268435456): 	Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:660] Bin for 392.00MiB was 256.00MiB, Chunk State:
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a300000 of size 1280
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a300500 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a300600 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a300700 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a300800 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a300900 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a300a00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a300b00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a300c00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a300d00 of size 512
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a300f00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a301000 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a301100 of size 512
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a301300 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a301400 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a301500 of size 1024
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a301900 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a301a00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a301b00 of size 1024
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a301f00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a302000 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a302100 of size 1024
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a302500 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a302600 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a302700 of size 2048
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a302f00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a303000 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a303100 of size 2048
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a303900 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a303a00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a303b00 of size 2048
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a304300 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a304400 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a304500 of size 2048
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a304d00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a304e00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a304f00 of size 2048
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a305700 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a305800 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a305900 of size 2048
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a306100 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a306200 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a306300 of size 16384
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a30a300 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a30a400 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a30a500 of size 16384
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a30e500 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a30e600 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a30e700 of size 16384
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a312700 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a312800 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a312900 of size 16384
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a316900 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a316a00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a316b00 of size 2048
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a317300 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a317400 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a317500 of size 2048
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a317d00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a317e00 of size 4096
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a318e00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a318f00 of size 2048
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a319700 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a319800 of size 4096
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a31a800 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a31a900 of size 2048
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a31b100 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a31b200 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a31b300 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a31b400 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a31b500 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a31b600 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a31b700 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a31b800 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a31b900 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a31ba00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a31bb00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a31bc00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a31bd00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a31be00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a31bf00 of size 768
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a31c200 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a31c300 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a31c400 of size 2560
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a31ce00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a31cf00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a31d000 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a31d100 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a31d200 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a31d300 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a31d400 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a31d500 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a31d600 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a31d700 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a31d800 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a31d900 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x130a31da00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:687] Free at 0x130a31db00 of size 926976
I tensorflow/core/common_runtime/bfc_allocator.cc:693]      Summary of in-use Chunks by size:
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 73 Chunks of size 256 totalling 18.2KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 2 Chunks of size 512 totalling 1.0KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 768 totalling 768B
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 3 Chunks of size 1024 totalling 3.0KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 1280 totalling 1.2KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 10 Chunks of size 2048 totalling 20.0KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 2560 totalling 2.5KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 2 Chunks of size 4096 totalling 8.0KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 4 Chunks of size 16384 totalling 64.0KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:700] Sum Total of in-use chunks: 118.8KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:702] Stats:
Limit:                   198770688
InUse:                      121600
MaxInUse:                   121600
NumAllocs:                      97
MaxAllocSize:                16384
W tensorflow/core/common_runtime/bfc_allocator.cc:274] ************________________________________________________________________________________________
W tensorflow/core/common_runtime/bfc_allocator.cc:275] Ran out of memory trying to allocate 392.00MiB.  See logs for memory state.
W tensorflow/core/framework/op_kernel.cc:965] Internal: Dst tensor is not initialized.
E tensorflow/core/common_runtime/executor.cc:390] Executor failed to create kernel. Internal: Dst tensor is not initialized.
[[Node: zeros_6 = Constdtype=DT_FLOAT, value=Tensor<type: float shape: [25088,4096] values: [0 0 0]...>, _device="/job:localhost/replica:0/task:0/gpu:0"]]

excuse me, have you solved this problem? I have searched this problem and try some methods, but it does not work. would you mind telling the way to solve this method.


SpanRelationQuery with Regex

In Annis, it should be possible to search for relations using regex, see https://github.com/KorAP/Koral/issues/45

Added in https://github.com/KorAP/Krill/commit/f09fdd467bffec12581404fafbced29c149dacd8.


feat: Qdrant as a supported vector store

Description
This PR adds support for Qdrant - https://qdrant.tech/ to be used as a vector store.

Awesome! @Anush008 once we add this, would you be interested in doing a joint blog post?

Hi Zain. We can definitely come up with content to promote this.

@Anush008 I added tests and in the process of adding tests I found a couple of places where I had to make some minor changes. Could you review the changes that I made?


Twisted Error using 'pipenv install twisted

Upon trying to run scrapy, I got an error. reading through it, I discovered it required twisted. I installed it using "sudo pipenv install twisted", I got this terminal out. I am doing this on a chromebook using the native debian VM.

I ha the same problem in a ubuntu 16.04 crouton on a chromebook. This stackoverflow solution worked for me: https://stackoverflow.com/questions/31457009/error-installing-twisted-for-python
I had to run the following before installing twisted: sudo apt-get install python3-dev

Install .whl file from here and make sure the c3x version of python matches
https://www.lfd.uci.edu/~gohlke/pythonlibs/#twisted

Install C++ Build Tools from here (only select build tools, ~3GB): https://visualstudio.microsoft.com/downloads/#build-tools-for-visual-studio-2017

Download wheel files from the link jess posted
Make sure the filename matches your version of python in the (27, 33, 37) etc.

Run pip install [wheel.whl] where you downloaded the wheel

I am having a twisted bug on OSX 10.14.6. After entering my forked directory and running pipenv install --dev --ignore-pipfile --three . I try to run scrapy genspider chi_housing "Chicago Housing Authority" http://www.thecha.org and get the error ModuleNotFoundError: No module named 'twisted'.  I tried to run
pip install twisted  and
pip3 install twisted  outside of pipenv shell
pipenv install twisted  before going into pipenv.
All seemed to be successful but the error is still happening.
Any ideas what I should try?

Solved the problem for Mac OSX Mojave twisted error!  I attempted to deinstall all my versions of python and then reinstall python 3.7 by following these two links and it worked.

https://apple.stackexchange.com/questions/284824/remove-and-reinstall-python-on-mac-can-i-trust-these-old-references

https://stackoverflow.com/questions/3819449/how-to-uninstall-python-2-7-on-a-mac-os-x-10-6-4

Also reinstalled pipenv during the process (a licensing error came up). I am down to talk about this more if anyone else gets the same error.
SUMMARY: It had pretty much nothing to do with twisted. Seemed more to be an issue with my having weird linking and dependency stuff and I just had to delete all my python versions and reinstall the one I needed for this project.

Duplicated by #84


Addition of timeout function for urllib

Addition of timeout for urllib when loading in the help functionality from code.kx
- Default is set to 5 seconds
The default value can be changed by editing the Jupyter configuration file kernel.json and adding -timeout as a command line argument to JUPYTERQ_SERVERARGS. i.e "JUPYTERQ_SERVERARGS":"-timeout 10"
If an inappropriate value is given for timeout then the value will be changed to the default value and a message will be printed to the user letting them know.

This addition provides a programmatic resolution to issue #62

This addition provides a programmatic resolution to issue #62


A section on LCD artifacts may be useful

This would cover things such as blur/smearing/etc, including simple 2-frame blending effects, but also more complex behaviors. One of the more complex behaviors is as follows.
ISSOtm noticed an unexpected artifact in The Forest Hero, as described here:

Go into the hole shown at the beginning, then go up and down the stairs
My DMG shows a bunch of vertical lines for some reason

Based on the belief that it was due to the brick tiles effectively toggling pixels between black/white on alternating frames I created the following test ROM. If you press A when this ROM is running the stripped tiles will essentially be xor'd every other frame, leading to a similar effect, which gets worse as there's more white in a given column.
It's not quite the same as the game because this is 50:50 white/black, but I believe it's the same effect.
(FLASHING WARNING: You probably don't want to press A with this ROM without frame blending enabled in an emulator, as it will flash very quickly.):
vertical_smear.zip
LIJI produced mockups of the resulting effect on their GB Light with normal contrast:

as well as high contrast:

I'm unaware of an emulator which attempts to emulate this behavior at this time.

144p Test Suite's "Motion blur" test likewise shows a fadeout on DMG and MGB if you turn on stripes.


oneOf does not work on requestBody when oneOf is nested under #/components/schemas

Description
oneOf in schema does not work on requestBody when this oneOf is nested under other schema defined in #/components/schemas/.
Expected behaviour
oneOf shall work when it is nested under other schema defined in #/components/schemas/.
Actual behaviour
The body does not contain the block use oneOf.
Steps to reproduce
The following is python and schema used to reprocedure this issue.
When we send a post request to /bad endpoint with body {"oneof":{"name":"one"}}. The response becomes {} instead of {"oneof":{"name":"one"}}.
The response shall be the same as request because they use the same schema, and the request is not rejected due to schema error.
openapi: 3.0.0
info:
  title: title
  version: 0.0.0
paths:
  /bad:
    post:
      summary: test
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/nested'
      responses:
        200:
          description: OK
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/nested'
      operationId: test.post
components:
  schemas:
    nested:
      type: object
      properties:
        oneof:
          $ref: '#/components/schemas/oneof'
    oneof:
          discriminator:
            propertyName: name
            mapping:
              one: '#/components/schemas/one'
              two: '#/components/schemas/two'
          oneOf:
            - $ref: '#/components/schemas/one'
            - $ref: '#/components/schemas/two'
    one:
      type: object
      required:
        - name
      properties:
        name:
          type: string
          pattern: ^one$
    two:
      type: object
      required:
        - name
      properties:
        name:
          type: string
          pattern: ^two$

import connexion

def post(body):
    print('body = {}'.format(body))
    return body, 200, {}

app = connexion.App(__name__)
app.add_api('test.yml', strict_validation=True, validate_responses=True)
app.run()

Additional info:
Output of the commands:
$ python --version
Python 3.6.4+
$ grep connexion Pipfile
connexion = {git = "https://github.com/zalando/connexion", ref = "62582643cbfca65b0cf0d54755f81f0e1a7d1f82"}

Interesting! Thanks for filing the issue. I'll try to reproduce it and report back.

It looks like the problem is here:
https://github.com/zalando/connexion/blob/6aafd33e4dcece168d44f38a7d11ab623f2a1a8b/connexion/operations/openapi.py#L248
I'll need to think on this a bit. It seems like we need a way to get the "matched" schema's properties, but Ideally we could lean on the jsonschema library. I'll check if it has a feature like that and report back.

just adding more context...
we need to be able to successfully call
def _get_val_from_param(self, value, query_defn):
     query_schema = query_defn["schema"]

     if is_nullable(query_schema) and is_null(value):
         return None

     if query_schema["type"] == "array":
         return [make_type(part, query_schema["items"]["type"]) for part in value]
     else:
         return make_type(value, query_schema["type"])

when the query_defn (not a great variable name...) looks like this:
{
   "schema" : {
      "discriminator" : {
         "mapping" : {
            "two" : "#/components/schemas/two",
            "one" : "#/components/schemas/one"
         },
         "propertyName" : "name"
      },
      "oneOf" : [
         {
            "type" : "object",
            "properties" : {
               "name" : {
                  "pattern" : "^one$",
                  "type" : "string"
               }
            },
            "required" : [
               "name"
            ]
         },
         {
            "type" : "object",
            "required" : [
               "name"
            ],
            "properties" : {
               "name" : {
                  "type" : "integer"
               }
            }
         }
      ]
   }
}

The reason this breaks is because it fails to look up the type.
This is a challenge because we want to look up the type from the schema that successfully validated.
It seems like we need a way to collapse this defn by leveraging some jsonschema functionality before passing it in here.
Of course we don't usually need to make_type when the body is json, but I can imagine there is a case where this breaks with form_params. Maybe we can lean more heavily on coerce_type in validation...

@cziebuhr it sounds like you are working on this based on your comment in https://github.com/zalando/connexion/pull/789#issuecomment-442370836 (awesome)
Want me to assign this ticket it to you?

Seems to be a duplicate of #781. Please have a look at https://github.com/zalando/connexion/pull/789#issuecomment-442370836. This is another reason why I don't think that #789 will fix the issue for #781.

I don't think they are duplicates.
#781 is about supporting additionalProperties, this ticket is about supporting oneOf. The author of that ticket has already confirmed that #789 fixed their issued.
AFAICT In order to support oneOf we need to implement logic based on the discriminator field, which you mentioned that you were working on.

IMHO oneOf should work out of the box (without discriminator). The only reason it doesn't work is extra logic in the _get_body_argument function which I still try to understand. Can you bring some light into the dark why the function is totally different to swagger 2? Swagger 2 directly passes the body arg to the handler function, so 'free form' data should always works.

I am facing the same issue. Has anyone found a solution to this it ?

Bug is still unfixed... :(

Still unfixed

Anyone working on this?  I am having the issue as well and I'm willing to help fix this.

@jasonrhaas This really works fine. Thanks for help.

I am having the same problem, but the workaround from @jasonrhaas does not work
    get:
      operationId: api.aggregation.get_hourly
      responses:
        "200":
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/aggregated-data"
          description: aggregated measurement data of a vessel
      summary: Get measurement data for a single vessel for the last hour, aggregated
    parameters:
      - in: query
        name: measures
        description: ids of measure_points to return
        schema:
          example: [1,2,3]
          oneOf:
            - type: string
              enum: [all]
            - type: array
              items:
                type: integer

I am having the same problem.

Same problem for me as well

Please provide a workaround for the nested oneof if it is not to fix easily. Seems like the problem is open for a quite long time. Thanks!

@yagnasrinath it can be fixed easily via #1170 but nobody is interested in merging that pull request

using anyOf in the meantime...

Closed by #1170


[REQUEST] Pokecube Compat

Describe the solution you'd like
Hi, have you considered adding tall doors for Pokecube AIO? If you'd prefer, I could see about providing the textures since I am the one who made the textures for the doors (and most items & blocks) in Pokecube.

Hello! Thank you for reaching out to me. I'm willing to add the tall version of the doors and I wouldn't mind being sent the textures too! The more mods supported, the better! :D

Awesome! I'll see if I have any time next week to make some textures during my spring break

Hi! I finally finished up the textures today. :)
Only thing to note is that I plan to eventually retexture the corrupted door, since it wasn't made by me and is currently just a recolor of the warped door.
dramatic_doors_pokecube_compat.zip

Thank you! I will still look to futureproof the mod and possibly add in the next release.

Sounds good!

The compat is now implemented! Now if only Pokecube could be updated for 1.18.2.


Some (lots) of items disappear in internet explorer

Very strange: some items just disappear completely in internet explorer. Don't know exactly why, but it seems to be related to the zOrder property.
The elements disappearing al have a zOrder lower then ~180, elements with a higher zIndex seem to work. (I'm calculating zIndex by distance from the bottom of the screen)
I've attached two screenshots, one in chrome and another, from the same game, in IE (11).

Anyway, I don't have more information right now, I'll probably look further into it later. (Maybe I'm doing something strange, I dunno)

I've looked further into this issue and it seems that the cause was sprite.cacheAsBitmap = true;
I only had this on elements with zOrder, so I wrongly assumed it had to do with that.
So I'm closing the issue. Sorry for bothering.

Hello! Sorry for late answer :) I happy that you resolved it


Add option to lock button in place

Is your feature request related to a problem?

Describe the solution you'd like.
A config option to enable/disable dragging of the authentication button.
Describe alternatives you've considered.
None.
Agreements

[X] I have searched for and ensured there isn't already an open issue regarding this.
[X] I have ensured the feature I'm requesting isn't already in the latest supported mod version.

Other
No response

Fixed cb72cdb6e37a01cd20a581a107b3fbe96ffd9c70


Step 12 Minor Tweek

Describe the Issue
The changes to Step 12 on Cat Photo App seem to be paying off.
One last common issue seems to be the period inside of the anchor text. Should we add a dynamic hint?
Affected Page
https://www.freecodecamp.org/learn/2022/responsive-web-design/learn-html-by-building-a-cat-photo-app/step-12
Your code

CatPhotoApp

Cat Photos

Click here to view more  cat photos.

      link to cat pictures

Expected behavior
Clear hint indicating to remove the period.
System
All

Why do we need to add extra information, as per instruction cat photos is without period(.).

which option do you want?

clear hint to remove the period
accept a link text with a period at the end

Ive done #2

We haven't decided yet

Change the test so it so it doesn't matter where the period is.

Sure no problem, is this open to working on it or not? I've been told the
issue is not open to accepting pull requests
On Wed, Oct 5, 2022, 8:13 PM Tom @.***> wrote:

Change the test so it so it doesn't matter where the period is.
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https://github.com/freeCodeCamp/freeCodeCamp/issues/47854#issuecomment-1269249765,
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@.***>

There is a label when its ready for contribution.
I don't think we care about the period, so I'm fine making the test more flexible and letting the period be optionally included.

I closed the PR because having this discussion in a PR tends to pull the contents of the changes back and forth as the discussions work towards consensus.
WWe have three consistent votes for the second, to accept the period inside or outside of the anchor, but in a consistent location. Placing the period in a location that turns the words into gibberish should not be allowed.
I generally like restructuring the sentence to avoid this issue, but I don't see a clean way to do that here.

I am all for keeping programming what it is - absurdly specific. So, I like the suggestion to make the hint dynamic/specific.
Either, have a test specifically looking for a . within the textContent. Or, use a dynamic hint to say "expected X, but found Y".

I am all for keeping programming what it is - absurdly specific. So, I like the suggestion to make the hint dynamic/specific.
Either, have a test specifically looking for a . within the textContent. Or, use a dynamic hint to say "expected X, but found Y".

The user can either have a period at the end of the link or not. and display a dynamic hint otherwise. Is the work I did good?
problem is equal only take two parameters I cant do equal(userInput, "cat photos"||"cat photos")
should we change modify this:
https://github.com/freeCodeCamp/freeCodeCamp/pull/44279/files/8a535e7489b96754df62a7e3e8879ec1689c3c62

The user can either have a period at the end of the link or not. and display a dynamic hint otherwise.

No. We specifically want to make sure Campers do not include a period in the link.

My suggestion to...

Change the test so it so it doesn't matter where the period is.

comes because I recall a very similar conversation on another issue. My original position in that conversation was similar to yours @ShaunSHamilton - programming doesn't allow typos, so why would we? But I was overruled because we don't want people to get stuck in spots like this. They completed the main learning task of adding the anchor element, but just misplaced a period - so we decided to be more flexible in spots like this. I really don't care in this instance, it's pretty minor - but that was the result of that discussion and my reasoning for the suggestion.

@moT01 I suppose that is a fair point. My reason for sticking with requiring specificity is we have the ability to say: "We expected some text, but you put some text.".
We could even go as far as pointing out the diff.

If instruction provided says some text, I don't think we should allow some text.

Just throwing out another option that I alluded to above. Reword the sentence to something like:
"View more cat photos from our collection."
And have the link text be "more cat photos". This gets rid of the awkward "Click here" at the beginning of the sentence, which if you think about it, the "Click here" should be the link. But really, "Click here" is so mid-90s early internet. We shouldn't be encouraging it.

We could even go as far as pointing out the diff.

Very cool, i'd like to do it, does this feature currently exist somewhere in fcc? if not, it would be a seperate feature request and have its own issue yes?

If instruction provided says some text, I don't think we should allow some text.

Agreed, I think it is enough for most users who have made it this far to figure out what needs to be done in order to pass the challenge:

Agreed, I think it is enough for most users who have made it this far to figure out what needs to be done in order to pass the challenge:

Not sure what you mean by "made it this far".  The test is in a particular order so that the correct hint is given.  I would just stick with creating the hint, so that it says  "We expected some text, but you put some text." as @ShaunSHamilton suggested.  Creating a diff seems unnecessary because you can just tell the user what they have and what is expected without creating a diff.

Ok, I had referenced this. I hope it solves it.
https://github.com/freeCodeCamp/freeCodeCamp/commit/25078213a76767e789a2c6a7f1592e90f16840ed
then it was suggested "We could even go as far as pointing out the diff." that is why I thought that what I had done was perhaps not enough.

@KravMaguy It is advisable to create PR for issue with label help wanted.
Read contribution guideline for more info.

Just throwing out another option that I alluded to above. Reword the sentence to something like:

"View more cat photos from our collection."

I think this is a bit more work but ultimately a better fix.

Let's change it to: See more cat photos in our gallery. please follow Jeremys comment above.


Use Image Viewer for selection of Upload Image

Currently an internal file selection dialog opens when selecting an image to upload. This is cumbersome because this dialog doesn't have the ordering mode "date taken", can't order asc or desc and doesn't remember the last selected order mode.
Would be nice if a more gallery like dialog/app would open. Like Simple Gallery
(A gallery for viewing photos and videos without ads.
) - https://f-droid.org/app/com.simplemobiletools.gallery

Hey @realthargor. Actually, it opens whatever image picker dialog your manufacturer has set as the default on your phone (code). The image picker on Google's version of Android does have a way to sort by ascending/descending order of time:

Closing, as there isn't much I can do here short of implementing a custom image picker, for which the cost-benefit doesn't work out.

Hi @vickychijwani
thanks for the answer.
I looked at it a bit myself. Would it not be okay to use a imagepicker library or just use another intend like this:
Intent galleryIntent = new Intent(Intent.ACTION_PICK,	             android.provider.MediaStore.Images.Media.EXTERNAL_CONTENT_URI);    startActivityForResult(galleryIntent, RESULT_LOAD_IMG);
30
}
Or is that what you mean by implementing a custom image picker?

So, the piece of code you have will work only for the gallery and some apps - it won't work correctly for picking images from Dropbox, Google Photos, and/or other apps that expose their content in the image picker and download it on-demand.
I'm also not inclined to use a library as this (picking images) is a first-class concept on Android by default, so the default picker is likely to be better integrated with the rest of the system and likely to work better overall.

Makes sense. Thanks for explaining, I learned a lot. :)


preload="none" not working

Hi,
Whether I set preload="none" or preload="auto", the video is reloaded before user press play button, on Chrome.
I test this preload on videojs and it works accordingly so  I don't think it's browser issue.
Thanks.

Hi, I'm not an expert, I'm just a simple user, I'll tell you what works for me. You just have to change from preload = "none" to preload: false, as simple as that, I hope it works for you.

this doesn't work. It completely ignores preload

Can you paste here the complete code you are using for the player?

I'm quite an advanced user and have a wrapper for the player..
but my config is as follows
var myFP=fluidPlayer('"+rdid+"',{layoutControls:{fillToContainer:false,primaryColor:\"#D3D3D3\",posterImage:false,autoPlay:false,preload:false,playButtonShowing:true,playPauseAnimation:true,mute:false,logo:{imageUrl:null,position:'top left',clickUrl:null,opacity:1,mouseOverImageUrl:null,imageMargin:'2px',hideWithControls:false,showOverAds:false},htmlOnPauseBlock:{html:null,height:null,width:null},allowDownload:false,allowTheatre:false,playbackRateEnabled:false,controlBar:{autoHide:true,autoHideTimeout:3,animated:true},},vastOptions:{}});
It preloads .mp4 files on both chrome and firefox (seen on the network tab)

Your code is very similar to mine, however, mine works. I was stuck for a long time and tried several configurations, in my case what solved this problem was what I published, I don't know why in your case it doesn't work. It doesn't work ... something I can ask you if you use something to minify or measure the .js files, if so, add an exception so that fluidplayer.min.js is not minified and try again ... luck.

yes I minify the files and its behind cloudflare so it gets auto minified. But all the other configurations work so why is it preloading. Also there's another recent thread about preloading under issues here.
where do you have preload:false in the very beginning? maybe it's come combination glitch with another option

Well, that is the problem, the preload does not work because the file is minimized. I mentioned that your code is very similar to mine and mine works, but mine is not minimized. When I started using this player, it was minimized and I had to create an exception so that it is not minimized, I don't remember what problem caused me on that occasion. In the small capture that I publish, you will see where the preload is preload: false, it is right at the beginning. I currently don't use Cloudflare and I don't know if you can create an exception for the fluidplayer.min.js file, it would be nice if you tried, I'm almost sure why the preload doesn't work for you.

alright i figured the issue out. the issue is within the player itself.
       videoPlayerTag.setAttribute('preload', this.displayOptions.layoutControls.preload);
assumes <video id=xxx preload="false"> (or true, or auto).
whereas the correct values are auto, metadata, or none.
So to fix it you simply do preload:'none' for layoutControls, even tho it's not documented properly..

preload:'none'

alright i figured the issue out. the issue is within the player itself.
 videoPlayerTag.setAttribute('preload', this.displayOptions.layoutControls.preload);
assumes <video id=xxx preload="false"> (or true, or auto).
whereas the correct values are auto, metadata, or none.
So to fix it you simply do preload:'none' for layoutControls, even tho it's not documented properly..

I wanted to help you and it turns out that you have helped me, although my code worked as I wanted I made the small change you suggested and everything is a little faster, it works better, I have saved some milliseconds, thank you friend, good suggestion.

Mmm, sorry. How to fix this? I still don't understand.
I put in to my config this
preload:'none'
It doesn't matter.
It's easy config
	fluidPlayer('video-id', 	{ 		layoutControls: { 			posterImage: '/images/large/bbb.jpg', 			preload:'none', 			autoPlay: false, 		}, 		vastOptions: { 			//Zzzzzzz 		} 	} );
OR THIS
   var myFP = fluidPlayer(         'video-id',         {             layoutControls: { 		fillToContainer: false, 		primaryColor: false, 		preload:'none', 		posterImage: false, 		autoPlay: false, 		playButtonShowing: true, 		playPauseAnimation: true, 		mute: false, 		logo: { 			imageUrl: null, 			position: 'top left', 			clickUrl: null, 			opacity: 1, 			mouseOverImageUrl: null, 			imageMargin: '2px', 			hideWithControls: false, 			showOverAds: false 		}, 		htmlOnPauseBlock: { 			html: null, 			height: null, 			width: null 		}, 		allowDownload: false, 		allowTheatre: true, 		playbackRateEnabled: false, 		controlBar: { 			autoHide: false, 			autoHideTimeout: 3, 			animated: true 		},             },             vastOptions: {                 //              }         }     );

either show me an example page or confirm in developer tools in your browser that

the preload=none attribute is actually there

*Loading .ts
Code

Hello @redrabbit85, the site you sent as an example does not use Fluid Player, that site uses VideoJs, possibly the parameters are not the same, otherwise, check the url you sent and you are not using any parameters for Preload, look at Gtmetrix and part of the video is loading just by opening the url .. I am not sure if you want to switch to VideoJs, or if you want to implement the Preload to Fluid Player, it seems that it is already implemented, I don't know if you want to confirm if it worked for you or not, yes would you be a little clearer at least I could give you some guidance .. Preload is working for you or not?

Thanks for reply. I don't want load *.ts file when page loading. Many web bot load my files and I don't want them load traffic. I want load *.ts files when user click on play button.
I know it another script.
Can you help me?

Can you send one of your url that has Fluid Player loaded and working? .. If you want you can create a sample page just for testing, just put the Fluid Player script with the current configuration you use, if that is possible, post it here. I'm a little busy, but I'll take the time.

Hm, I don't know how to send text in your PM. My Telegram is https://t.me/redrabbit85
I send you worked site.

as lestadt mentioned, the site you linked is videojs..
your developer tools parameter looks correct, however i don't use m3u8 (HLS) and not sure how preload affects m3u8 files..

I changed source to mp4 and preload "none" is working.
But I still don't know why HLS is loading.
I trying use on play api. I changed source scr via jquery and this worked, but video didn't loading.

I don't use HLS..It might preload by default based on web browser standards and the video tag..i have no idea

preload seems to be working just fine. What pays to remember is that preload is a hint argument - browsers and crawlers CAN ignore it entirely.

my settings are exactly like this and preload works.


"No form data sent to superForm, schema type safety cannot be guaranteed." ERROR

Description
Thank you so much for this excellent package!
I am very pleased w/ it, and had a working project set up w/ SuperForms 0.6.18, and several forms.
Am just now going thru to update npm packages, and updated to SuperForms 1.1.2...
After the update, I receive the following error when running in dev & preview modes:

"No form data sent to superForm, schema type safety cannot be guaranteed. Also, no constraints will exist for the form. Set the warnings.noValidationAndConstraints option to false to disable this warning."

The forms actually seem to work & submit. I have been thru the documentation and migration guide, as well as searching the web for that particular error message, but can find nothing.
I am loading SuperValidate from +page.server.js files, not using client side SPA setup. From the documentation, and the fact that the forms submit, it would seem this is fine.
I have tried to match both the documentation, and the code w/in the stackblitz repl, but receive that same error.
The  component works fine, and shows the input values as entered.
Any insight you might provide on the error will be greatly appreciated 😌🙏

Hello, thank you for hanging on since the beginning! It happens because somewhere, null or undefined is being sent to superForm. You need to call superValidate with the schema, usually in the load function, and pass its output on to superForm.

Migration guide updated: https://superforms.rocks/migration#superform

Thanks for your quick reply; inspires a great deal of trust in you as the creator 🙏
I don't think this is closed yet however...I've just tried the upgrade again, with same results. Even weirder, 1 of my forms works fine, and the one that doesn't was copied from that and looks identical but for the data fields.
Both load schema from a "schema.js" in the same directory as the +page files.
The 3rd form, that also generates that error on page load has an embedded form, with all the same fields as the working 'contact-us', and loads that same schema.
Recap:

I have 3 SuperForms
1 of them works fine, SuperValidate in +page.server.ts
1 embedded in another page, which submits to the above working one, but generates the error
3rd one also generates error, but it's +page.server.ts is basically identical to the first form that works, but for different form fields

contact-us | Works fine, no error
import { message, superValidate } from 'sveltekit-superforms/server';

let PUBLIC_CONTACT_URL = import.meta.env.VITE_CONTACT_URL;

import { schema } from './schema.js';

export const load = async (event) => {
	// Server API:
	const form = await superValidate(event, schema);
	// Always return { form } in load and form actions.
	return { form };
};

export const actions = {
	default: async (event) => {
		// Same syntax as in the load function
		const form = await superValidate(event, schema);
		// console.log('POST', form);

		// Convenient validation check:
		if (!form.valid) {
			// Again, always return { form } and things will just work.
			// return fail(400, { form });
			// Will return fail(400, { form }) since form isn't valid
			return message(form, 'Invalid form!');
		}

		// TODO: Do something with the validated data
		let data = new FormData();
		data.append('your-name', form.data.name);
		data.append('your-email', form.data.email);
		data.append('your-company', form.data.company);
		data.append('your-telephone', form.data.phone);
		data.append('comments', form.data.comments);
		data.append('newsletter', form.data.newsletter === 'on' ? 'SUBSCRIBE' : 'UNSUBSCRIBE');
		// console.log('MLX page server data: ', data);
		try {
			// console.log('Form URL: ', PUBLIC_CONTACT_URL);
			const response = await fetch(PUBLIC_CONTACT_URL, {
				method: 'POST',
				body: data
			});
			// console.log('MLX Form Response: ', response );
			// handle successful response
			form.data.response = response.status; // add server response to form data object
			// console.log('MLX Form Success: ', form);
			// return { form };
			return message(form, 'Message submitted!');
		} catch (error) {
			// handle error
			console.log('MLX Form Error: ', error);
			console.log('Form URL: ', PUBLIC_CONTACT_URL);
			// return message(form, 'Error submitting form!');
			return { form };
		}
		// Yep, return { form } here too
		// return { form };
	}
};

// test error.html w/ below
// export function load() {
//     throw new Error('😫');
// }

Generates Error, basically the same afaict
import { message, superValidate } from 'sveltekit-superforms/server';

let PUBLIC_CONTACT_URL = import.meta.env.VITE_REQUEST_QUOTE_URL;

import { schema } from './schema.js';

export const load = async (event) => {
	// Server API:
	const form = await superValidate(event, schema);
	// Always return { form } in load and form actions.
	return { form };
};

export const actions = {
	default: async (event) => {
		// Same syntax as in the load function
		const form = await superValidate(event, schema);
		// console.log('POST', form);

		// Convenient validation check:
		if (!form.valid) {
			// Again, always return { form } and things will just work.
			// return fail(400, { form });
			// Will return fail(400, { form }) since form isn't valid
			return message(form, 'Invalid form!');
		}

		// TODO: Do something with the validated data
		let data = new FormData();
		// Contact Form DB automatically takes the first 4 values for columns
		// so the order here affects what will be seen there
		data.append('your-name', form.data.full_name);
		data.append('your-email', form.data.email);
		if (form.data.unlisted_product) {
			// use the manually entered proeduct instead
			data.append('product-name', form.data.unlisted_product);
		} else {
			// use the prodcut label instead of product_name, which is actually ACF ID on submit
			data.append('product-name', form.data.product_label);
		}
		data.append('qty', form.data.product_qty);
		// end Contact Form 7 DB auto columns
		data.append('product-kit', form.data.product_kit);
		data.append('product-number', form.data.product_number);
		data.append('purpose', form.data.purpose);
		data.append('organisation-name', form.data.organisation_name);
		data.append('phone-number', form.data.phone);
		data.append('your-city', form.data.city);
		data.append('your-state', form.data.state);
		data.append('country', form.data.country);
		data.append('special-requirements', form.data.special_requirements);
		data.append('newsletter', form.data.newsletter === 'on' ? 'SUBSCRIBE' : 'UNSUBSCRIBE');
		try {
			// console.log('Form URL: ', PUBLIC_CONTACT_URL);
			const response = await fetch(PUBLIC_CONTACT_URL, {
				method: 'POST',
				body: data
			});
			// console.log('MLX Form Response: ', response );
			// handle successful response
			form.data.response = response.status; // add server response to form data object
			// console.log('MLX Form Success: ', form);
			return message(form, 'Request for Quote submitted!', { status: 200 });
		} catch (error) {
			// handle error
			console.log('MLX Form Error: ', error);
			console.log('Form URL: ', PUBLIC_CONTACT_URL);
			if (!form.valid) {
				return message(form, 'Failed to submit form!', { status: 403 });
			}
		}
		// Yep, return { form } here too
		// return { form };
	}
};

// test error.html w/ below
// export function load() {
//     throw new Error('😫');
// }

...the form load code is identical in the contact-us (works) & request-for-quote (which doesn't)
contact-us & request-for-quote
	import { superForm } from 'sveltekit-superforms/client';
	// import SuperDebug from 'sveltekit-superforms/client/SuperDebug.svelte';
	export let data;
	import { schema } from './schema.js';
	const { form, valid, enhance, reset, errors, constraints, delayed, message } = superForm(
		data.form,
		{
			applyAction: true,
			invalidateAll: true,
			clearOnSubmit: 'errors',
			resetForm: true,
			multipleSubmits: 'prevent',
			taintedMessage: 'Are you sure you want leave?',
			validators: schema,
			autoFocusOnError: 'detect'
		}
	); // Client API

...so, completely open to my missing something obvious. And if both contact-us forms, which submit to the same place, and use the same schema.js worked, that would be more sensible...
Anything there leap out to you?
Thanks much,
Morgan
🙏

@ciscoheat Thank you so much for responding so quickly!
Your confidence that this was pretty simple fix encouraged me to continue on...and problem is now FIXED!
For SuperForms 1.1.3, there is a breaking change from 0.6.18. The earlier version was more lax about requiring a SuperValidated form on initial page load...
For embedded forms, you must:

include SuperValidate & return form in the +page.js/+page.server.js
although you may already do this in the actual form page to which you submit the embedded form, the loading page where the form is embedded will have no idea about this validated form object

For complex forms where you have +page.server.js for the form actions, and +page.js to load data for dependent select drop downs, etc., you must:

include SuperValidate & return form in the +page.js/+page.server.js
YES, that's in both files. While earlier version were fine when it was just included in +page.server.js, in the SuperForms 1.x series, this now causes an error

Thanks again for this most excellent package, and for your timely and helpful response to the issue queue...much appreciated!
All the best,
Morgan
😌🙏

Glad you could fix it! But when you say include (and call?) superValidate in both +page.js and +page.server.js, did you know that you can use the data from the server load function with the data parameter, so you can just pass it on? Docs here: https://kit.svelte.dev/docs/types#public-types-loadevent
The +page.js function would look something like this:
export const load = ({ data }) => {
  return data
}

So if I'm understanding you correctly, you are suggesting that by returning data from +page.js I would then avoid importing and running superValidate again in +page.server.js?
Currently I am returning an object from +page.js which itself has 2 objects; form, and another object that has the data I need to build my selects. It was by adding the SuperValidate import & form to the return here that I was able to correct the SuperForms 1.1.3 error.
Some of the automagic of the data load & passing sequence is rather fuzzy to me. I have an alright sense of how the +page.*.js files magically populate data for the +page.svelte files, but have seen nothing about how +page.js & +page.server.js might pass the data object around to each other...
I started with the +page.server.js, and added the +page.js later because I don't need the queries that build the product select dropdown to be server only. That's why originally I didn't do anything w/ superValidate in +page.js, and SuperForms 0.6.18 had no issue w/ that...
I don't want to burn too many of your CPU cycles w/ this, and I am quite happy to have working code & a current version of SuperForms now. However, I really appreciate your responses and insight, and do want to make sure I am using SuperForms as correctly and efficiently as possible...
+page.server.js

+page.js

Thanks again,
Morgan

In +page.js you can deconstruct the data parameter, which will contain the form, since you returned { form } in +page.server.js. Then just pass it on at the end with form: data.form, or, if you want to pass on all server data:
return {
  ...data,
  rfqData: {
    // The other fields
  }
}

Perfect, that works great, and no more double schema loading & SuperValidate calls for that route!
Once more, thank you so much! The package is great, and your timely responses and engagement are greatly appreciated!
Am very pleased to have all my frontend dependencies up to date for go live this coming week. Forms are an important part of that, and your quick turn around and help give me loads of confidence that I have chosen the right lib for this 😌🙏


Remove attribute.list from initial state for product page

What is the motivation for adding / enhancing this feature?

Currently, the initial state can be really huge mostly because of the INITIAL_STATE. Removing the server pre-fetched attributes list seems like a safe bet to improve it (as the attributes will be loaded client's side then anyway)
To comment out this line for tests: https://github.com/DivanteLtd/vue-storefront/blob/d6ace4928f3334739c099f7a481ad80e683175a4/core/modules/catalog/store/product/actions.ts#L627
TODO:

[ ] figure out the Category initial state if there are any other things which can be safely removed from the initial state (and loaded just client's side)

The potential issues will be related with the hydration

Closed by #3326 :)


Cerificate has expired

Hello there!
Thanks alot for running this service. I see the certificate has expired on the site https://onix-codelists.io
Is there any way you can renew it? I would also recommend setting up automatic renewal, but I do not know anything about your hosting situation :)
Thanks again!

It was setup to use Let's Encrypt automatic renewals but somehow the config got broken and the renew did not happen in time. I did get notification about, but I was away for holidays. This has been now fixed and the SSL certificate is valid once gain.

🎉
Thanks alot for the quick response. And thank you once again for making the service, and contributing your own time. It is very much appreciated :)


FEAT: Added the feature of scrolling back to top

Related Issue
Solves issue #136 of adding a button in the footer to scroll back to the top of the page.
Description
This PR solves the issue by adding a button in the footer which helps scroll back to the top of the page. The design has been done according to the UI.
Type of PR

[ ] Bug fix
[ X ] Feature enhancement
[ ] Documentation update
[ ] Other (specify): _______________

Screenshots/videos (if applicable)

Checklist:

[ X ] I have performed a self-review of my code
[ X ] I have read and followed the Contribution Guidelines.
[ X ] I have tested the changes thoroughly before submitting this pull request.
[ X ] I have provided relevant issue numbers, screenshots, and videos after making the changes.
[ X ] I have commented my code, particularly in hard-to-understand areas.

Hi @Muskansahuincredible, I have added a PR for issue #136  , please merge it under the category of GSSOC'24.
Thank You!


ARROW-11885: [R] Turn off some capabilities when LIBARROW_MINIMAL=true

This was made trivial to do by ARROW-12981. Also includes ARROW-13586.

@github-actions crossbow submit test-r-rstudio-r-base-3.6-centos7-devtoolset-8 test-r-minimal-build


fix(dialog): small edits

Just some small content tweaks.

Accidentally deleted the fork, #752 resolves this.


Fix file exists error

In function tf.train.write_graph, first check if logdir exists, then write file in "w" or "wb" mode depends on as_text

I signed it!

(squash the commits once this is done and i'll run a quick test before merging)

Can one of the admins verify this patch?

Can one of the admins verify this patch?

@vrv, done, thanks

@tensorflow-jenkins, test this please.

(ugh, give us some time to fix our CI testing, sorry)

Jenkins, test this please.

@martinwicke: why is one check stuck?

We're working on it. Looking good though, I think we can merge (I'll take the beating if I'm wrong).


API Gateway Path Fix

This PR fixes up the apigateway and apigatewayv2 endpoints. A small adjustment was also made to CloudFront distributions on the same endpoints command, where the path was set to // for empty OriginPaths.
A coworker of mine recognized that Cloudfox wasn't building the API endpoints correctly for most API gateways, so I decided to take a look.
Feel free to refactor it or add any suggestions and I can make updates.
This PR should translate v1.11.0 output from:
https://aaaaaaaaaa.execute-api.us-east-1.amazonaws.com/foobar
https://aaaaaaaaaa.execute-api.us-east-1.amazonaws.com/
https://aaaaaaaaaa.execute-api.us-east-1.amazonaws.com/foobar/foobaz
https://aaaaaaaaaa.execute-api.us-east-1.amazonaws.com/hello-world
https://bbbbbbbbbb.execute-api.us-east-1.amazonaws.com/foobar/test

To:
https://aaaaaaaaaa.execute-api.us-east-1.amazonaws.com/test/
https://aaaaaaaaaa.execute-api.us-east-1.amazonaws.com/test/foobar/foobaz
https://aaaaaaaaaa.execute-api.us-east-1.amazonaws.com/test/hello-world
https://bbbbbbbbbb.execute-api.us-east-1.amazonaws.com/dev/foobar/test
https://api.example.com/foobar/test
https://api.example.com/api/foobar/test

Details
Here is my understanding of how apigateway urls can be generated:

Get list of APIs via GetRestApis
Loop over APIs and get the stages per API via GetStages
Get the API resources via GetResources
Then finally loop over the stages and resources to generate the endpoint in the format of: https://{apiId}.execute-api.{region}.amazonaws.com/{stage}/{resourcePath}, only where a resource's ResourceMappings is not empty (some HTTP method is supported)

Note a stage name can not be empty or contain slashes.
The GetResources call may return data like the following:
{
  "items": [
    {
      "id": "d9vr9f",
      "parentId": "gks4vcu5sd",
      "pathPart": "foobar",
      "path": "/foobar"
    },
    {
      "id": "gks4vcu5sd",
      "path": "/",
      "resourceMethods": {
        "GET": {}
      }
    },
    {
      "id": "je7k01",
      "parentId": "d9vr9f",
      "pathPart": "foobaz",
      "path": "/foobar/foobaz",
      "resourceMethods": {
        "GET": {},
        "OPTIONS": {}
      }
    },
    {
      "id": "n3xxyd",
      "parentId": "gks4vcu5sd",
      "pathPart": "hello-world",
      "path": "/hello-world",
      "resourceMethods": {
        "GET": {}
      }
    }
  ]
}

Not every resource will have the resourceMethods property, however the SDK doesn't use a *map[string]struct{}. The length of the map can be checked, since we get an actual map object back, to see if there are any supported methods. pathPart could be used, but then a linked list or similar data structure would be needed to reference the chain of parents and join the paths.
Here is my understanding of how apigatewayv2 urls can be generated:

Get list of APIs via GetApis
Loop over APIs and get the list of stages for each API via GetStages
Loop over the routes via GetRoutes
Generate the url to be: https://{apiId}.execute-api.{region}.amazonaws.com/{stage}/{routePath}

The GetRoutes call returns data such as:
{
  "Items": [
    {
      "ApiKeyRequired": false,
      "AuthorizationType": "NONE",
      "RouteId": "39vpeqm",
      "RouteKey": "GET /foobar/test",
      "Target": "integrations/74l1qev"
    },
    {
      "ApiKeyRequired": false,
      "AuthorizationType": "NONE",
      "RouteId": "ary8h48",
      "RouteKey": "GET /foobar",
      "Target": "integrations/f6c3wfm"
    }
  ]
}

I kept the same logic that was used to extract the RouteKey previously, however the stage was missing.
For both of these, I felt that it was important to retrieve any custom domain names and interpolate them with the existing API endpoints. To associate a custom domain name with an API, a mapping is needed to point a path towards an API GW and a stage. To simplify this process, I re-use the cached list of all APIs rather than retrieving the individual API and then perform a string replace for every endpoint on the matching API. I prefer to keep the execute-api version as well as the custom API to indicate that there are multiple endpoints for the same service, which may be helpful if one is blocked.
For apigateway, an empty mapping is labeled as (none), while with apigatewayv2 an empty mapping is labeled as an empty string.

Found a small bug. When the stage is set to $default, the root path of apigatewayv2's should be used

Thanks @wdahlenburg. I'll be looking at merging this to main shortly.


README updated

updating README and addition of useful links.

Next time, do comment Claimed in the Issue tab.


Feat-Req: Organize Beyond Depth 1

Really convenient and would love to use it more extensively, but the fact that collision detection is limited to immediate children is kind of limiting.

I'm not terribly familiar with Obsidians API, but here's my uneducated thoughts:

Select node who's children you want to deeply organize (usually Root) and use special hotkey to trigger action. Depth-first search to get deeply connected nodes (break/alert if cycle detected or ignore?). Sum height of children + gap heights (setting) to get chunk sizes, position/align chunks for each depth (given the initially selected node as the center line).
potentially take advantage of the group feature:
a. if group has special name (some regex pattern), always deeply organize after updates (+/- node, node resized, connection changed, etc. ).
b. use groups to limit scope of nodes you need to consider if that is troubling for any reason.

Thank you for the validation!
This diagram represents my comprehension of your concept. Are there any discrepancies?

Wow bro, sorry for the late reply, I'm being forced to learn rust language by my boss😂
You're really doing a lot of thinking ! Surely, the generation of nodes have different resolution because we should consider many scenarios.
Tree structure and 2-Passes in the last example is a good choice, they will be released in the next version!


HasPrepareQuorum - log missing proposal only in prepare state

Description
This PR fixing unnecessary logging when the HasPrepareQuorum is called. To check if we have a quorum of prepare msgs, we need to know the proposer, which is read from the proposal. In some cases HasPrepareQuorum can be called when the proposer is not set e.g. when the prepare messages for a certain view arrives before the preprepare msg for the same view, when the state is in the runNewRound phase. In that case we should just return false without logging an error. If the state is in the prepare phase the message should be logged since we expect proposal to be set.
Changes include

[x] Bugfix (non-breaking change that solves an issue)
[ ] Hotfix (change that solves an urgent issue, and requires immediate attention)
[ ] New feature (non-breaking change that adds functionality)
[ ] Breaking change (change that is not backwards-compatible and/or changes current functionality)

Checklist

[x] I have assigned this PR to myself
[x] I have added at least 1 reviewer
[ ] I have added the relevant labels
[ ] I have added sufficient documentation in code

Codecov Report

Merging #81 (5ff63e2) into main (081b97a) will increase coverage by 0.21%.
The diff coverage is 57.14%.

:exclamation: Your organization is not using the GitHub App Integration. As a result you may experience degraded service beginning May 15th. Please install the Github App Integration for your organization. Read more.
@@            Coverage Diff             @@
##             main      #81      +/-   ##
==========================================
+ Coverage   88.61%   88.83%   +0.21%
==========================================
  Files           7        7
  Lines        1520     1522       +2
==========================================
+ Hits         1347     1352       +5
+ Misses        132      130       -2
+ Partials       41       40       -1

Impacted Files
Coverage Δ

core/validator_manager.go
89.15% <50.00%> (-3.44%)
:arrow_down:

core/ibft.go
85.77% <100.00%> (+0.68%)
:arrow_up:

:mega: We’re building smart automated test selection to slash your CI/CD build times. Learn more


AST convert error

Describe the bug
AST convert error
To Reproduce
Steps to reproduce the behavior:

select CONVERT(true, CHAR)
Parsing this SQL using JSqlParser with this statements

Expected behavior
why  the key [true] and [Char]  is  instance of Column
System

Database you are using
mysql8.0
Java Version
11
JSqlParser version
4.2

Greetings.
Your observation is not a dogmatic bug, but a practical feature: JSQLParser is agnostic to the RDBMS (to a large extend) and does not even try to understand the specific syntax but will parse the syntax as generic as possible.
So in your case, JSQLParser does not see a specific CONVERT function with a Boolean and a CHAR typename. Instead it sees any abstract function and true could be a column-name and char could be a column name.
We do not know better and we do not WANT to know better -- because this would be expensive (parsing and implementation wise) and would restrict us to a certain RDBMS.
So as a rule of thumb: JSQLParser will always try to parse you statement as simple as possible and assign the "first compliant java class" (in the order of parsing effort).
Related to your example, we test first if true and char could be a column. When then answer is yes, we are satisfied and move on and do not even (want to) try if it also could be a BOOL expression or a CHAR typename.
If you are looking for RDBMS specific grammar which would return CONVERT function and EXPRESSION and TYPENAME then you may have more luck with ANTLR and its various RDBMS specific grammars (which all use different tokens and productions and are huge and complex and slow.)

@wumpz: I tend to close this as "not a bug but a feature", how do you see it please?

It's exactly like you lectured ;). The true nature of objects or values parsed could only be revealed using an actual running database.


when i publish package to the server,shows 'one of the uplinks is down, refuse to publish'

My reason:

Steps to reproduce:

App Version:

Config file:

Additional information:

$ set DEBUG=express:* verdaccio enable extreme verdaccio debug mode
$ npm --verbose prints:
$ npm config get registry prints:
Verdaccio terminal output
Windows, OS X/macOS, or Linux?:
Verdaccio configuration file, eg: cat ~/.config/verdaccio/config.yaml

Container:

I use local environment
I develop / deploy using Docker.
I deploy to a PaaS.

Additional verbose log:

Yeah, that's intended behaviour, to allow that you have to enable https://github.com/verdaccio/verdaccio/blob/master/wiki/config.md#offline-publish


Can add items to cart without selecting required modifiers

See video of menu client being able to add item to cart without selecting the required mod.
Menu builder screenshot below video showing Burrito Size mod as required.
https://images.zenhubusercontent.com/262458950/a94c4572-83fa-4ab7-90ff-1d34cd036ca2/can_add_item_to_cart_w_out_required_mods.mov

closed for duplicate, this ones in the wrong repo


Using opentonapi as a package

Hi, trying to use this repo as a package in my go code
using go get github.com/tonkeeper/opentonapi results in getting the latest 1.X version which is 1.2.2 from a few months ago
In order to get newest v2 version, the go.mod file must be updated to include v2 in the module name
I have opened this PR for doing so
https://github.com/tonkeeper/opentonapi/pull/450

Hi, trying to use this repo as a package in my go code using go get github.com/tonkeeper/opentonapi results in getting the latest 1.X version which is 1.2.2 from a few months ago
In order to get newest v2 version, the go.mod file must be updated to include v2 in the module name I have opened this PR for doing so #450

Has your issue already been escalated?


calculate_OKS？

what is it?

what is it?

@henbucuoshanghai
Object keypoint similarity. Please refer to this

我水平好菜，现在搞姿态。。   发愁

我水平好菜，现在搞姿态。。   发愁


Wrong redirect after log out -> /login/ and not /lets-chat/login/

Hi,
I don't know what i'm doing wrong.
I installed lets-chat in a subfolder lets-chat, if I log me out I will redirected to xxx.de/login and not to xxx.de/lets-chat/login
Have I a mistake in my .htaccess
RewriteEngine On

RewriteCond %{SERVER_PORT} 80
RewriteRule ^lets-chat/(.*) http://localhost:myportnumber/$1 [P]

I don't have the most experience with node applications.
Cheers

Give a look at #411 , I think you'll find your answer

@mmeinert Thanks, why I don't have found it last night?
Problem solved, thanks again!