libiomp5-gnu-hpc-debuginfo-2.6.0-bp153.2.3.1 4>$  Apb!M@eee=|h*{Œ&W bNhG&h/[ôEC2xoM3h`60*gٳ^P@O:^ &7žJM8EgG3˅ʄĪirc2SϘ~{ ?fM\"ЍrN~̈k8,U1{^֖6;LHlu͏MŽ4\ն6?vH dwjS0\3cb3506d2e06480a273a0796ec3b5a05ec9ea738cd81536a2066c148a4f42a0291a0f058e86eaa05e8908fbf2952a699bbe791c6db!M@eee~DMc~Lɶ N32u6獂mW,lPà 0,NO3~ m2LEQ2B}H\&udR #D"ُKʗu)xDov3VV:y4%ynCkЌp@`?`! - \ $=F  4  N  h        ! b  4(8K9$K:GKFZNGZ` HZ IZ XZYZ\[4 ][h ^\# b]vc^d^e^f^l^u^ v_ w_ x` y`8z`<`L`P`~````libiomp5-gnu-hpc-debuginfo2.6.0bp153.2.3.1Debug information for package libiomp5-gnu-hpcThis package provides debug information for package libiomp5-gnu-hpc. Debug information is useful when developing applications that use this package or when debugging this package.blamb03SUSE Linux Enterprise 15openSUSEApache-2.0 AND BSD-2-Clause AND BSD-3-Clause AND FSFUL AND MIT AND MPL-2.0 AND OpenSSL AND Python-2.0http://bugs.opensuse.orgDevelopment/Debughttps://www.tensorflow.org/linuxx86_64CpAAAAAAAAAA큤bbbbbbxbxbxbxbxbxbb9940bf374d4a1fde893ac6f2224debeede68f87bb895023add7b9c50887cd09a../../../../../usr/lib/hpc/gnu7/tensorflow2/2.6.0/lib64/libiomp5.so../../../../../usr/lib/debug/usr/lib/hpc/gnu7/tensorflow2/2.6.0/lib64/libiomp5.so-2.6.0-bp153.2.3.1.x86_64.debugrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootroottensorflow2_2_6_0-gnu-hpc-2.6.0-bp153.2.3.1.src.rpmdebuginfo(build-id)libiomp5-gnu-hpc-debuginfolibiomp5-gnu-hpc-debuginfo(x86-64)    rpmlib(CompressedFileNames)rpmlib(FileDigests)rpmlib(PayloadFilesHavePrefix)rpmlib(PayloadIsXz)tensorflow2_2_6_0-gnu-hpc-debuginfo(x86-64)3.0.4-14.6.0-14.0-15.2-12.6.0-bp153.2.3.14.14.3a/k@aG``v@`lM@`.V`U`_=_I@_@_"__q@__[@_Z@_X_Wr@_P_>e_>e^^^@^˳@^k@^}^|@^y@^t@^ku^f/^`^Y^Nt^F^E:@^B@^:@^6^4^2@^2@^0"@^0"@^0"@^*@^)]]V]]]>]/ ]#0@\]@\t@\@\@\\\\\@\b@\ac\R@\A\8@[@[=@[%@[u[ZN@Egbert Eich Fusion Future Guillaume GARDET Ferdinand Thiessen Ben Greiner Guillaume GARDET Guillaume GARDET Guillaume GARDET Dirk Müller Guillaume GARDET Guillaume GARDET Christian Goll Christian Goll Christian Goll Christian Goll Christian Goll Christian Goll Christian Goll Guillaume GARDET Guillaume GARDET Martin Liška Christian Goll Christian Goll Christian Goll Christian Goll Christian Goll Guillaume GARDET Christian Goll Bernhard Wiedemann Guillaume GARDET Christian Goll Guillaume GARDET Christian Goll Guillaume GARDET Guillaume GARDET Christian Goll Guillaume GARDET Guillaume GARDET Christian Goll Guillaume GARDET Guillaume GARDET Christian Goll Guillaume GARDET Christian Goll Guillaume GARDET Guillaume GARDET Guillaume GARDET Christian Goll Christian Goll Christian Goll Christian Goll guillaume.gardet@opensuse.orgChristian Goll Christian Goll Christian Goll Guillaume GARDET Guillaume GARDET Guillaume GARDET Guillaume GARDET Christian Goll Guillaume GARDET Guillaume GARDET Christian Goll Adrian Schröter cgoll@suse.comChristian Goll Bernhard Wiedemann Guillaume GARDET Guillaume GARDET Todd R Todd R cgoll@suse.comJan Engelhardt cgoll@suse.comcgoll@suse.com- Limit BuildRequires for bazel-skylib-source to versions >= 1.0.3.- Update to 2.6.0 Major changes are: * Keras been split into a separate PIP package (keras), and its code has been moved to the GitHub repositorykeras-team/keras. The API endpoints for tf.keras stay unchanged, but are now backed by the keras PIP package. The existing code in tensorflow/python/keras is a staled copy and will be removed in future release (2.7). Please remove any imports to tensorflow.python.keras and replace them with public tf.keras API instead. * tf.train.experimental.enable_mixed_precision_graph_rewrite is removed, as the API only works in graph mode and is not customizable. The function is still accessible under tf.compat.v1.mixed_precision.enable_mixed_precision_graph_rewrite, but it is recommended to use the Keras mixed precision API instead. * tf.lite: Remove experimental.nn.dynamic_rnn, experimental.nn.TfLiteRNNCell and experimental.nn.TfLiteLSTMCell since they're no longer supported. It's recommended to just use keras lstm instead. * tf.keras: The methods Model.to_yaml() and keras.models.model_from_yaml have been replaced to raise a RuntimeError as they can be abused to cause arbitrary code execution. It is recommended to use JSON serialization instead of YAML, or, a better alternative, serialize to H5. - Major changes from 2.5.x: * Support for Python3.9 has been added. * The TF_CPP_MIN_VLOG_LEVEL environment variable has been renamed to to TF_CPP_MAX_VLOG_LEVEL which correctly describes its effect. - Fixed multiple CVEs (boo#1189423): * CVE-2021-37635 * CVE-2021-37636 * CVE-2021-37637 * CVE-2021-37638 * CVE-2021-37639 * CVE-2021-37640 * CVE-2021-37642 * CVE-2021-37641 * CVE-2021-37644 * CVE-2021-37643 * CVE-2021-37645 * CVE-2021-37646 * CVE-2021-37647 * CVE-2021-37648 * CVE-2021-37649 * CVE-2021-37650 * CVE-2021-37651 * CVE-2021-37652 * CVE-2021-37653 * CVE-2021-37654 * CVE-2021-37655 * CVE-2021-37656 * CVE-2021-37657 * CVE-2021-37658 * CVE-2021-37659 * CVE-2021-37660 * CVE-2021-37661 * CVE-2021-37662 * CVE-2021-37664 * CVE-2021-37663 * CVE-2021-37665 * CVE-2021-37666 * CVE-2021-37667 * CVE-2021-37668 * CVE-2021-37669 * CVE-2021-37670 * CVE-2021-37671 * CVE-2021-37672 * CVE-2021-37673 * CVE-2021-37674 * CVE-2021-37676 * CVE-2021-37675 * CVE-2021-37677 * CVE-2021-37678 * CVE-2021-37679 * CVE-2021-37680 * CVE-2021-37681 * CVE-2021-37682 * CVE-2021-37683 * CVE-2021-37684 * CVE-2021-37686 * CVE-2021-37685 * CVE-2021-37687 * CVE-2021-37688 * CVE-2021-37689 * CVE-2021-37691 * CVE-2021-37692 * CVE-2021-37690 - Updated sources: * abseil-cpp.tar.gz * cpuinfo.zip * dill-0.3.2.zip * eigen.tar.gz * google-cloud-cpp.tar.gz * libxsmm_1.14.tar.gz * llvm.tar.gz * oneDNN.tar.gz * rules_cc.tar.gz * rules_closure.tar.gz * rules_docker-0.18.0.tar.gz * ruy.zip * tblib-1.7.0.tar.gz - Added sources: * ComputeLibrary.tar.gz * oneDNN-v2.3-rc2.tar.gz * platforms-0.0.2.tar.gz * rules_proto.tar.gz * tf_runtime.tar.gz * tf_toolchains.tar.gz - Removed sources: * kafka-v0.11.5.tar.gz - Add "tensorflow-2.6.0" prefix to existing patches to indicate that patches are likely to be only applicable to a specific version. * fix-lite.patch -> tensorflow-2.6.0-fix-lite.patch * numpy-tensor-small.patch -> tensorflow-2.6.0-numpy-tensor-small.patch * removed-clog-build-as-included-in-cpuinfo.patch -> tensorflow-2.6.0-removed-clog-build-as-included-in-cpuinfo.patch * removed-external-toolchains.patch -> tensorflow-2.6.0-removed-external-toolchains.patch * remove-weakref.patch -> tensorflow-2.6.0-remove-weakref.patch * tf-keras-hdf5-3.patch -> tensorflow-2.6.0-tf-keras-hdf5-3.patch - Rebase all existing patches. - Add tensorflow-2.6.0-compile-with-protobuf-3.16.patch to fix build error with protobuf >= 3.16.0. (boo#1186860) (https://github.com/protocolbuffers/protobuf/pull/8354) - Update bazel version requirement to 3.7.2. - Drop pcre-devel build requirement as it is not used anymore. - Drop --incompatible_no_support_tools_in_action_inputs=false as it is removed in bazel >= 3.6.- Update _constraints to use host with 'asimdrdm' cpu flag to avoid slow CPU and be scheduled on faster systems- Update to version 2.4.1 * Bugfixes * Drops requirement of AVX2- Don't BuildRequire keras_applications. Tensorflow provides it itself: https://github.com/tensorflow/tensorflow/commit/23c3bdaa - These were discovered by Keras test suite: * add numpy-tensor-small.patch for Numpy >= 1.20 gh#tensorflow/tensorflow#47691 * add tf-keras-hdf5-3.patch for hdf5 >= 3.0 gh#tensorflow/tensorflow#44467- Generate and install pkgconfig files for tensorflow-lite and tensorflow (non-hpc)- Do not install bazel tools to build Lite version. This will allow to build for armv7 where bazel 3.x is not available - boo#1178564- Fix packaging for libiomp5- build verbose to not fail on the obs stall detection- libiomp5 is x86_64 only- Fix build on aarch64 and on hpc variants- Updated to version 2.4.0 which fixes several bugs (bsc#1173128) (bsc#1173314)i, (bsc#1179455) and (bsc#1178287) - Security fixes for CVE-2020-26266, CVE-2020-26267, CVE-2020-26268, CVE-2020-26270 and CVE-2020-26271 - updated sources: * abseil-cpp.tar.gz * eigen.tar.gz * gemmlowp.zip * googleapis.zip * llvm.tar.gz - added sources: * DouraFFT.tar.gz * cpuinfo.zip * dill-0.3.1.1.tar.gz (aarch64 only) * dlpack.tar.gz * oneDNN.tar.gz * openmp-10.0.1.src.tar.xz * python-license-astunparse * re2.tar.gz * rules_android.zip * rules_cc.tar.gz * rules_docker-0.15.0.tar.gz * ruy.zip * sobol_data.tar.gz * tblib-1.3.2.tar.gz (aarch64 only) * typing_extensions-3.7.4.2.tar.gz (aarch64 only) - removed sources: * boring_ssl.tar.gz * cub_1.8.0.zip * mkl-v0.21.2.tar.gz * pybind11-v2.3.0.tar.gz * right-json-location.patch * rules_docker.tar.gz - removed patches: * Provide-overload-to-cope-with-const-ness-change-of-N.patch * fix-google-absl-memory.patch * json-feature-name.patch * libjpeg_turbo-name.patch * removed-docker-tools.patch - added patches: * removed-clog-build-as-included-in-cpuinfo.patch * removed-external-toolchains.patch - Major changes are: * tf.distribute introduces experimental support for asynchronous training of models via the tf.distribute.experimental.ParameterServerStrategy API. * MultiWorkerMirroredStrategy is now a stable API and is no longer considered experimental. * Introduces experimental support for a new module named tf.experimental.numpy which is a NumPy-compatible API for writing TF programs. * A major refactoring of the internals of the Keras Functional API has been completed, that should improve the reliability, stability, and performance of constructing Functional models. * Keras mixed precision API tf.keras.mixed_precision is no longer experimental and allows the use of 16-bit floating point formats during training, improving performance by up to 3x on GPUs and 60% on TPUs. * TensorFlow Profiler now supports profiling MultiWorkerMirroredStrategy and tracing multiple workers using the sampling mode API.- fixed hpc flavor and Leap15.2 builds- updated to 2.1.2 with following fixes (bsc#1177022): * Fixes an undefined behavior causing a segfault in tf.raw_ops.Switch (CVE-2020-15190) * Fixes three vulnerabilities in conversion to DLPack format (CVE-2020-15191, CVE-2020-15192, CVE-2020-15193) * Fixes two vulnerabilities in SparseFillEmptyRowsGrad (CVE-2020-15194, CVE-2020-15195) * Fixes an integer truncation vulnerability in code using the work sharder API (CVE-2020-15202) * Fixes a format string vulnerability in tf.strings.as_string (CVE-2020-15203) * Fixes segfault raised by calling session-only ops in eager mode (CVE-2020-15204) * Fixes data leak and potential ASLR violation from tf.raw_ops.StringNGrams (CVE-2020-15205) * Fixes segfaults caused by incomplete SavedModel validation (CVE-2020-15206) * Fixes a data corruption due to a bug in negative indexing support in TFLite (CVE-2020-15207) * Fixes a data corruption due to dimension mismatch in TFLite (CVE-2020-15208) * Fixes several vulnerabilities in TFLite saved model format (CVE-2020-15209, CVE-2020-15210, CVE-2020-15211) - using fft2d.tgz instead of fft.tar.gz - removed fft.tar.gz- fixed json-feature-name.patch for leap15.2 builds- updated disk constraints, as sometimes the build fails with too low disk space- Package C-headers for standard tensorflow (boo#1175789) - fixed build gcc10.1 errors for Tumbleweed with following upstream patch: * added file Provide-overload-to-cope-with-const-ness-change-of-N.patch- Package header files for Tensoflow2 Lite - boo#1175099- Revert memoryperjob constraint support and use again %limit_build macro to avoid OOM errors- Lower memoryperjob to 1300 MB (as done for tensorflow)- Use memoryperjob constraint instead of %limit_build macro.- fixed build with json_cpp 1.9.3 (bsc#1173314)- fixed local CUDA builds- updated to 2.1.1 which is a bug fix release mostly for external sources which are not part of this package (sqlite,libjpeg-turbo, Apache Spark) * Fixes a versioning bug which causes Keras layers from TF 1.x to be used instead of those from TF 2.x- fixed broken builds which were caused due to missing dependency on @com_google_absl//absl/strings in various BUILD files - added patch: fix-google-absl-memory.patch- added mkl-ddn as source and do not use system mkl-dnn (bsc#1168839) - removed patches: * fixed-mkl-sgemm-call.patch * added-mkl_dnn-as-syslib.patch - added source: mkl-v0.21.2.tar.gz- tensorflow2-lite-devel does not requires libtensorflow*- removed hpc-mvapich2 build (bsc#1167735)- Use pip install --no-compile (boo#1094323)- Lite flavor should not provide python3-tensorflow nor tensorflow- removed sources of bazel sources and replaced them by internal packages * rules-cc.zip removed * bazel-toolchains.tar.gz removed * bazel-skylib.0.8.0.tar.gz removed- Lite flavor should not provide "tensorflow", otherwise tensorlfow2-devel and tensorlfow2-lite-devel conflict and break armnn- added Provides: tensorflow, so that Kerase works with this package and fixed Leap 15.2 build- Fix name for libtensorflow* sub-packages- openSUSE has no CUDA package, so disable cuda build for openSUSE- addding changes for CUDA builds- Add 'Provides' only for hpc flavors, otherwise it matches the package name- Add provides/conflicts to avoid to install tensorflow and tensorflow2 as some files are provided by both packages- removed mkl-dnn as sourc and force usage of system mkl-dnn for x86_64 builds * removed file mkl-dnn-v021.2.tar.gz * added patch: added-mkl_dnn-as-syslib.patch * added patch: fixed-mkl-sgemm-call.patch- Add 1.25.0 as minimal version for grpc-devel- Add 3.1.5 as a minimal version for double-conversion-devel (2.0.1 from SLE15SP2/Leap15.2 is too old) - Lower required version for protobuf (3.9.1 from SLE15SP2/Leap15.2 is fine)- removed AVX2 flavor, this should be fixed via mkl-dnn- Fix build on hpc targets- added shared library packages libtensorflow2, libtensorflow_cc2 and libtensorflow_framework2 - removed the AWS sdk support as this forces a SEGFAULT * remobed file aws-sdk-cpp-1.5.8.tar.gz - dropped following source files as they are not needed any more * removed file backports.weakref-1.0rc1.tar.gz * removed file gettid.patch * removed file grpc-v1.24.2.gz * removed file libjpeg-turbo-2.0.0.tar.gz * removed file nsync_1.20.0.tar.gz- Do not try to install *.pb.* files in Lite flavor- Define package name at 'tensorflow2' instead of 'tensorflow'- Generate *.pb.* files and package them, to be used by ArmNN - Do not build on %ix86 - Do not build %arm, except for Lite flavor- updated to tensorflow 2.1.0 which is a stable release and has following breaking changes: * Deletes Operation.traceback_with_start_lines for which we know of no usages. * Removed id from tf.Tensor.__repr__() as id is not useful other than internal debugging. * Some tf.assert_* methods now raise assertions at operation creation time if the input tensors' values are known at that time, not during the session.run(). This only changes behavior when the graph execution would have resulted in an error. When this happens, a noop is returned and the input tensors are marked non-feedable. In other words, if they are used as keys in feed_dict argument to session.run(), an error will be raised. Also, because some assert ops don't make it into the graph, the graph structure changes. A different graph can result in different per-op random seeds when they are not given explicitly (most often). * The following APIs are not longer experimental: tf.config.list_logical_devices, tf.config.list_physical_devices, tf.config.get_visible_devices, tf.config.set_visible_devices, tf.config.get_logical_device_configuration, tf.config.set_logical_device_configuration. * tf.config.experimentalVirtualDeviceConfiguration has been renamed to tf.config.LogicalDeviceConfiguration. * tf.config.experimental_list_devices has been removed, please use tf.config.list_logical_devices. - renamed the project to tensorflow2 so that the original tensorflow v1 API compatible release can stay in factory. Following changes were made to achive this: * added tensorflow-v2.1.0.tar.gz * added tensforflow2.spec * added tensforflow2.changes * removed tensorflow-v1.13.2.tar.gz * removed tensorflow.spec * removed tensorflow.chnages - following source files had to be updated * updated abseil-cpp.tar.gz * updated bazel-toolchains.tar.gz * updated eigen.tar.gz * updated gemmlowp.zip * updated license.rst.txt * updated rules_closure.tar.gz - following new souces had to be updated * added aws-sdk-cpp-1.5.8.tar.gz * added bazel-skylib.0.8.0.tar.gz * added fft2d.tgz * added rules_cc.zip - for the following souces the system libraries are now ues * removed aws-sdk-cpp-1.3.15.tar.gz * removed double_conversion.zip * removed file unicode-org-icu.tar.gz * removed file 816a4ae622e964763ca0862d9dbd19324a1eaf45.tar.gz - these patches were removed * removed file support-new-bazel.patch * removed file tensorflow-make_aws_sdk_work_on_aarch64.patch * removed file tensorflow-fix_lite.patch * removed file remove-keras.patch * removed file grpc-namespace-corrections.patch - these new patches were added * added fix-lite.patch * added removed-docker-tools.patch * added right-json-location.patch- updated to tensorflow 0.13.2 - dropped grpc.tar.gz and grpc-v1.13.0.gz as system grpc is used, this fixes the broken builds which were introduced with gcc9 (bsc#1152671) * added grpc-namespace-corrections.patch in order to use system grpc - dropped re2-2018-10-01.tar.gz as system re2 is used now- added remove-keras.patch which removes keras sources and uses distribution keras libaries * removed keras-applications-1.0.6.tar.gz * removed keras-preprocessing-1.0.9.tar.gz- using now system protobuf instead of building it (bsc#1151150)- Ajust %limit_build to avoid OOM errors - Do not use %limit_build for lite flavor- added additonal dependencies- fixed installation location of shared library- removed bazel mirror from as much source links as possible - added support-new-bazel.patch support newer upcoming bazel versions- Fix build for lite flavor: * tensorflow-fix_lite.patch- Call ldconfig for devel package in post/postun- Fix aarch64 build with upstream patch: * tensorflow-make_aws_sdk_work_on_aarch64.patch- Add Lite flavor- updated to 1.13.1 fixes boo#1133490- Update _constraints to avoid OOM errors- Build and package libtensorflow_cc and libtensorflow_framework- added fix_mvapich_mpi_bzl.patch which fixes detection of mvapich2 mpi library - fixed python3 build- update to version 1.13.1 * Major Features and Improvements * TensorFlow Lite has moved from contrib to core. This means that Python modules are under tf.lite and source code is now under tensorflow/lite rather than tensorflow/contrib/lite. * TensorFlow GPU binaries are now built against CUDA 10 and TensorRT 5.0. * Support for Python3.7 on all operating systems. * Moved NCCL to core. - drop merged patch mpilibpath_configure_py.patch - drop obsolete pyton3.7 patches - disabled jemalloc for now- enabled aws and googlecloud support * removed no_aws_and_googlecloud.patch- Fixed build issues with python 3.7 what introduced the patches * python3_7_compatibility.patch backported from upstream * python3.7_unicode.patch fixes a minor function call * python3.7_async_keyword.patch avoids the new keyword async- Fix build with python 3.7- Build and package libtensorflow.so as some packages may link to it- Add constraints on HDD size to avoid no space-left error- Fix python3 provides - Minor spec file cleanups- Provide python3-tensorflow- updated build command to fit bazel-0.19- Trim pad wording from descriptions.- Updated to Tensorflow 1.10 as with this release it supports the partial use of systemlibs. Still a lot additional sources are included which are * closure * weakref * double-conversion * gast * farmhash * nsync * gemmlowp * abseil-cpp * boring-ssl * google-apis * cub * highwayhash * abseil-pypi * eigen * arm_neon_x86_sse * fft * grpc * re2 Although some of these libraries are available in factory they could not be used as explicit versions are needed or bazel or the build system links them in the wrong way. - mpilibpath_configure_py.patch changes the search path for the mpi to also include lib64/ - no_aws_and_googlecloud.patch removes the dependence of aws, googlecloud and kafaka apis, as this version is not compiled with the support of this apis.- Initial commit of Tensorflow 1.4 not all requirement could be met by the distribution packages and the sources have to be included. This is true for - Eigen - protobuf - grpc - lmdb - json-cpp The build itself is now based on bazel and creates the pip package which is then extracted from the build environmentlamb03 1645215947 5fcd8f5655a7b71af120813faee4190f2287983d2.6.0-bp153.2.3.12.6.0-bp153.2.3.1 debug.build-id5fcd8f5655a7b71af120813faee4190f2287983dcd8f5655a7b71af120813faee4190f2287983d.debugusrlibhpcgnu7tensorflow22.6.0lib64libiomp5.so-2.6.0-bp153.2.3.1.x86_64.debug/usr/lib//usr/lib/debug//usr/lib/debug/.build-id//usr/lib/debug/.build-id/5f//usr/lib/debug/usr//usr/lib/debug/usr/lib//usr/lib/debug/usr/lib/hpc//usr/lib/debug/usr/lib/hpc/gnu7//usr/lib/debug/usr/lib/hpc/gnu7/tensorflow2//usr/lib/debug/usr/lib/hpc/gnu7/tensorflow2/2.6.0//usr/lib/debug/usr/lib/hpc/gnu7/tensorflow2/2.6.0/lib64/-fmessage-length=0 -grecord-gcc-switches -O2 -Wall -D_FORTIFY_SOURCE=2 -fstack-protector-strong -funwind-tables -fasynchronous-unwind-tables -fstack-clash-protection -gobs://build.opensuse.org/openSUSE:Maintenance:17392/openSUSE_Backports_SLE-15-SP3_Update/5ff9bca2e88c298f05496bf69cb3167d-tensorflow2.openSUSE_Backports_SLE-15-SP3_Update:hpccpioxz5x86_64-suse-linuxdirectoryELF 64-bit LSB shared object, x86-64, version 1 (SYSV), dynamically linked, BuildID[sha1]=5fcd8f5655a7b71af120813faee4190f2287983d, with debug_info, not strippedPIcrpZ*9tensorflow2_2_6_0-gnu-hpc-debugsource(x86-64)2.6.0-bp153.2.3.1utf-8221821b4b0250e677e6c685b09e5be210053389544b94eb8d983be1c72b8fe15?7zXZ !t/c] crt:bLL ܕ6!;{e(ش6hJjLs^V[6FB$Ϝ\zW80 g ۹"И7u'γj^m^SڎqIE%U^T0N3vfW8!¦.cn*zTa 1Ԥx3&N,F5Wمcȵͦ i[ Y_"=E*FW:{6Yb lwcړO<ۺᙪx 5`yA}эF2eTʱ\p;`X]y`B_>6SN))^PmƪCB[%IUպ!weX{Qq;ipt$vtk{ h&㼯ҳIƠ}Sn}D6I.OS;te!-6"#ԇ`܊!\;!zj K)VϾOjaSg)] zvF4z0,<߶ҝSK|o~SJԘn-l%xe$&vň홒7@\f,ؼ>8=%S1ǖZ'Đ2ql8\eق+O=ZFIv>,K4|T, 3Jw׽%9Ni-NeuM8g+nzbo!GjK b 4<;-7;&g ]2`y6v.J[`{ $IUYQQTE&n c9H %<QH53+d ٔhz5H% wPnt~;`9Od!f? 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