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SECURITY.md
### Saved graphs and checkpoints When loading untrusted serialized computation graphs (in form of a `GraphDef`, `SavedModel`, or equivalent on-disk format), the set of computation primitives available to TensorFlow is powerful enough that you should assume that the TensorFlow process effectively executes arbitrary code. The risk of loading untrusted checkpoints depends on the code or graph that you
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RELEASE.md
* Added APIs for switching between interactive logging and absl logging. By default, Keras always writes the logs to stdout. However, this is not optimal in a non-interactive environment, where you don't have access to stdout, but can only view the logs. You can use `tf.keras.utils.disable_interactive_logging()` to write the logs to ABSL logging. You can also use `tf.keras.utils.enable_interactive_logging()`
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tensorflow/BUILD
"//tensorflow/lite/kernels:builtin_ops", "//tensorflow/lite/kernels:reference_ops", "//tensorflow/lite/schema:schema_fbs", "//tensorflow/lite/toco/logging:conversion_log_util", "//tensorflow/lite/toco/logging:toco_conversion_log_proto_cc", "//tensorflow/lite/toco:model_flags_proto_cc", "//tensorflow/lite/toco:toco_convert", "//tensorflow/lite/toco:toco_flags_proto_cc",
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.bazelrc
build:ios_x86_64 --cpu=ios_x86_64 build:ios_fat --config=ios build:ios_fat --ios_multi_cpus=armv7,arm64,i386,x86_64 # Config to use a mostly-static build and disable modular op registration # support (this will revert to loading TensorFlow with RTLD_GLOBAL in Python). # By default, TensorFlow will build with a dependence on # //tensorflow:libtensorflow_framework.so. build:monolithic --define framework_shared_object=false
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