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tensorflow/c/eager/unified_api_testutil.h
absl::Span<AbstractTensorHandle*>)>; // Runs `model` maybe wrapped in a function call op. This can be thought as // being equivalent to the following python code. // // if use_function: // outputs = tf.function(model)(inputs) // else: // outputs = model(inputs) Status RunModel(Model model, AbstractContext* ctx,
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tensorflow/c/eager/tfe_op_attrs_internal.h
#include "tensorflow/c/conversion_macros.h" #include "tensorflow/c/eager/abstract_op_attrs.h" #include "tensorflow/c/tf_status.h" #include "tensorflow/core/framework/attr_value.pb.h" // An equivalent of a tensorflow::NameAttrList protocol buffer, but used in ways // that sometimes do not require serialization. typedef struct TFE_OpAttrs TFE_OpAttrs; typedef struct TFE_Context TFE_Context; typedef struct TFE_Op TFE_Op;
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SECURITY.md
computation [**graphs**](https://developers.google.com/machine-learning/glossary/#graph). Since models are practically programs that TensorFlow executes, using untrusted models or graphs is equivalent to running untrusted code. If you need to run untrusted models, execute them inside a [**sandbox**](https://developers.google.com/code-sandboxing). Memory corruptions
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ci/official/utilities/setup.sh
# variables in it, OR may be left empty if the user has already exported the # relevant variables in their environment. Because of 'set -o allexport' above # (which is equivalent to "set -a"), every variable in the file is exported # for other files to use. # # Separately, if TFCI is set *and* there are also additional TFCI_ variables
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