required: true
  - type: textarea
    id: code-to-reproduce
    attributes:
      label: Standalone code to reproduce the issue
      description: Provide a reproducible test case that is the bare minimum necessary to generate the problem. Please share a link to Colab, Jupyter, or any notebook.
      placeholder: Tell us what you see!
      value:
      render: shell
    validations:
      required: true

# Build with the AVX instruction set when on macOS x86
build:release_macos_x86 --config=avx_linux
build:release_macos_x86 --cpu=darwin
# Target Catalina as the minimum compatible OS version
build:release_macos_x86 --macos_minimum_os=10.15
build:release_macos_x86 --action_env MACOSX_DEPLOYMENT_TARGET=10.15

# Build configs for macOS Arm64
build:release_macos_arm64 --config=release_macos_base

               tensorflow::uint64 send_device_incarnation);

// Return a 1-D INT32 tensor containing a single value 1.
TFE_TensorHandle* TestAxisTensorHandle(TFE_Context* ctx);

// Return an op taking minimum of `input` long `axis` dimension.
TFE_Op* MinOp(TFE_Context* ctx, TFE_TensorHandle* input,
              TFE_TensorHandle* axis);

// If there is a device of type `device_type`, returns true

*   TensorFlow 1.7 may be the last time we support Cuda versions below 8.0.
    Starting with TensorFlow 1.8 release, 8.0 will be the minimum supported
    version.
*   TensorFlow 1.7 may be the last time we support cuDNN versions below 6.0.
    Starting with TensorFlow 1.8 release, 6.0 will be the minimum supported
    version.

## Thanks to our Contributors

// executed in sequence. Assigning nodes to different executors allows executing
// nodes in parallel.
// in_flight_nodes_limit: when is_async is true, this value controls the
// maximum number of in flight async nodes. Enqueuing of additional async ops
// after the limit is reached blocks until some inflight nodes finishes.
// The effect is bounding the memory held by inflight TensorHandles that are

your report:

  - A descriptive title
  - Your name and affiliation (if any).
  - A description of the technical details of the vulnerabilities.
  - A minimal example of the vulnerability. It is very important to let us know
    how we can reproduce your findings. For memory corruption triggerable in
    TensorFlow models, please demonstrate an exploit against one of Alphabet's

// blocks read from GCS. Specified in MB.
constexpr char kMaxCacheSize[] = "GCS_READ_CACHE_MAX_SIZE_MB";
constexpr size_t kDefaultMaxCacheSize = 0;
// The environment variable that overrides the maximum staleness of cached file
// contents. Once any block of a file reaches this staleness, all cached blocks
// will be evicted on the next read.
constexpr char kMaxStaleness[] = "GCS_READ_CACHE_MAX_STALENESS";

    default_cc_opt_flags = '/arch:AVX'
  else:
    # On all other platforms, no longer use `-march=native` as this can result
    # in instructions that are too modern being generated. Users that want
    # maximum performance should compile TF in their environment and can pass
    # `-march=native` there.
    # See https://github.com/tensorflow/tensorflow/issues/45744 and duplicates
    default_cc_opt_flags = '-Wno-sign-compare'