Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful.

# explicitly, *after* checking if update_version.py needs to be run for a
# nightly job. update_version.py affects TF_VER_SUFFIX, TF_VER_PYTHON, and
# TF_VER_FULL.

# Note: in awk, the command '/search/ {commands}' applies the commands to any line that
# matches the /search/ regular expression. "print $N" prints the Nth "field",
# where fields are strings separated by whitespace.

on:
  push:
    branches:
      - master
      
permissions: {}

jobs:
  create-issue-on-pr-rollback:
    runs-on: ubuntu-latest
    permissions:
      contents: read
      issues: write
      pull-requests: read
    if: |
      github.repository == 'tensorflow/tensorflow' &&
      startsWith(github.event.head_commit.message, 'Rollback of PR #')
    steps:

#include "tensorflow/c/experimental/filesystem/plugins/posix/copy_file.h"

namespace tf_posix_filesystem {

// Transfers up to `size` bytes from `dst_fd` to `src_fd`.
//
// This method uses a temporary buffer to hold contents.
int CopyFileContents(int dst_fd, int src_fd, off_t size) {
  // Use a copy buffer of 128KB but don't store it on the stack.
  constexpr static size_t kPosixCopyFileBufferSize = 128 * 1024;

# limitations under the License.
# ==============================================================================

name: ARM CI

on:
  push:
    branches:
      - master
      - r2.**
permissions:
  contents: read

jobs:
  build:
    # Don't do this in forks, and if labeled, only for 'kokoro:force-run'

    auto fcmp = block_map_.upper_bound(fmax);
    if (fcmp != block_map_.begin() && key < (--fcmp)->first) {
      return TF_SetStatus(status, TF_INTERNAL,
                          "Block cache contents are inconsistent.");
    }
  }

  Trim();

  return TF_SetStatus(status, TF_OK, "");
}

void RamFileBlockCache::MaybeFetch(const Key& key,

  EXPECT_EQ(TF_INT32, TF_TensorType(out));
  EXPECT_EQ(0, TF_NumDims(out));  // scalar
  ASSERT_EQ(sizeof(int32), TF_TensorByteSize(out));
  int32* output_contents = static_cast<int32*>(TF_TensorData(out));
  EXPECT_EQ(3 + 2, *output_contents);

  // Add another operation to the graph.
  TF_Operation* neg = Neg(add, graph, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

          // Expect a partial read.
          EXPECT_EQ(got.size(), size - offset)
              << "block size = " << block_size << ", offset = " << offset
              << ", n = " << n;
        }
        // Verify the contents of the read.
        std::vector<char>::const_iterator begin = buf.begin() + offset;
        std::vector<char>::const_iterator end =
            offset + n > buf.size() ? buf.end() : begin + n;

    if (TF_GetCode(status.get()) != TF_OK) {
      ADD_FAILURE() << tag << " -- " << TF_Message(status.get());
      continue;
    }
    TFE_DeleteTensorHandle(hdevice2);

    // Ensure that the contents are the same!
    TF_Tensor* tcopy = TFE_TensorHandleResolve(hcopy, status.get());
    TFE_DeleteTensorHandle(hcopy);
    if (TF_GetCode(status.get()) != TF_OK) {
      ADD_FAILURE() << tag;
      continue;
    }

vulnerabilities to the vendor of the affected hardware accelerator.

## Reporting vulnerabilities

### Vulnerabilities in TensorFlow

This document covers different use cases for TensorFlow together with comments
whether these uses were recommended or considered safe, or where we recommend
some form of isolation when dealing with untrusted data. As a result, this
document also outlines what issues we consider as TensorFlow security