We'd love to accept your patches! Before we can take them, we have to jump a couple of legal hurdles.

Please fill out either the individual or corporate Contributor License Agreement (CLA).

  * If you are an individual writing original source code and you're sure you own the intellectual property, then you'll need to sign an [individual CLA](https://code.google.com/legal/individual-cla-v1.0.html).

  TF_Operation* feed4 =
      TF_GraphOperationByName(graph, "imported4/imported2/feed");

  // Check that imported `imported3/scalar` has remapped control dep from
  // original graph and imported control dep
  num_control_inputs = TF_OperationGetControlInputs(
      scalar4, control_inputs, TF_OperationNumControlInputs(scalar4));
  ASSERT_EQ(2, num_control_inputs);

        # TF's CI runs 'git fetch origin "pull/PR#/merge:pull_branch"'
        # To get the as-merged branch during the CI tests
        git diff --diff-filter ACMRT --name-only pull_branch^ pull_branch > $BATS_FILE_TMPDIR/changed_files
    else
        # If the branch is not present, then diff against origin/master
        git diff --diff-filter ACMRT --name-only origin/master > $BATS_FILE_TMPDIR/changed_files
    fi
}

namespace tensorflow {

namespace {

// Managing context for the DLManagedTensor, will manage the lifetime of
// DLManagedTensor. When calling DLManagedTensor::deleter, it will notify the
// original framework of destruction, and this context will be deleted also.
struct TfDlManagedTensorCtx {
  TensorReference reference;
  std::vector<int64_t> shape;
  std::vector<int64_t> strides;
  DLManagedTensor tensor;

      tensorflow::GetBuildXlaOpsPassFlags();
  bool original = flags->tf_xla_enable_lazy_compilation;
  flags->tf_xla_enable_lazy_compilation = enable;
  return original;
}

unsigned char TF_SetTfXlaCpuGlobalJit(unsigned char enable) {
  tensorflow::MarkForCompilationPassFlags* flags =
      tensorflow::GetMarkForCompilationPassFlags();
  bool original = flags->tf_xla_cpu_global_jit;

  // Method to execute an operation.
  //
  // Arguments provide enough information to reconstruct the original `TFE_Op`,
  // or construct a transformed version, by inspecting the passed `op`.
  //
  // TFE_OpGetDevice(op) records the original placement of the operation. It may
  // be an empty string if no device was explicitly requested, but will

    // would otherwise deadlock).
    if (TF_GetCode(status) != TF_OK &&
        (first_bad_status == nullptr
         // Prefer propagating non-cancellation related statuses to avoid
         // shadowing the original failure.
         || TF_GetCode(first_bad_status.get()) == TF_CANCELLED)) {
      first_bad_status.reset(TF_NewStatus());
      TF_SetStatus(first_bad_status.get(), TF_GetCode(status),
                   TF_Message(status));

)

# buildifier: disable=same-origin-load
load("@rules_python//python:repositories.bzl", "py_repositories")

py_repositories()

load("@rules_python//python:repositories.bzl", "python_register_multi_toolchains")  # buildifier: disable=same-origin-load
load("@rules_python//python/pip_install:repositories.bzl", "pip_install_dependencies")

except (ImportError, AttributeError):
  pass

del importlib

# Delete modules that should be hidden from dir().
# Don't fail if these modules are not available.
# For e.g. this file will be originally placed under tensorflow/_api/v1 which
# does not have "python", "core" directories. Then, it will be copied
# to tensorflow/ which does have these two directories.
try:
  del python
except NameError:
  pass

)

# buildifier: disable=same-origin-load
load("@rules_python//python:repositories.bzl", "py_repositories")

py_repositories()

load("@rules_python//python:repositories.bzl", "python_register_toolchains")  # buildifier: disable=same-origin-load
load(
    "//tensorflow/tools/toolchains/python:python_repo.bzl",
    "python_repository",
)