// Recover data format from forward pass for gradient.
    std::string data_format;
    TF_RETURN_IF_ERROR(forward_attrs_.Get("data_format", &data_format));

    // Grad for A
    grad_inputs[0] = upstream_grad;
    grad_inputs[0]->Ref();

    // Grad for bias
    std::string name = "bias_add_grad";
    TF_RETURN_IF_ERROR(BiasAddGrad(ctx, upstream_grad, &grad_inputs[1],

)

updater_config_repository(name = "updater_config_repository")

python_register_multi_toolchains(
    name = "python",
    default_version = default_python_version,
    ignore_root_user_error = True,
    python_versions = [
        "3.9",
        "3.10",
        "3.11",
        "3.12",
    ],
    register_coverage_tool = True,
)

// Set the config in TF_ContextOptions.options.
// config should be a serialized tensorflow.ConfigProto proto.
// If config was not parsed successfully as a ConfigProto, record the
// error information in *status.
TF_CAPI_EXPORT extern void TFE_ContextOptionsSetConfig(
    TFE_ContextOptions* options, const void* proto, size_t proto_len,
    TF_Status* status);

      "' (available: ");
  // Ensure deterministic (sorted) order in the error message
  std::set<string> factories_sorted;
  for (const auto& factory : GetFactories())
    factories_sorted.insert(factory.first);
  const char* comma = "";
  for (const string& factory : factories_sorted) {
    msg += comma + factory;
    comma = ", ";
  }
  msg += ")";

  return errors::InvalidArgument(msg.c_str());
}

using tensorflow::string;
using tensorflow::Tensor;
using tensorflow::TensorId;
using tensorflow::TensorShapeProto;
using tensorflow::VersionDef;
using tensorflow::errors::FailedPrecondition;
using tensorflow::errors::InvalidArgument;
using tensorflow::errors::OutOfRange;
using tensorflow::gtl::ArraySlice;
using tensorflow::strings::StrCat;

extern "C" {

        if [[ ! -f $target ]] && [[ ! -d $target ]]; then
            echo "$i" >> errors.txt
        fi
        if [[ -e errors.txt ]]; then
            echo "Broken links found:"
            cat errors.txt
            rm errors.txt
            false
        fi
    done
}

@test "No duplicate files on Windows" {
    cat <<EOF

}

class FunctionErrorInjectionPass : public tensorflow::FunctionOptimizationPass {
 public:
  FunctionErrorInjectionPass(string error_node, string error_device)
      : error_node_(error_node), error_device_(error_device) {}
  tensorflow::Status Run(const std::string& function_name,
                         const tensorflow::DeviceSet& device_set,
                         const tensorflow::ConfigProto& config_proto,

                      bool use_function) {
  AbstractTensorHandle* model_outputs[1];

  // Run the model.
  TF_RETURN_IF_ERROR(
      RunModel(forward, ctx, inputs, model_outputs, use_function));
  AbstractTensorHandlePtr model_out(model_outputs[0]);

  TF_Tensor* model_out_tensor;
  TF_RETURN_IF_ERROR(GetValue(model_out.get(), &model_out_tensor));
  int num_dims_out = TF_NumDims(model_out_tensor);
  TF_DeleteTensor(model_out_tensor);

    # Change "testsuite > testcase,system-out" to "testsuite > testcase > error"
    for p in testsuite._elem.xpath(".//system-out"):
      for c in p.getparent().xpath(".//error | .//failure"):
        c.text = p.text
      p.getparent().remove(p)
    # Remove duplicate results of the same exact test (e.g. due to retry
    # attempts)
    for p in testsuite._elem.xpath(".//error | .//failure"):
      # Sharded tests have target names like this:

build --copt="-mtune=generic" --copt="-march=armv8-a" --copt="-O3"

# Test-related settings below this point.
test --build_tests_only --keep_going --test_output=errors --verbose_failures=true
test --test_timeout=500,900,-1,-1
# Give only the list of failed tests at the end of the log
test --test_summary=short

# "nonpip" tests are regular py_test tests.