absl::Span<AbstractTensorHandle* const> inputs,
                              absl::Span<AbstractTensorHandle*> outputs) {
  Tape tape(/*persistent=*/false);
  tape.Watch(inputs[0]);  // Watch x.
  AbstractTensorHandle* exp_output;
  TF_RETURN_IF_ERROR(ops::Exp(ctx, inputs[0], &exp_output, "Exp"));
  std::unique_ptr<GradientFunction> gradient_function(
      new PassThroughGradientFunction);

      bool use_batch)
      : vspace_(vspace), use_batch_(use_batch) {
    call_state_.emplace(nullptr, false);
  }

  virtual ~ForwardAccumulator() {
    for (auto accumulated : accumulated_gradients_) {
      vspace_.DeleteGradient(accumulated.second);
    }
  }

  // Tell the forward accumulator to watch tensor_id, with a Tensor tangent

             absl::Span<AbstractTensorHandle* const> inputs,
             absl::Span<AbstractTensorHandle*> outputs) -> Status {
    Tape tape(/*persistent=*/false);
    for (size_t i{}; i < inputs.size(); ++i) {
      tape.Watch(inputs[i]);
    }
    std::vector<AbstractTensorHandle*> temp_outputs(1);
    AbstractContextPtr tape_ctx(new TapeContext(ctx, &tape, grad_registry));
    TF_RETURN_IF_ERROR(

void TapeVSpace::MarkAsResult(AbstractTensorHandle* gradient) const {}

void TapeVSpace::DeleteGradient(AbstractTensorHandle* gradient) const {
  gradient->Unref();
}

void Tape::Watch(const AbstractTensorHandle* t) {
  GradientTape::Watch(ToId(t));
}
void Tape::RecordOperation(absl::Span<AbstractTensorHandle* const> inputs,
                           absl::Span<AbstractTensorHandle* const> outputs,

# simple" is MUCH more important than "don't repeat yourself."

# -e: abort script if one command fails
# -u: error if undefined variable used
# -x: log all commands
# -o pipefail: entire command fails if pipe fails. watch out for yes | ...
# -o history: record shell history
# -o allexport: export all functions and variables to be available to subscripts
#               (affects 'source $TFCI')
set -euxo pipefail -o history -o allexport

Status RecordOperationWithNullGradientFunctionModel(
    AbstractContext* ctx, absl::Span<AbstractTensorHandle* const> inputs,
    absl::Span<AbstractTensorHandle*> outputs) {
  Tape tape(/*persistent=*/false);
  tape.Watch(inputs[0]);
  AbstractTensorHandle* neg_output;
  TF_RETURN_IF_ERROR(ops::Neg(ctx, inputs[0], &neg_output, "Neg"));
  tape.RecordOperation(inputs, {neg_output}, nullptr, "Neg");
  return tape.ComputeGradient(ctx,

export TF_VER_MAJOR=$(awk '/#define TF_MAJOR_VERSION/ {print $3}' tensorflow/core/public/version.h)
export TF_VER_MINOR=$(awk '/#define TF_MINOR_VERSION/ {print $3}' tensorflow/core/public/version.h)
export TF_VER_PATCH=$(awk '/#define TF_PATCH_VERSION/ {print $3}' tensorflow/core/public/version.h)

# Note: in awk, "print $N" prints the Nth "field", where fields are strings separated

}

# Note: this is excluded on the full code base, since any submitted code must
# have passed Google's internal style guidelines.
@test "Check formatting for C++ files" {
    skip "clang-format doesn't match internal clang-format checker"
    echo "clang-format is recommended. Here are the suggested changes:"
    echo "============================="

    // Compare that the graphs match.
    GraphDef expected_gdef;
    GraphDef gdef;
    EXPECT_TRUE(GetGraphDef(expected_graph_, &expected_gdef));
    EXPECT_TRUE(GetGraphDef(graph_, &gdef));
    TF_EXPECT_GRAPH_EQ(expected_gdef, gdef);

    // Compare that the output of the gradients of both graphs match.
    RunGraphsAndCompareOutputs(grad_outputs, expected_grad_outputs);
  }

# manylinux2014-compatible packages.
COPY builder.devtoolset/fixlinks_aarch64.sh /fixlinks.sh
COPY builder.devtoolset/rpm-patch.sh /rpm-patch.sh
COPY builder.devtoolset/build_devtoolset.sh /build_devtoolset.sh
COPY builder.devtoolset/gcc9-fixups.patch /gcc9-fixups.patch
COPY builder.devtoolset/stringop_trunc.patch /stringop_trunc.patch

RUN /build_devtoolset.sh devtoolset-10 /dt10

# Build later version of patchelf that is not so buggy