TF_TensorByteSize(theta_tensor));

  // Initialize space for the numerical gradient.
  vector<float> dtheta_approx(num_elems);

  // Get theta shape and store in theta_dims.
  int num_dims = TF_NumDims(theta_tensor);
  vector<int64_t> theta_dims(num_dims);
  GetDims(theta_tensor, theta_dims.data());

  // Initialize auxilary data structures.

  # Just ignore any failures, they're probably not important
  try:
    r = JUnitXml.fromfile(f)
  except Exception as e:  # pylint: disable=broad-except
    print("Ignoring this XML parse failure in {}: ".format(f), str(e))

  source_file = re.search(
      r"/(bazel_pip|tensorflow)/.*", f.decode("utf-8")
  ).group(0)
  for testsuite in r:
    testsuite._elem.set("source_file", source_file)
    # Remove empty testcases

        TFE_NewOp(context, "Const", status), TFE_DeleteOp);
    if (TF_GetCode(status) != TF_OK) return nullptr;
    TFE_OpSetDevice(const_op.get(), underlying_devices_[device_index].c_str(),
                    status);
    if (TF_GetCode(status) != TF_OK) return nullptr;
    TFE_OpSetAttrTensor(const_op.get(), "value", tensor.get(), status);
    if (TF_GetCode(status) != TF_OK) return nullptr;

  // only reasonable way to make variables with no aliasing using the eager C
  // API.
  std::string no_sharing = "cd2c89b7-88b7-44c8-ad83-06c2a9158347";
  TFE_OpSetAttrString(op.get(), "shared_name", no_sharing.c_str(),
                      no_sharing.length());
  TFE_OpSetDevice(op.get(), device, status);
  if (TF_GetCode(status) != TF_OK) return nullptr;
  TFE_TensorHandle* var_handle = nullptr;
  int num_retvals = 1;

      inputs, noutputs, outputs, output_names, 0, nullptr, nullptr, opts,
      description, status);
}

const char* TF_FunctionName(TF_Function* func) {
  return func->record->fdef().signature().name().c_str();
}

void TF_GraphCopyFunction(TF_Graph* g, const TF_Function* func,
                          const TF_Function* grad, TF_Status* status) {
  if (func == nullptr) {
    status->status = InvalidArgument(

absl-py==2.1.0 \
    --hash=sha256:526a04eadab8b4ee719ce68f204172ead1027549089702d99b9059f129ff1308 \
    --hash=sha256:7820790efbb316739cde8b4e19357243fc3608a152024288513dd968d7d959ff
    # via
    #   keras-nightly
    #   tb-nightly
astor==0.7.1 \
    --hash=sha256:95c30d87a6c2cf89aa628b87398466840f0ad8652f88eb173125a6df8533fb8d \
    --hash=sha256:fb503b9e2fdd05609fbf557b916b4a7824171203701660f0c55bbf5a7a68713e
    # via -r requirements.in
astunparse==1.6.3 \

absl-py==2.1.0 \
    --hash=sha256:526a04eadab8b4ee719ce68f204172ead1027549089702d99b9059f129ff1308 \
    --hash=sha256:7820790efbb316739cde8b4e19357243fc3608a152024288513dd968d7d959ff
    # via
    #   keras-nightly
    #   tb-nightly
astor==0.7.1 \
    --hash=sha256:95c30d87a6c2cf89aa628b87398466840f0ad8652f88eb173125a6df8533fb8d \
    --hash=sha256:fb503b9e2fdd05609fbf557b916b4a7824171203701660f0c55bbf5a7a68713e
    # via -r requirements.in
astunparse==1.6.3 \

//
// Questions left to address:
// * Might at some point need a way for callers to provide their own Env.
// * Maybe add TF_TensorShape that encapsulates dimension info.
//
// Design decisions made:
// * Backing store for tensor memory has an associated deallocation
//   function.  This deallocation function will point to client code
//   for tensors populated by the client.  So the client can do things
//   like shadowing a numpy array.