graph_outputs.size(), graph_outputs.data(),
                                   nullptr, nullptr, name_.data(), s);
    *f = new GraphFunction(std::move(func->record->fdef()));
    TF_DeleteFunction(func);
    TF_RETURN_IF_ERROR(StatusFromTF_Status(s));
    TF_DeleteStatus(s);
    return absl::OkStatus();
  }

# it easier to combine settings later.
selects.config_setting_group(
    name = "mobile",
    match_any = [
        ":android",
        ":chromiumos",
        # TODO(jakeharmon8): Move all platform configs to TSL with fuchsia (non-blocking)
        ":fuchsia",
        ":emscripten",
        ":ios",
    ],
    visibility = ["//visibility:public"],
)

        and `tf.data.Dataset.range`.
    *   Promote `tf.data.Options.experimental_deterministic` API to
        `tf.data.Options.deterministic` and deprecate the experimental endpoint.
    *   Move autotuning options
        from`tf.data.Options.experimental_optimization.autotune*` to a newly
        created `tf.data.Options.autotune.*` and remove support for

  std::vector<TensorHandlePtr> vector_handles;
  vector_handles.reserve(2);
  vector_handles.push_back(std::move(two_vector));
  vector_handles.push_back(std::move(three_vector));
  std::unique_ptr<ParallelTensor> unknown_length_vector =
      ParallelTensor::FromTensorHandles(
          parallel_device, std::move(vector_handles), status.get());
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());

    }
  }

  TF_DeleteTensor(analytical_tensor);
  delete[] danalytical;
}

Model BuildGradModel(Model forward, GradientRegistry registry) {
  return [forward_model = std::move(forward),
          grad_registry = std::move(registry)](
             AbstractContext* ctx,
             absl::Span<AbstractTensorHandle* const> inputs,
             absl::Span<AbstractTensorHandle*> outputs) -> Status {

namespace tensorflow {
namespace tracing {
namespace graph {
GraphFunction::GraphFunction(FunctionDef fdef)
    : AbstractFunction(kGraph),
      func_record_(new FunctionRecord(std::move(fdef), {}, true)) {}
GraphFunction::~GraphFunction() {}
Status GraphFunction::GetFunctionDef(const FunctionDef **fdef) {
  *fdef = &(func_record_->fdef());
  return absl::OkStatus();
}
}  // namespace graph

#include "tensorflow/c/eager/tfe_op_attrs_internal.h"  // IWYU pragma: export
#include "tensorflow/c/eager/tfe_tensor_debug_info_internal.h"  // IWYU pragma: export

// TODO(b/154564140): Move this to its own header. This requires splitting
// c_api_experimental.h
struct TFE_ContextOptions {
  TF_SessionOptions session_options;
  // true if async execution is enabled.
  bool async = false;

    tensor_tape_[o.GetID()] = op_id;
    tensor_usage_[o.GetID()] = 1;
    tensors.push_back(o);
  }
  op_tape_[op_id] = OpTapeEntry<BackwardFunction, TapeTensor>{
      op_type, std::move(tensors), std::move(ids), backward_function_getter(),
      backward_function_deleter};
}

template <typename Gradient, typename BackwardFunction, typename TapeTensor>

    $zulu_root = \"c:/openjdk\"; \
    (New-Object Net.WebClient).DownloadFile($zulu_url, $zulu_zip); \
    [System.IO.Compression.ZipFile]::ExtractToDirectory($zulu_zip, \"c:/temp\"); \
    Move-Item $zulu_extracted_path -Destination $zulu_root; \
    Remove-Item $zulu_zip; \
    $old_path = [Environment]::GetEnvironmentVariable(\"PATH\", \"Machine\"); \

APPLE_BAZEL_FILES = [
    'tensorflow/lite/ios/BUILD', 'tensorflow/lite/objc/BUILD',
    'tensorflow/lite/swift/BUILD',
    'tensorflow/lite/tools/benchmark/experimental/ios/BUILD'
]

# List of files to move when building for iOS.
IOS_FILES = [
    'tensorflow/lite/objc/TensorFlowLiteObjC.podspec',
    'tensorflow/lite/swift/TensorFlowLiteSwift.podspec',
]


class UserInputError(Exception):
  pass