pkg debug/macho, const CpuArm = 12
pkg debug/macho, const CpuArm Cpu
pkg debug/macho, const CpuPpc = 18
pkg debug/macho, const CpuPpc Cpu
pkg debug/macho, const CpuPpc64 = 16777234
pkg debug/macho, const CpuPpc64 Cpu
pkg debug/macho, const MagicFat = 3405691582
pkg debug/macho, const MagicFat uint32
pkg debug/macho, const TypeBundle = 8
pkg debug/macho, const TypeBundle Type
pkg debug/macho, const TypeDylib = 6

pkg debug/elf, const R_MIPS_PC16 R_MIPS
pkg debug/elf, const R_MIPS_PJUMP = 35
pkg debug/elf, const R_MIPS_PJUMP R_MIPS
pkg debug/elf, const R_MIPS_REL16 = 33
pkg debug/elf, const R_MIPS_REL16 R_MIPS
pkg debug/elf, const R_MIPS_REL32 = 3
pkg debug/elf, const R_MIPS_REL32 R_MIPS
pkg debug/elf, const R_MIPS_RELGOT = 36
pkg debug/elf, const R_MIPS_RELGOT R_MIPS
pkg debug/elf, const R_MIPS_SCN_DISP = 32

absl::optional<std::vector<std::unique_ptr<ParallelTensor>>>
ParallelDevice::Execute(TFE_Context* context,
                        const std::vector<ParallelTensor*>& inputs,
                        const char* operation_name,
                        const TFE_OpAttrs* attributes, int expected_max_outputs,
                        TF_Status* status) const {
  std::vector<PartialTensorShape> expected_output_shapes(expected_max_outputs);

pkg debug/elf, const R_AARCH64_CALL26 = 283
pkg debug/elf, const R_AARCH64_CALL26 R_AARCH64
pkg debug/elf, const R_AARCH64_CONDBR19 = 280
pkg debug/elf, const R_AARCH64_CONDBR19 R_AARCH64
pkg debug/elf, const R_AARCH64_COPY = 1024
pkg debug/elf, const R_AARCH64_COPY R_AARCH64
pkg debug/elf, const R_AARCH64_GLOB_DAT = 1025
pkg debug/elf, const R_AARCH64_GLOB_DAT R_AARCH64
pkg debug/elf, const R_AARCH64_GOT_LD_PREL19 = 309

TFE_Op* SendOp(TFE_Context* ctx, TFE_TensorHandle* in,
               const std::string& op_name, const std::string& send_device,
               const std::string& recv_device,
               tensorflow::uint64 send_device_incarnation);

// Return a RecvOp op `op_name` with the attributes `send_device`,
// `recv_device`, and `send_device_incarnation` set.
TFE_Op* RecvOp(TFE_Context* ctx, const std::string& op_name,

pkg debug/elf, const R_LARCH_SOP_SR R_LARCH #46229
pkg debug/elf, const R_LARCH_SOP_SUB = 32 #46229
pkg debug/elf, const R_LARCH_SOP_SUB R_LARCH #46229
pkg debug/elf, const R_LARCH_SUB16 = 53 #46229
pkg debug/elf, const R_LARCH_SUB16 R_LARCH #46229
pkg debug/elf, const R_LARCH_SUB24 = 54 #46229
pkg debug/elf, const R_LARCH_SUB24 R_LARCH #46229
pkg debug/elf, const R_LARCH_SUB32 = 55 #46229

}

void TF_AttrBuilderSetTypeList(TF_AttrBuilder* builder, const char* attr_name,
                               const TF_DataType* values, int num_values) {
  auto iter = builder->attr_names.insert(attr_name).first;
  builder->Set(*iter, tensorflow::gtl::ArraySlice<const tensorflow::DataType>(
                          reinterpret_cast<const tensorflow::DataType*>(values),
                          num_values));
}

                       GradientFunction* gradient_function,
                       const string& op_name = "");
  // Returns whether any tensor in a list of tensors is being watched and has
  // a trainable dtype.
  bool ShouldRecord(
      absl::Span<const AbstractTensorHandle* const> tensors) const;
  // Unwatches this tensor on the tape. Mainly used for cleanup when deleting
  // eager tensors.

pkg go/types, const Float32 BasicKind
pkg go/types, const Float64 = 14
pkg go/types, const Float64 BasicKind
pkg go/types, const Int = 2
pkg go/types, const Int BasicKind
pkg go/types, const Int16 = 4
pkg go/types, const Int16 BasicKind
pkg go/types, const Int32 = 5
pkg go/types, const Int32 BasicKind
pkg go/types, const Int64 = 6
pkg go/types, const Int64 BasicKind
pkg go/types, const Int8 = 3
pkg go/types, const Int8 BasicKind

  // that the argument need to be handled by a custom device.
  virtual bool HasCustomDeviceInput() const = 0;

  virtual const tensorflow::OpDef* OpDef() const = 0;

  virtual absl::Status InputLength(const char* input_name, int* length) = 0;
  virtual absl::Status OutputLength(const char* output_name, int* length) = 0;

  // Set stack trace to be used for potential async error reporting.