Module: Tensorflow

Defined in:
lib/tensorflow/core/util/event.rb,
lib/tensorflow/core/util/test_log.rb,
lib/tensorflow/core/protobuf/saver.rb,
lib/tensorflow/core/protobuf/master.rb,
lib/tensorflow/core/protobuf/config.rb,
lib/tensorflow/core/protobuf/worker.rb,
lib/tensorflow/core/framework/types.rb,
lib/tensorflow/core/framework/graph.rb,
lib/tensorflow/core/example/feature.rb,
lib/tensorflow/core/example/example.rb,
lib/tensorflow/core/framework/op_def.rb,
lib/tensorflow/core/framework/tensor.rb,
lib/tensorflow/core/framework/summary.rb,
lib/tensorflow/core/framework/function.rb,
lib/tensorflow/core/framework/variable.rb,
lib/tensorflow/core/framework/versions.rb,
lib/tensorflow/core/protobuf/meta_graph.rb,
lib/tensorflow/core/kernels/reader_base.rb,
lib/tensorflow/core/framework/attr_value.rb,
lib/tensorflow/core/framework/kernel_def.rb,
lib/tensorflow/core/framework/log_memory.rb,
lib/tensorflow/core/framework/cost_graph.rb,
lib/tensorflow/core/framework/step_stats.rb,
lib/tensorflow/core/protobuf/queue_runner.rb,
lib/tensorflow/core/protobuf/named_tensor.rb,
lib/tensorflow/core/framework/tensor_shape.rb,
lib/tensorflow/core/framework/tensor_slice.rb,
lib/tensorflow/core/util/saved_tensor_slice.rb,
lib/tensorflow/core/protobuf/worker_service.rb,
lib/tensorflow/core/protobuf/master_service.rb,
lib/tensorflow/core/util/memmapped_file_system.rb,
lib/tensorflow/core/protobuf/tensorflow_server.rb,
lib/tensorflow/core/framework/device_attributes.rb,
lib/tensorflow/core/framework/tensor_description.rb,
lib/tensorflow/core/framework/allocation_description.rb,
ext/Tensorflow_wrap.cxx

Defined Under Namespace

Modules: Grpc Classes: Character, ConstIterator, Double, Float, GC_VALUE, Int, Integer_Vector, Iterator, Long, Long_long, String_Vector, TF_Buffer, Tensor_Vector

Constant Summary collapse

Event =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.Event").msgclass
LogMessage =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.LogMessage").msgclass
SessionLog =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.SessionLog").msgclass
TaggedRunMetadata =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.TaggedRunMetadata").msgclass
EntryValue =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.EntryValue").msgclass
BenchmarkEntry =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.BenchmarkEntry").msgclass
BenchmarkEntries =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.BenchmarkEntries").msgclass
BuildConfiguration =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.BuildConfiguration").msgclass
CommitId =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.CommitId").msgclass
CPUInfo =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.CPUInfo").msgclass
MemoryInfo =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.MemoryInfo").msgclass
GPUInfo =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.GPUInfo").msgclass
PlatformInfo =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.PlatformInfo").msgclass
AvailableDeviceInfo =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.AvailableDeviceInfo").msgclass
MachineConfiguration =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.MachineConfiguration").msgclass
RunConfiguration =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.RunConfiguration").msgclass
TestResults =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.TestResults").msgclass
SaverDef =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.SaverDef").msgclass
CreateSessionRequest =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.CreateSessionRequest").msgclass
CreateSessionResponse =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.CreateSessionResponse").msgclass
ExtendSessionRequest =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.ExtendSessionRequest").msgclass
ExtendSessionResponse =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.ExtendSessionResponse").msgclass
RunStepRequest =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.RunStepRequest").msgclass
RunStepResponse =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.RunStepResponse").msgclass
CloseSessionRequest =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.CloseSessionRequest").msgclass
CloseSessionResponse =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.CloseSessionResponse").msgclass
ResetRequest =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.ResetRequest").msgclass
ResetResponse =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.ResetResponse").msgclass
ListDevicesRequest =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.ListDevicesRequest").msgclass
ListDevicesResponse =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.ListDevicesResponse").msgclass
GPUOptions =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.GPUOptions").msgclass
OptimizerOptions =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.OptimizerOptions").msgclass
GraphOptions =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.GraphOptions").msgclass
ThreadPoolOptionProto =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.ThreadPoolOptionProto").msgclass
ConfigProto =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.ConfigProto").msgclass
RunOptions =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.RunOptions").msgclass
RunMetadata =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.RunMetadata").msgclass
GetStatusRequest =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.GetStatusRequest").msgclass
GetStatusResponse =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.GetStatusResponse").msgclass
RegisterGraphRequest =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.RegisterGraphRequest").msgclass
RegisterGraphResponse =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.RegisterGraphResponse").msgclass
DeregisterGraphRequest =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.DeregisterGraphRequest").msgclass
DeregisterGraphResponse =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.DeregisterGraphResponse").msgclass
CleanupAllRequest =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.CleanupAllRequest").msgclass
CleanupAllResponse =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.CleanupAllResponse").msgclass
NamedTensor =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.NamedTensor").msgclass
ExecutorOpts =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.ExecutorOpts").msgclass
RunGraphRequest =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.RunGraphRequest").msgclass
RunGraphResponse =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.RunGraphResponse").msgclass
CleanupGraphRequest =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.CleanupGraphRequest").msgclass
CleanupGraphResponse =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.CleanupGraphResponse").msgclass
RecvTensorRequest =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.RecvTensorRequest").msgclass
RecvTensorResponse =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.RecvTensorResponse").msgclass
LoggingRequest =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.LoggingRequest").msgclass
LabeledStepStats =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.LabeledStepStats").msgclass
LoggingResponse =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.LoggingResponse").msgclass
TraceOpts =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.TraceOpts").msgclass
TracingRequest =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.TracingRequest").msgclass
TracingResponse =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.TracingResponse").msgclass
DataType =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.DataType").enummodule
GraphDef =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.GraphDef").msgclass
NodeDef =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.NodeDef").msgclass
BytesList =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.BytesList").msgclass
FloatList =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.FloatList").msgclass
Int64List =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.Int64List").msgclass
Feature =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.Feature").msgclass
Features =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.Features").msgclass
FeatureList =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.FeatureList").msgclass
FeatureLists =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.FeatureLists").msgclass
Example =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.Example").msgclass
SequenceExample =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.SequenceExample").msgclass
OpDef =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.OpDef").msgclass
OpDeprecation =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.OpDeprecation").msgclass
OpList =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.OpList").msgclass
TensorProto =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.TensorProto").msgclass
HistogramProto =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.HistogramProto").msgclass
Summary =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.Summary").msgclass
FunctionDefLibrary =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.FunctionDefLibrary").msgclass
FunctionDef =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.FunctionDef").msgclass
GradientDef =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.GradientDef").msgclass
VariableDef =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.VariableDef").msgclass
SaveSliceInfoDef =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.SaveSliceInfoDef").msgclass
VersionDef =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.VersionDef").msgclass
MetaGraphDef =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.MetaGraphDef").msgclass
CollectionDef =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.CollectionDef").msgclass
ReaderBaseState =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.ReaderBaseState").msgclass
AttrValue =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.AttrValue").msgclass
NameAttrList =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.NameAttrList").msgclass
KernelDef =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.KernelDef").msgclass
MemoryLogStep =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.MemoryLogStep").msgclass
MemoryLogTensorAllocation =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.MemoryLogTensorAllocation").msgclass
MemoryLogTensorDeallocation =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.MemoryLogTensorDeallocation").msgclass
MemoryLogTensorOutput =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.MemoryLogTensorOutput").msgclass
MemoryLogRawAllocation =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.MemoryLogRawAllocation").msgclass
MemoryLogRawDeallocation =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.MemoryLogRawDeallocation").msgclass
CostGraphDef =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.CostGraphDef").msgclass
AllocatorMemoryUsed =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.AllocatorMemoryUsed").msgclass
NodeOutput =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.NodeOutput").msgclass
NodeExecStats =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.NodeExecStats").msgclass
DeviceStepStats =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.DeviceStepStats").msgclass
StepStats =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.StepStats").msgclass
QueueRunnerDef =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.QueueRunnerDef").msgclass
NamedTensorProto =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.NamedTensorProto").msgclass
TensorShapeProto =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.TensorShapeProto").msgclass
TensorSliceProto =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.TensorSliceProto").msgclass
SavedSliceMeta =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.SavedSliceMeta").msgclass
SavedTensorSliceMeta =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.SavedTensorSliceMeta").msgclass
SavedSlice =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.SavedSlice").msgclass
SavedTensorSlices =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.SavedTensorSlices").msgclass
MemmappedFileSystemDirectoryElement =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.MemmappedFileSystemDirectoryElement").msgclass
MemmappedFileSystemDirectory =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.MemmappedFileSystemDirectory").msgclass
JobDef =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.JobDef").msgclass
ClusterDef =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.ClusterDef").msgclass
ServerDef =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.ServerDef").msgclass
DeviceAttributes =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.DeviceAttributes").msgclass
BusAdjacency =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.BusAdjacency").enummodule
TensorDescription =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.TensorDescription").msgclass
AllocationDescription =
Google::Protobuf::DescriptorPool.generated_pool.lookup("tensorflow.AllocationDescription").msgclass
TF_MAJOR_VERSION =
SWIG_From_int(static_cast< int >(0))
TF_MINOR_VERSION =
SWIG_From_int(static_cast< int >(8))
TF_PATCH_VERSION =
SWIG_From_int(static_cast< int >(0))
TF_VERSION_SUFFIX =
SWIG_FromCharPtr("")
TF_VERSION_STRING =
SWIG_FromCharPtr("(0.8.0)")
TF_GRAPH_DEF_VERSION_MIN_PRODUCER =
SWIG_From_int(static_cast< int >(0))
TF_GRAPH_DEF_VERSION_MIN_CONSUMER =
SWIG_From_int(static_cast< int >(0))
TF_GRAPH_DEF_VERSION =
SWIG_From_int(static_cast< int >(9))
TF_CHECKPOINT_VERSION_MIN_PRODUCER =
SWIG_From_int(static_cast< int >(0))
TF_CHECKPOINT_VERSION_MIN_CONSUMER =
SWIG_From_int(static_cast< int >(0))
TF_CHECKPOINT_VERSION =
SWIG_From_int(static_cast< int >(1))
TF_FLOAT =
SWIG_From_int(static_cast< int >(TF_FLOAT))
TF_DOUBLE =
SWIG_From_int(static_cast< int >(TF_DOUBLE))
TF_INT32 =
SWIG_From_int(static_cast< int >(TF_INT32))
TF_UINT8 =
SWIG_From_int(static_cast< int >(TF_UINT8))
TF_INT16 =
SWIG_From_int(static_cast< int >(TF_INT16))
TF_INT8 =
SWIG_From_int(static_cast< int >(TF_INT8))
TF_STRING =
SWIG_From_int(static_cast< int >(TF_STRING))
TF_COMPLEX64 =
SWIG_From_int(static_cast< int >(TF_COMPLEX64))
TF_COMPLEX =
SWIG_From_int(static_cast< int >(TF_COMPLEX))
TF_INT64 =
SWIG_From_int(static_cast< int >(TF_INT64))
TF_BOOL =
SWIG_From_int(static_cast< int >(TF_BOOL))
TF_QINT8 =
SWIG_From_int(static_cast< int >(TF_QINT8))
TF_QUINT8 =
SWIG_From_int(static_cast< int >(TF_QUINT8))
TF_QINT32 =
SWIG_From_int(static_cast< int >(TF_QINT32))
TF_BFLOAT16 =
SWIG_From_int(static_cast< int >(TF_BFLOAT16))
TF_QINT16 =
SWIG_From_int(static_cast< int >(TF_QINT16))
TF_QUINT16 =
SWIG_From_int(static_cast< int >(TF_QUINT16))
TF_UINT16 =
SWIG_From_int(static_cast< int >(TF_UINT16))
TF_COMPLEX128 =
SWIG_From_int(static_cast< int >(TF_COMPLEX128))
TF_HALF =
SWIG_From_int(static_cast< int >(TF_HALF))
TF_OK =
SWIG_From_int(static_cast< int >(TF_OK))
TF_CANCELLED =
SWIG_From_int(static_cast< int >(TF_CANCELLED))
TF_UNKNOWN =
SWIG_From_int(static_cast< int >(TF_UNKNOWN))
TF_INVALID_ARGUMENT =
SWIG_From_int(static_cast< int >(TF_INVALID_ARGUMENT))
TF_DEADLINE_EXCEEDED =
SWIG_From_int(static_cast< int >(TF_DEADLINE_EXCEEDED))
TF_NOT_FOUND =
SWIG_From_int(static_cast< int >(TF_NOT_FOUND))
TF_ALREADY_EXISTS =
SWIG_From_int(static_cast< int >(TF_ALREADY_EXISTS))
TF_PERMISSION_DENIED =
SWIG_From_int(static_cast< int >(TF_PERMISSION_DENIED))
TF_UNAUTHENTICATED =
SWIG_From_int(static_cast< int >(TF_UNAUTHENTICATED))
TF_RESOURCE_EXHAUSTED =
SWIG_From_int(static_cast< int >(TF_RESOURCE_EXHAUSTED))
TF_FAILED_PRECONDITION =
SWIG_From_int(static_cast< int >(TF_FAILED_PRECONDITION))
TF_ABORTED =
SWIG_From_int(static_cast< int >(TF_ABORTED))
TF_OUT_OF_RANGE =
SWIG_From_int(static_cast< int >(TF_OUT_OF_RANGE))
TF_UNIMPLEMENTED =
SWIG_From_int(static_cast< int >(TF_UNIMPLEMENTED))
TF_INTERNAL =
SWIG_From_int(static_cast< int >(TF_INTERNAL))
TF_UNAVAILABLE =
SWIG_From_int(static_cast< int >(TF_UNAVAILABLE))
TF_DATA_LOSS =
SWIG_From_int(static_cast< int >(TF_DATA_LOSS))

Class Method Summary collapse

Class Method Details

.double_reader(*args) ⇒ Object


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# File 'ext/Tensorflow_wrap.cxx', line 14853

SWIGINTERN VALUE
_wrap_double_reader(int argc, VALUE *argv, VALUE self) {
  TF_Tensor *arg1 = (TF_Tensor *) 0 ;
  double *arg2 = (double *) 0 ;
  int arg3 ;
  void *argp1 = 0 ;
  int res1 = 0 ;
  void *argp2 = 0 ;
  int res2 = 0 ;
  int val3 ;
  int ecode3 = 0 ;
  
  if ((argc < 3) || (argc > 3)) {
    rb_raise(rb_eArgError, "wrong # of arguments(%d for 3)",argc); SWIG_fail;
  }
  res1 = SWIG_ConvertPtr(argv[0], &argp1,SWIGTYPE_p_TF_Tensor, 0 |  0 );
  if (!SWIG_IsOK(res1)) {
    SWIG_exception_fail(SWIG_ArgError(res1), Ruby_Format_TypeError( "", "TF_Tensor *","tensorflow::double_reader", 1, argv[0] )); 
  }
  arg1 = reinterpret_cast< TF_Tensor * >(argp1);
  res2 = SWIG_ConvertPtr(argv[1], &argp2,SWIGTYPE_p_double, 0 |  0 );
  if (!SWIG_IsOK(res2)) {
    SWIG_exception_fail(SWIG_ArgError(res2), Ruby_Format_TypeError( "", "double *","tensorflow::double_reader", 2, argv[1] )); 
  }
  arg2 = reinterpret_cast< double * >(argp2);
  ecode3 = SWIG_AsVal_int(argv[2], &val3);
  if (!SWIG_IsOK(ecode3)) {
    SWIG_exception_fail(SWIG_ArgError(ecode3), Ruby_Format_TypeError( "", "int","tensorflow::double_reader", 3, argv[2] ));
  } 
  arg3 = static_cast< int >(val3);
  tensorflow::double_reader(arg1,arg2,arg3);
  return Qnil;
fail:
  return Qnil;
}

.int_reader(*args) ⇒ Object


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# File 'ext/Tensorflow_wrap.cxx', line 14816

SWIGINTERN VALUE
_wrap_int_reader(int argc, VALUE *argv, VALUE self) {
  TF_Tensor *arg1 = (TF_Tensor *) 0 ;
  int *arg2 = (int *) 0 ;
  int arg3 ;
  void *argp1 = 0 ;
  int res1 = 0 ;
  void *argp2 = 0 ;
  int res2 = 0 ;
  int val3 ;
  int ecode3 = 0 ;
  
  if ((argc < 3) || (argc > 3)) {
    rb_raise(rb_eArgError, "wrong # of arguments(%d for 3)",argc); SWIG_fail;
  }
  res1 = SWIG_ConvertPtr(argv[0], &argp1,SWIGTYPE_p_TF_Tensor, 0 |  0 );
  if (!SWIG_IsOK(res1)) {
    SWIG_exception_fail(SWIG_ArgError(res1), Ruby_Format_TypeError( "", "TF_Tensor *","tensorflow::int_reader", 1, argv[0] )); 
  }
  arg1 = reinterpret_cast< TF_Tensor * >(argp1);
  res2 = SWIG_ConvertPtr(argv[1], &argp2,SWIGTYPE_p_int, 0 |  0 );
  if (!SWIG_IsOK(res2)) {
    SWIG_exception_fail(SWIG_ArgError(res2), Ruby_Format_TypeError( "", "int *","tensorflow::int_reader", 2, argv[1] )); 
  }
  arg2 = reinterpret_cast< int * >(argp2);
  ecode3 = SWIG_AsVal_int(argv[2], &val3);
  if (!SWIG_IsOK(ecode3)) {
    SWIG_exception_fail(SWIG_ArgError(ecode3), Ruby_Format_TypeError( "", "int","tensorflow::int_reader", 3, argv[2] ));
  } 
  arg3 = static_cast< int >(val3);
  tensorflow::int_reader(arg1,arg2,arg3);
  return Qnil;
fail:
  return Qnil;
}

.long_long_reader(*args) ⇒ Object


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# File 'ext/Tensorflow_wrap.cxx', line 14779

SWIGINTERN VALUE
_wrap_long_long_reader(int argc, VALUE *argv, VALUE self) {
  TF_Tensor *arg1 = (TF_Tensor *) 0 ;
  long long *arg2 = (long long *) 0 ;
  int arg3 ;
  void *argp1 = 0 ;
  int res1 = 0 ;
  void *argp2 = 0 ;
  int res2 = 0 ;
  int val3 ;
  int ecode3 = 0 ;
  
  if ((argc < 3) || (argc > 3)) {
    rb_raise(rb_eArgError, "wrong # of arguments(%d for 3)",argc); SWIG_fail;
  }
  res1 = SWIG_ConvertPtr(argv[0], &argp1,SWIGTYPE_p_TF_Tensor, 0 |  0 );
  if (!SWIG_IsOK(res1)) {
    SWIG_exception_fail(SWIG_ArgError(res1), Ruby_Format_TypeError( "", "TF_Tensor *","tensorflow::long_long_reader", 1, argv[0] )); 
  }
  arg1 = reinterpret_cast< TF_Tensor * >(argp1);
  res2 = SWIG_ConvertPtr(argv[1], &argp2,SWIGTYPE_p_long_long, 0 |  0 );
  if (!SWIG_IsOK(res2)) {
    SWIG_exception_fail(SWIG_ArgError(res2), Ruby_Format_TypeError( "", "long long *","tensorflow::long_long_reader", 2, argv[1] )); 
  }
  arg2 = reinterpret_cast< long long * >(argp2);
  ecode3 = SWIG_AsVal_int(argv[2], &val3);
  if (!SWIG_IsOK(ecode3)) {
    SWIG_exception_fail(SWIG_ArgError(ecode3), Ruby_Format_TypeError( "", "int","tensorflow::long_long_reader", 3, argv[2] ));
  } 
  arg3 = static_cast< int >(val3);
  tensorflow::long_long_reader(arg1,arg2,arg3);
  return Qnil;
fail:
  return Qnil;
}

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# File 'ext/Tensorflow_wrap.cxx', line 14734

SWIGINTERN VALUE
_wrap_print_tensor(int argc, VALUE *argv, VALUE self) {
  TF_Tensor *arg1 = (TF_Tensor *) 0 ;
  void *argp1 = 0 ;
  int res1 = 0 ;
  
  if ((argc < 1) || (argc > 1)) {
    rb_raise(rb_eArgError, "wrong # of arguments(%d for 1)",argc); SWIG_fail;
  }
  res1 = SWIG_ConvertPtr(argv[0], &argp1,SWIGTYPE_p_TF_Tensor, 0 |  0 );
  if (!SWIG_IsOK(res1)) {
    SWIG_exception_fail(SWIG_ArgError(res1), Ruby_Format_TypeError( "", "TF_Tensor *","tensorflow::print_tensor", 1, argv[0] )); 
  }
  arg1 = reinterpret_cast< TF_Tensor * >(argp1);
  tensorflow::print_tensor(arg1);
  return Qnil;
fail:
  return Qnil;
}

.tensor_size(*args) ⇒ Object


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# File 'ext/Tensorflow_wrap.cxx', line 14755

SWIGINTERN VALUE
_wrap_tensor_size(int argc, VALUE *argv, VALUE self) {
  TF_Tensor *arg1 = (TF_Tensor *) 0 ;
  void *argp1 = 0 ;
  int res1 = 0 ;
  long long result;
  VALUE vresult = Qnil;
  
  if ((argc < 1) || (argc > 1)) {
    rb_raise(rb_eArgError, "wrong # of arguments(%d for 1)",argc); SWIG_fail;
  }
  res1 = SWIG_ConvertPtr(argv[0], &argp1,SWIGTYPE_p_TF_Tensor, 0 |  0 );
  if (!SWIG_IsOK(res1)) {
    SWIG_exception_fail(SWIG_ArgError(res1), Ruby_Format_TypeError( "", "TF_Tensor *","tensorflow::tensor_size", 1, argv[0] )); 
  }
  arg1 = reinterpret_cast< TF_Tensor * >(argp1);
  result = (long long)tensorflow::tensor_size(arg1);
  vresult = SWIG_From_long_SS_long(static_cast< long long >(result));
  return vresult;
fail:
  return Qnil;
}

.TF_CloseSession(*args) ⇒ Object


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# File 'ext/Tensorflow_wrap.cxx', line 14222

SWIGINTERN VALUE
_wrap_TF_CloseSession(int argc, VALUE *argv, VALUE self) {
  TF_Session *arg1 = (TF_Session *) 0 ;
  TF_Status *arg2 = (TF_Status *) 0 ;
  void *argp1 = 0 ;
  int res1 = 0 ;
  void *argp2 = 0 ;
  int res2 = 0 ;
  
  if ((argc < 2) || (argc > 2)) {
    rb_raise(rb_eArgError, "wrong # of arguments(%d for 2)",argc); SWIG_fail;
  }
  res1 = SWIG_ConvertPtr(argv[0], &argp1,SWIGTYPE_p_TF_Session, 0 |  0 );
  if (!SWIG_IsOK(res1)) {
    SWIG_exception_fail(SWIG_ArgError(res1), Ruby_Format_TypeError( "", "TF_Session *","TF_CloseSession", 1, argv[0] )); 
  }
  arg1 = reinterpret_cast< TF_Session * >(argp1);
  res2 = SWIG_ConvertPtr(argv[1], &argp2,SWIGTYPE_p_TF_Status, 0 |  0 );
  if (!SWIG_IsOK(res2)) {
    SWIG_exception_fail(SWIG_ArgError(res2), Ruby_Format_TypeError( "", "TF_Status *","TF_CloseSession", 2, argv[1] )); 
  }
  arg2 = reinterpret_cast< TF_Status * >(argp2);
  TF_CloseSession(arg1,arg2);
  return Qnil;
fail:
  return Qnil;
}

.TF_DeleteBuffer(*args) ⇒ Object


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# File 'ext/Tensorflow_wrap.cxx', line 13692

SWIGINTERN VALUE
_wrap_TF_DeleteBuffer(int argc, VALUE *argv, VALUE self) {
  TF_Buffer *arg1 = (TF_Buffer *) 0 ;
  void *argp1 = 0 ;
  int res1 = 0 ;
  
  if ((argc < 1) || (argc > 1)) {
    rb_raise(rb_eArgError, "wrong # of arguments(%d for 1)",argc); SWIG_fail;
  }
  res1 = SWIG_ConvertPtr(argv[0], &argp1,SWIGTYPE_p_TF_Buffer, 0 |  0 );
  if (!SWIG_IsOK(res1)) {
    SWIG_exception_fail(SWIG_ArgError(res1), Ruby_Format_TypeError( "", "TF_Buffer *","TF_DeleteBuffer", 1, argv[0] )); 
  }
  arg1 = reinterpret_cast< TF_Buffer * >(argp1);
  TF_DeleteBuffer(arg1);
  return Qnil;
fail:
  return Qnil;
}

.TF_DeleteSession(*args) ⇒ Object


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# File 'ext/Tensorflow_wrap.cxx', line 14251

SWIGINTERN VALUE
_wrap_TF_DeleteSession(int argc, VALUE *argv, VALUE self) {
  TF_Session *arg1 = (TF_Session *) 0 ;
  TF_Status *arg2 = (TF_Status *) 0 ;
  void *argp1 = 0 ;
  int res1 = 0 ;
  void *argp2 = 0 ;
  int res2 = 0 ;
  
  if ((argc < 2) || (argc > 2)) {
    rb_raise(rb_eArgError, "wrong # of arguments(%d for 2)",argc); SWIG_fail;
  }
  res1 = SWIG_ConvertPtr(argv[0], &argp1,SWIGTYPE_p_TF_Session, 0 |  0 );
  if (!SWIG_IsOK(res1)) {
    SWIG_exception_fail(SWIG_ArgError(res1), Ruby_Format_TypeError( "", "TF_Session *","TF_DeleteSession", 1, argv[0] )); 
  }
  arg1 = reinterpret_cast< TF_Session * >(argp1);
  res2 = SWIG_ConvertPtr(argv[1], &argp2,SWIGTYPE_p_TF_Status, 0 |  0 );
  if (!SWIG_IsOK(res2)) {
    SWIG_exception_fail(SWIG_ArgError(res2), Ruby_Format_TypeError( "", "TF_Status *","TF_DeleteSession", 2, argv[1] )); 
  }
  arg2 = reinterpret_cast< TF_Status * >(argp2);
  TF_DeleteSession(arg1,arg2);
  return Qnil;
fail:
  return Qnil;
}

.TF_DeleteSessionOptions(*args) ⇒ Object


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# File 'ext/Tensorflow_wrap.cxx', line 14169

SWIGINTERN VALUE
_wrap_TF_DeleteSessionOptions(int argc, VALUE *argv, VALUE self) {
  TF_SessionOptions *arg1 = (TF_SessionOptions *) 0 ;
  void *argp1 = 0 ;
  int res1 = 0 ;
  
  if ((argc < 1) || (argc > 1)) {
    rb_raise(rb_eArgError, "wrong # of arguments(%d for 1)",argc); SWIG_fail;
  }
  res1 = SWIG_ConvertPtr(argv[0], &argp1,SWIGTYPE_p_TF_SessionOptions, 0 |  0 );
  if (!SWIG_IsOK(res1)) {
    SWIG_exception_fail(SWIG_ArgError(res1), Ruby_Format_TypeError( "", "TF_SessionOptions *","TF_DeleteSessionOptions", 1, argv[0] )); 
  }
  arg1 = reinterpret_cast< TF_SessionOptions * >(argp1);
  TF_DeleteSessionOptions(arg1);
  return Qnil;
fail:
  return Qnil;
}

.TF_DeleteStatus(*args) ⇒ Object


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# File 'ext/Tensorflow_wrap.cxx', line 13753

SWIGINTERN VALUE
_wrap_TF_DeleteStatus(int argc, VALUE *argv, VALUE self) {
  TF_Status *arg1 = (TF_Status *) 0 ;
  void *argp1 = 0 ;
  int res1 = 0 ;
  
  if ((argc < 1) || (argc > 1)) {
    rb_raise(rb_eArgError, "wrong # of arguments(%d for 1)",argc); SWIG_fail;
  }
  res1 = SWIG_ConvertPtr(argv[0], &argp1,SWIGTYPE_p_TF_Status, 0 |  0 );
  if (!SWIG_IsOK(res1)) {
    SWIG_exception_fail(SWIG_ArgError(res1), Ruby_Format_TypeError( "", "TF_Status *","TF_DeleteStatus", 1, argv[0] )); 
  }
  arg1 = reinterpret_cast< TF_Status * >(argp1);
  TF_DeleteStatus(arg1);
  return Qnil;
fail:
  return Qnil;
}

.TF_DeleteTensor(*args) ⇒ Object


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# File 'ext/Tensorflow_wrap.cxx', line 13929

SWIGINTERN VALUE
_wrap_TF_DeleteTensor(int argc, VALUE *argv, VALUE self) {
  TF_Tensor *arg1 = (TF_Tensor *) 0 ;
  void *argp1 = 0 ;
  int res1 = 0 ;
  
  if ((argc < 1) || (argc > 1)) {
    rb_raise(rb_eArgError, "wrong # of arguments(%d for 1)",argc); SWIG_fail;
  }
  res1 = SWIG_ConvertPtr(argv[0], &argp1,SWIGTYPE_p_TF_Tensor, 0 |  0 );
  if (!SWIG_IsOK(res1)) {
    SWIG_exception_fail(SWIG_ArgError(res1), Ruby_Format_TypeError( "", "TF_Tensor *","TF_DeleteTensor", 1, argv[0] )); 
  }
  arg1 = reinterpret_cast< TF_Tensor * >(argp1);
  TF_DeleteTensor(arg1);
  return Qnil;
fail:
  return Qnil;
}

.TF_Dim(*args) ⇒ Object


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# File 'ext/Tensorflow_wrap.cxx', line 13998

SWIGINTERN VALUE
_wrap_TF_Dim(int argc, VALUE *argv, VALUE self) {
  TF_Tensor *arg1 = (TF_Tensor *) 0 ;
  int arg2 ;
  void *argp1 = 0 ;
  int res1 = 0 ;
  int val2 ;
  int ecode2 = 0 ;
  long long result;
  VALUE vresult = Qnil;
  
  if ((argc < 2) || (argc > 2)) {
    rb_raise(rb_eArgError, "wrong # of arguments(%d for 2)",argc); SWIG_fail;
  }
  res1 = SWIG_ConvertPtr(argv[0], &argp1,SWIGTYPE_p_TF_Tensor, 0 |  0 );
  if (!SWIG_IsOK(res1)) {
    SWIG_exception_fail(SWIG_ArgError(res1), Ruby_Format_TypeError( "", "TF_Tensor const *","TF_Dim", 1, argv[0] )); 
  }
  arg1 = reinterpret_cast< TF_Tensor * >(argp1);
  ecode2 = SWIG_AsVal_int(argv[1], &val2);
  if (!SWIG_IsOK(ecode2)) {
    SWIG_exception_fail(SWIG_ArgError(ecode2), Ruby_Format_TypeError( "", "int","TF_Dim", 2, argv[1] ));
  } 
  arg2 = static_cast< int >(val2);
  result = (long long)TF_Dim((TF_Tensor const *)arg1,arg2);
  vresult = SWIG_From_long_SS_long(static_cast< long long >(result));
  return vresult;
fail:
  return Qnil;
}

.TF_ExtendGraph(*args) ⇒ Object


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# File 'ext/Tensorflow_wrap.cxx', line 14280

SWIGINTERN VALUE
_wrap_TF_ExtendGraph(int argc, VALUE *argv, VALUE self) {
  TF_Session *arg1 = (TF_Session *) 0 ;
  void *arg2 = (void *) 0 ;
  size_t arg3 ;
  TF_Status *arg4 = (TF_Status *) 0 ;
  void *argp1 = 0 ;
  int res1 = 0 ;
  int res2 ;
  size_t val3 ;
  int ecode3 = 0 ;
  void *argp4 = 0 ;
  int res4 = 0 ;
  
  if ((argc < 4) || (argc > 4)) {
    rb_raise(rb_eArgError, "wrong # of arguments(%d for 4)",argc); SWIG_fail;
  }
  res1 = SWIG_ConvertPtr(argv[0], &argp1,SWIGTYPE_p_TF_Session, 0 |  0 );
  if (!SWIG_IsOK(res1)) {
    SWIG_exception_fail(SWIG_ArgError(res1), Ruby_Format_TypeError( "", "TF_Session *","TF_ExtendGraph", 1, argv[0] )); 
  }
  arg1 = reinterpret_cast< TF_Session * >(argp1);
  res2 = SWIG_ConvertPtr(argv[1],SWIG_as_voidptrptr(&arg2), 0, 0);
  if (!SWIG_IsOK(res2)) {
    SWIG_exception_fail(SWIG_ArgError(res2), Ruby_Format_TypeError( "", "void const *","TF_ExtendGraph", 2, argv[1] )); 
  }
  ecode3 = SWIG_AsVal_size_t(argv[2], &val3);
  if (!SWIG_IsOK(ecode3)) {
    SWIG_exception_fail(SWIG_ArgError(ecode3), Ruby_Format_TypeError( "", "size_t","TF_ExtendGraph", 3, argv[2] ));
  } 
  arg3 = static_cast< size_t >(val3);
  res4 = SWIG_ConvertPtr(argv[3], &argp4,SWIGTYPE_p_TF_Status, 0 |  0 );
  if (!SWIG_IsOK(res4)) {
    SWIG_exception_fail(SWIG_ArgError(res4), Ruby_Format_TypeError( "", "TF_Status *","TF_ExtendGraph", 4, argv[3] )); 
  }
  arg4 = reinterpret_cast< TF_Status * >(argp4);
  TF_ExtendGraph(arg1,(void const *)arg2,arg3,arg4);
  return Qnil;
fail:
  return Qnil;
}

.TF_GetBuffer(*args) ⇒ Object


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# File 'ext/Tensorflow_wrap.cxx', line 13713

SWIGINTERN VALUE
_wrap_TF_GetBuffer(int argc, VALUE *argv, VALUE self) {
  TF_Buffer *arg1 = (TF_Buffer *) 0 ;
  void *argp1 = 0 ;
  int res1 = 0 ;
  TF_Buffer result;
  VALUE vresult = Qnil;
  
  if ((argc < 1) || (argc > 1)) {
    rb_raise(rb_eArgError, "wrong # of arguments(%d for 1)",argc); SWIG_fail;
  }
  res1 = SWIG_ConvertPtr(argv[0], &argp1,SWIGTYPE_p_TF_Buffer, 0 |  0 );
  if (!SWIG_IsOK(res1)) {
    SWIG_exception_fail(SWIG_ArgError(res1), Ruby_Format_TypeError( "", "TF_Buffer *","TF_GetBuffer", 1, argv[0] )); 
  }
  arg1 = reinterpret_cast< TF_Buffer * >(argp1);
  result = TF_GetBuffer(arg1);
  vresult = SWIG_NewPointerObj((new TF_Buffer(static_cast< const TF_Buffer& >(result))), SWIGTYPE_p_TF_Buffer, SWIG_POINTER_OWN |  0 );
  return vresult;
fail:
  return Qnil;
}

.TF_GetCode(*args) ⇒ Object


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# File 'ext/Tensorflow_wrap.cxx', line 13814

SWIGINTERN VALUE
_wrap_TF_GetCode(int argc, VALUE *argv, VALUE self) {
  TF_Status *arg1 = (TF_Status *) 0 ;
  void *argp1 = 0 ;
  int res1 = 0 ;
  TF_Code result;
  VALUE vresult = Qnil;
  
  if ((argc < 1) || (argc > 1)) {
    rb_raise(rb_eArgError, "wrong # of arguments(%d for 1)",argc); SWIG_fail;
  }
  res1 = SWIG_ConvertPtr(argv[0], &argp1,SWIGTYPE_p_TF_Status, 0 |  0 );
  if (!SWIG_IsOK(res1)) {
    SWIG_exception_fail(SWIG_ArgError(res1), Ruby_Format_TypeError( "", "TF_Status const *","TF_GetCode", 1, argv[0] )); 
  }
  arg1 = reinterpret_cast< TF_Status * >(argp1);
  result = (TF_Code)TF_GetCode((TF_Status const *)arg1);
  vresult = SWIG_From_int(static_cast< int >(result));
  return vresult;
fail:
  return Qnil;
}

.TF_GetOpList(*args) ⇒ Object


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# File 'ext/Tensorflow_wrap.cxx', line 14656

SWIGINTERN VALUE
_wrap_TF_GetOpList(int argc, VALUE *argv, VALUE self) {
  TF_Library *arg1 = (TF_Library *) 0 ;
  void *argp1 = 0 ;
  int res1 = 0 ;
  TF_Buffer result;
  VALUE vresult = Qnil;
  
  if ((argc < 1) || (argc > 1)) {
    rb_raise(rb_eArgError, "wrong # of arguments(%d for 1)",argc); SWIG_fail;
  }
  res1 = SWIG_ConvertPtr(argv[0], &argp1,SWIGTYPE_p_TF_Library, 0 |  0 );
  if (!SWIG_IsOK(res1)) {
    SWIG_exception_fail(SWIG_ArgError(res1), Ruby_Format_TypeError( "", "TF_Library *","TF_GetOpList", 1, argv[0] )); 
  }
  arg1 = reinterpret_cast< TF_Library * >(argp1);
  result = TF_GetOpList(arg1);
  vresult = SWIG_NewPointerObj((new TF_Buffer(static_cast< const TF_Buffer& >(result))), SWIGTYPE_p_TF_Buffer, SWIG_POINTER_OWN |  0 );
  return vresult;
fail:
  return Qnil;
}

.TF_LoadLibrary(*args) ⇒ Object


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# File 'ext/Tensorflow_wrap.cxx', line 14621

SWIGINTERN VALUE
_wrap_TF_LoadLibrary(int argc, VALUE *argv, VALUE self) {
  char *arg1 = (char *) 0 ;
  TF_Status *arg2 = (TF_Status *) 0 ;
  int res1 ;
  char *buf1 = 0 ;
  int alloc1 = 0 ;
  void *argp2 = 0 ;
  int res2 = 0 ;
  TF_Library *result = 0 ;
  VALUE vresult = Qnil;
  
  if ((argc < 2) || (argc > 2)) {
    rb_raise(rb_eArgError, "wrong # of arguments(%d for 2)",argc); SWIG_fail;
  }
  res1 = SWIG_AsCharPtrAndSize(argv[0], &buf1, NULL, &alloc1);
  if (!SWIG_IsOK(res1)) {
    SWIG_exception_fail(SWIG_ArgError(res1), Ruby_Format_TypeError( "", "char const *","TF_LoadLibrary", 1, argv[0] ));
  }
  arg1 = reinterpret_cast< char * >(buf1);
  res2 = SWIG_ConvertPtr(argv[1], &argp2,SWIGTYPE_p_TF_Status, 0 |  0 );
  if (!SWIG_IsOK(res2)) {
    SWIG_exception_fail(SWIG_ArgError(res2), Ruby_Format_TypeError( "", "TF_Status *","TF_LoadLibrary", 2, argv[1] )); 
  }
  arg2 = reinterpret_cast< TF_Status * >(argp2);
  result = (TF_Library *)TF_LoadLibrary((char const *)arg1,arg2);
  vresult = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_TF_Library, 0 |  0 );
  if (alloc1 == SWIG_NEWOBJ) delete[] buf1;
  return vresult;
fail:
  if (alloc1 == SWIG_NEWOBJ) delete[] buf1;
  return Qnil;
}

.TF_Message(*args) ⇒ Object


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# File 'ext/Tensorflow_wrap.cxx', line 13838

SWIGINTERN VALUE
_wrap_TF_Message(int argc, VALUE *argv, VALUE self) {
  TF_Status *arg1 = (TF_Status *) 0 ;
  void *argp1 = 0 ;
  int res1 = 0 ;
  char *result = 0 ;
  VALUE vresult = Qnil;
  
  if ((argc < 1) || (argc > 1)) {
    rb_raise(rb_eArgError, "wrong # of arguments(%d for 1)",argc); SWIG_fail;
  }
  res1 = SWIG_ConvertPtr(argv[0], &argp1,SWIGTYPE_p_TF_Status, 0 |  0 );
  if (!SWIG_IsOK(res1)) {
    SWIG_exception_fail(SWIG_ArgError(res1), Ruby_Format_TypeError( "", "TF_Status const *","TF_Message", 1, argv[0] )); 
  }
  arg1 = reinterpret_cast< TF_Status * >(argp1);
  result = (char *)TF_Message((TF_Status const *)arg1);
  vresult = SWIG_FromCharPtr((const char *)result);
  return vresult;
fail:
  return Qnil;
}

.TF_NewBuffer(*args) ⇒ Object


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# File 'ext/Tensorflow_wrap.cxx', line 13676

SWIGINTERN VALUE
_wrap_TF_NewBuffer(int argc, VALUE *argv, VALUE self) {
  TF_Buffer *result = 0 ;
  VALUE vresult = Qnil;
  
  if ((argc < 0) || (argc > 0)) {
    rb_raise(rb_eArgError, "wrong # of arguments(%d for 0)",argc); SWIG_fail;
  }
  result = (TF_Buffer *)TF_NewBuffer();
  vresult = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_TF_Buffer, 0 |  0 );
  return vresult;
fail:
  return Qnil;
}

.TF_NewBufferFromString(*args) ⇒ Object


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# File 'ext/Tensorflow_wrap.cxx', line 13646

SWIGINTERN VALUE
_wrap_TF_NewBufferFromString(int argc, VALUE *argv, VALUE self) {
  void *arg1 = (void *) 0 ;
  size_t arg2 ;
  int res1 ;
  size_t val2 ;
  int ecode2 = 0 ;
  TF_Buffer *result = 0 ;
  VALUE vresult = Qnil;
  
  if ((argc < 2) || (argc > 2)) {
    rb_raise(rb_eArgError, "wrong # of arguments(%d for 2)",argc); SWIG_fail;
  }
  res1 = SWIG_ConvertPtr(argv[0],SWIG_as_voidptrptr(&arg1), 0, 0);
  if (!SWIG_IsOK(res1)) {
    SWIG_exception_fail(SWIG_ArgError(res1), Ruby_Format_TypeError( "", "void const *","TF_NewBufferFromString", 1, argv[0] )); 
  }
  ecode2 = SWIG_AsVal_size_t(argv[1], &val2);
  if (!SWIG_IsOK(ecode2)) {
    SWIG_exception_fail(SWIG_ArgError(ecode2), Ruby_Format_TypeError( "", "size_t","TF_NewBufferFromString", 2, argv[1] ));
  } 
  arg2 = static_cast< size_t >(val2);
  result = (TF_Buffer *)TF_NewBufferFromString((void const *)arg1,arg2);
  vresult = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_TF_Buffer, 0 |  0 );
  return vresult;
fail:
  return Qnil;
}

.TF_NewSession(*args) ⇒ Object


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# File 'ext/Tensorflow_wrap.cxx', line 14190

SWIGINTERN VALUE
_wrap_TF_NewSession(int argc, VALUE *argv, VALUE self) {
  TF_SessionOptions *arg1 = (TF_SessionOptions *) 0 ;
  TF_Status *arg2 = (TF_Status *) 0 ;
  void *argp1 = 0 ;
  int res1 = 0 ;
  void *argp2 = 0 ;
  int res2 = 0 ;
  TF_Session *result = 0 ;
  VALUE vresult = Qnil;
  
  if ((argc < 2) || (argc > 2)) {
    rb_raise(rb_eArgError, "wrong # of arguments(%d for 2)",argc); SWIG_fail;
  }
  res1 = SWIG_ConvertPtr(argv[0], &argp1,SWIGTYPE_p_TF_SessionOptions, 0 |  0 );
  if (!SWIG_IsOK(res1)) {
    SWIG_exception_fail(SWIG_ArgError(res1), Ruby_Format_TypeError( "", "TF_SessionOptions const *","TF_NewSession", 1, argv[0] )); 
  }
  arg1 = reinterpret_cast< TF_SessionOptions * >(argp1);
  res2 = SWIG_ConvertPtr(argv[1], &argp2,SWIGTYPE_p_TF_Status, 0 |  0 );
  if (!SWIG_IsOK(res2)) {
    SWIG_exception_fail(SWIG_ArgError(res2), Ruby_Format_TypeError( "", "TF_Status *","TF_NewSession", 2, argv[1] )); 
  }
  arg2 = reinterpret_cast< TF_Status * >(argp2);
  result = (TF_Session *)TF_NewSession((TF_SessionOptions const *)arg1,arg2);
  vresult = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_TF_Session, 0 |  0 );
  return vresult;
fail:
  return Qnil;
}

.TF_NewSessionOptions(*args) ⇒ Object


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# File 'ext/Tensorflow_wrap.cxx', line 14078

SWIGINTERN VALUE
_wrap_TF_NewSessionOptions(int argc, VALUE *argv, VALUE self) {
  TF_SessionOptions *result = 0 ;
  VALUE vresult = Qnil;
  
  if ((argc < 0) || (argc > 0)) {
    rb_raise(rb_eArgError, "wrong # of arguments(%d for 0)",argc); SWIG_fail;
  }
  result = (TF_SessionOptions *)TF_NewSessionOptions();
  vresult = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_TF_SessionOptions, 0 |  0 );
  return vresult;
fail:
  return Qnil;
}

.TF_NewStatus(*args) ⇒ Object


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# File 'ext/Tensorflow_wrap.cxx', line 13737

SWIGINTERN VALUE
_wrap_TF_NewStatus(int argc, VALUE *argv, VALUE self) {
  TF_Status *result = 0 ;
  VALUE vresult = Qnil;
  
  if ((argc < 0) || (argc > 0)) {
    rb_raise(rb_eArgError, "wrong # of arguments(%d for 0)",argc); SWIG_fail;
  }
  result = (TF_Status *)TF_NewStatus();
  vresult = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_TF_Status, 0 |  0 );
  return vresult;
fail:
  return Qnil;
}

.TF_NewTensor(*args) ⇒ Object


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# File 'ext/Tensorflow_wrap.cxx', line 13862

SWIGINTERN VALUE
_wrap_TF_NewTensor(int argc, VALUE *argv, VALUE self) {
  TF_DataType arg1 ;
  long long *arg2 = (long long *) 0 ;
  int arg3 ;
  void *arg4 = (void *) 0 ;
  size_t arg5 ;
  void (*arg6)(void *,size_t,void *) = (void (*)(void *,size_t,void *)) 0 ;
  void *arg7 = (void *) 0 ;
  int val1 ;
  int ecode1 = 0 ;
  void *argp2 = 0 ;
  int res2 = 0 ;
  int val3 ;
  int ecode3 = 0 ;
  int res4 ;
  size_t val5 ;
  int ecode5 = 0 ;
  int res7 ;
  TF_Tensor *result = 0 ;
  VALUE vresult = Qnil;
  
  if ((argc < 7) || (argc > 7)) {
    rb_raise(rb_eArgError, "wrong # of arguments(%d for 7)",argc); SWIG_fail;
  }
  ecode1 = SWIG_AsVal_int(argv[0], &val1);
  if (!SWIG_IsOK(ecode1)) {
    SWIG_exception_fail(SWIG_ArgError(ecode1), Ruby_Format_TypeError( "", "TF_DataType","TF_NewTensor", 1, argv[0] ));
  } 
  arg1 = static_cast< TF_DataType >(val1);
  res2 = SWIG_ConvertPtr(argv[1], &argp2,SWIGTYPE_p_long_long, 0 |  0 );
  if (!SWIG_IsOK(res2)) {
    SWIG_exception_fail(SWIG_ArgError(res2), Ruby_Format_TypeError( "", "long long *","TF_NewTensor", 2, argv[1] )); 
  }
  arg2 = reinterpret_cast< long long * >(argp2);
  ecode3 = SWIG_AsVal_int(argv[2], &val3);
  if (!SWIG_IsOK(ecode3)) {
    SWIG_exception_fail(SWIG_ArgError(ecode3), Ruby_Format_TypeError( "", "int","TF_NewTensor", 3, argv[2] ));
  } 
  arg3 = static_cast< int >(val3);
  res4 = SWIG_ConvertPtr(argv[3],SWIG_as_voidptrptr(&arg4), 0, 0);
  if (!SWIG_IsOK(res4)) {
    SWIG_exception_fail(SWIG_ArgError(res4), Ruby_Format_TypeError( "", "void *","TF_NewTensor", 4, argv[3] )); 
  }
  ecode5 = SWIG_AsVal_size_t(argv[4], &val5);
  if (!SWIG_IsOK(ecode5)) {
    SWIG_exception_fail(SWIG_ArgError(ecode5), Ruby_Format_TypeError( "", "size_t","TF_NewTensor", 5, argv[4] ));
  } 
  arg5 = static_cast< size_t >(val5);
  {
    int res = SWIG_ConvertFunctionPtr(argv[5], (void**)(&arg6), SWIGTYPE_p_f_p_void_size_t_p_void__void);
    if (!SWIG_IsOK(res)) {
      SWIG_exception_fail(SWIG_ArgError(res), Ruby_Format_TypeError( "", "void (*)(void *,size_t,void *)","TF_NewTensor", 6, argv[5] )); 
    }
  }
  res7 = SWIG_ConvertPtr(argv[6],SWIG_as_voidptrptr(&arg7), 0, 0);
  if (!SWIG_IsOK(res7)) {
    SWIG_exception_fail(SWIG_ArgError(res7), Ruby_Format_TypeError( "", "void *","TF_NewTensor", 7, argv[6] )); 
  }
  result = (TF_Tensor *)TF_NewTensor(arg1,arg2,arg3,arg4,arg5,arg6,arg7);
  vresult = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_TF_Tensor, 0 |  0 );
  return vresult;
fail:
  return Qnil;
}

.TF_NewTensor_wrapper(*args) ⇒ Object


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# File 'ext/Tensorflow_wrap.cxx', line 14680

SWIGINTERN VALUE
_wrap_TF_NewTensor_wrapper(int argc, VALUE *argv, VALUE self) {
  TF_DataType arg1 ;
  long long *arg2 = (long long *) 0 ;
  int arg3 ;
  void *arg4 = (void *) 0 ;
  size_t arg5 ;
  int val1 ;
  int ecode1 = 0 ;
  void *argp2 = 0 ;
  int res2 = 0 ;
  int val3 ;
  int ecode3 = 0 ;
  int res4 ;
  size_t val5 ;
  int ecode5 = 0 ;
  TF_Tensor *result = 0 ;
  VALUE vresult = Qnil;
  
  if ((argc < 5) || (argc > 5)) {
    rb_raise(rb_eArgError, "wrong # of arguments(%d for 5)",argc); SWIG_fail;
  }
  ecode1 = SWIG_AsVal_int(argv[0], &val1);
  if (!SWIG_IsOK(ecode1)) {
    SWIG_exception_fail(SWIG_ArgError(ecode1), Ruby_Format_TypeError( "", "TF_DataType","tensorflow::TF_NewTensor_wrapper", 1, argv[0] ));
  } 
  arg1 = static_cast< TF_DataType >(val1);
  res2 = SWIG_ConvertPtr(argv[1], &argp2,SWIGTYPE_p_long_long, 0 |  0 );
  if (!SWIG_IsOK(res2)) {
    SWIG_exception_fail(SWIG_ArgError(res2), Ruby_Format_TypeError( "", "long long *","tensorflow::TF_NewTensor_wrapper", 2, argv[1] )); 
  }
  arg2 = reinterpret_cast< long long * >(argp2);
  ecode3 = SWIG_AsVal_int(argv[2], &val3);
  if (!SWIG_IsOK(ecode3)) {
    SWIG_exception_fail(SWIG_ArgError(ecode3), Ruby_Format_TypeError( "", "int","tensorflow::TF_NewTensor_wrapper", 3, argv[2] ));
  } 
  arg3 = static_cast< int >(val3);
  res4 = SWIG_ConvertPtr(argv[3],SWIG_as_voidptrptr(&arg4), 0, 0);
  if (!SWIG_IsOK(res4)) {
    SWIG_exception_fail(SWIG_ArgError(res4), Ruby_Format_TypeError( "", "void *","tensorflow::TF_NewTensor_wrapper", 4, argv[3] )); 
  }
  ecode5 = SWIG_AsVal_size_t(argv[4], &val5);
  if (!SWIG_IsOK(ecode5)) {
    SWIG_exception_fail(SWIG_ArgError(ecode5), Ruby_Format_TypeError( "", "size_t","tensorflow::TF_NewTensor_wrapper", 5, argv[4] ));
  } 
  arg5 = static_cast< size_t >(val5);
  result = (TF_Tensor *)tensorflow::TF_NewTensor_wrapper(arg1,arg2,arg3,arg4,arg5);
  vresult = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_TF_Tensor, 0 |  0 );
  return vresult;
fail:
  return Qnil;
}

.TF_NumDims(*args) ⇒ Object


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# File 'ext/Tensorflow_wrap.cxx', line 13974

SWIGINTERN VALUE
_wrap_TF_NumDims(int argc, VALUE *argv, VALUE self) {
  TF_Tensor *arg1 = (TF_Tensor *) 0 ;
  void *argp1 = 0 ;
  int res1 = 0 ;
  int result;
  VALUE vresult = Qnil;
  
  if ((argc < 1) || (argc > 1)) {
    rb_raise(rb_eArgError, "wrong # of arguments(%d for 1)",argc); SWIG_fail;
  }
  res1 = SWIG_ConvertPtr(argv[0], &argp1,SWIGTYPE_p_TF_Tensor, 0 |  0 );
  if (!SWIG_IsOK(res1)) {
    SWIG_exception_fail(SWIG_ArgError(res1), Ruby_Format_TypeError( "", "TF_Tensor const *","TF_NumDims", 1, argv[0] )); 
  }
  arg1 = reinterpret_cast< TF_Tensor * >(argp1);
  result = (int)TF_NumDims((TF_Tensor const *)arg1);
  vresult = SWIG_From_int(static_cast< int >(result));
  return vresult;
fail:
  return Qnil;
}

.TF_PRun(*args) ⇒ Object


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# File 'ext/Tensorflow_wrap.cxx', line 14517

SWIGINTERN VALUE
_wrap_TF_PRun(int argc, VALUE *argv, VALUE self) {
  TF_Session *arg1 = (TF_Session *) 0 ;
  char *arg2 = (char *) 0 ;
  char **arg3 = (char **) 0 ;
  TF_Tensor **arg4 = (TF_Tensor **) 0 ;
  int arg5 ;
  char **arg6 = (char **) 0 ;
  TF_Tensor **arg7 = (TF_Tensor **) 0 ;
  int arg8 ;
  char **arg9 = (char **) 0 ;
  int arg10 ;
  TF_Status *arg11 = (TF_Status *) 0 ;
  void *argp1 = 0 ;
  int res1 = 0 ;
  int res2 ;
  char *buf2 = 0 ;
  int alloc2 = 0 ;
  void *argp3 = 0 ;
  int res3 = 0 ;
  void *argp4 = 0 ;
  int res4 = 0 ;
  int val5 ;
  int ecode5 = 0 ;
  void *argp6 = 0 ;
  int res6 = 0 ;
  void *argp7 = 0 ;
  int res7 = 0 ;
  int val8 ;
  int ecode8 = 0 ;
  void *argp9 = 0 ;
  int res9 = 0 ;
  int val10 ;
  int ecode10 = 0 ;
  void *argp11 = 0 ;
  int res11 = 0 ;
  
  if ((argc < 11) || (argc > 11)) {
    rb_raise(rb_eArgError, "wrong # of arguments(%d for 11)",argc); SWIG_fail;
  }
  res1 = SWIG_ConvertPtr(argv[0], &argp1,SWIGTYPE_p_TF_Session, 0 |  0 );
  if (!SWIG_IsOK(res1)) {
    SWIG_exception_fail(SWIG_ArgError(res1), Ruby_Format_TypeError( "", "TF_Session *","TF_PRun", 1, argv[0] )); 
  }
  arg1 = reinterpret_cast< TF_Session * >(argp1);
  res2 = SWIG_AsCharPtrAndSize(argv[1], &buf2, NULL, &alloc2);
  if (!SWIG_IsOK(res2)) {
    SWIG_exception_fail(SWIG_ArgError(res2), Ruby_Format_TypeError( "", "char const *","TF_PRun", 2, argv[1] ));
  }
  arg2 = reinterpret_cast< char * >(buf2);
  res3 = SWIG_ConvertPtr(argv[2], &argp3,SWIGTYPE_p_p_char, 0 |  0 );
  if (!SWIG_IsOK(res3)) {
    SWIG_exception_fail(SWIG_ArgError(res3), Ruby_Format_TypeError( "", "char const **","TF_PRun", 3, argv[2] )); 
  }
  arg3 = reinterpret_cast< char ** >(argp3);
  res4 = SWIG_ConvertPtr(argv[3], &argp4,SWIGTYPE_p_p_TF_Tensor, 0 |  0 );
  if (!SWIG_IsOK(res4)) {
    SWIG_exception_fail(SWIG_ArgError(res4), Ruby_Format_TypeError( "", "TF_Tensor **","TF_PRun", 4, argv[3] )); 
  }
  arg4 = reinterpret_cast< TF_Tensor ** >(argp4);
  ecode5 = SWIG_AsVal_int(argv[4], &val5);
  if (!SWIG_IsOK(ecode5)) {
    SWIG_exception_fail(SWIG_ArgError(ecode5), Ruby_Format_TypeError( "", "int","TF_PRun", 5, argv[4] ));
  } 
  arg5 = static_cast< int >(val5);
  res6 = SWIG_ConvertPtr(argv[5], &argp6,SWIGTYPE_p_p_char, 0 |  0 );
  if (!SWIG_IsOK(res6)) {
    SWIG_exception_fail(SWIG_ArgError(res6), Ruby_Format_TypeError( "", "char const **","TF_PRun", 6, argv[5] )); 
  }
  arg6 = reinterpret_cast< char ** >(argp6);
  res7 = SWIG_ConvertPtr(argv[6], &argp7,SWIGTYPE_p_p_TF_Tensor, 0 |  0 );
  if (!SWIG_IsOK(res7)) {
    SWIG_exception_fail(SWIG_ArgError(res7), Ruby_Format_TypeError( "", "TF_Tensor **","TF_PRun", 7, argv[6] )); 
  }
  arg7 = reinterpret_cast< TF_Tensor ** >(argp7);
  ecode8 = SWIG_AsVal_int(argv[7], &val8);
  if (!SWIG_IsOK(ecode8)) {
    SWIG_exception_fail(SWIG_ArgError(ecode8), Ruby_Format_TypeError( "", "int","TF_PRun", 8, argv[7] ));
  } 
  arg8 = static_cast< int >(val8);
  res9 = SWIG_ConvertPtr(argv[8], &argp9,SWIGTYPE_p_p_char, 0 |  0 );
  if (!SWIG_IsOK(res9)) {
    SWIG_exception_fail(SWIG_ArgError(res9), Ruby_Format_TypeError( "", "char const **","TF_PRun", 9, argv[8] )); 
  }
  arg9 = reinterpret_cast< char ** >(argp9);
  ecode10 = SWIG_AsVal_int(argv[9], &val10);
  if (!SWIG_IsOK(ecode10)) {
    SWIG_exception_fail(SWIG_ArgError(ecode10), Ruby_Format_TypeError( "", "int","TF_PRun", 10, argv[9] ));
  } 
  arg10 = static_cast< int >(val10);
  res11 = SWIG_ConvertPtr(argv[10], &argp11,SWIGTYPE_p_TF_Status, 0 |  0 );
  if (!SWIG_IsOK(res11)) {
    SWIG_exception_fail(SWIG_ArgError(res11), Ruby_Format_TypeError( "", "TF_Status *","TF_PRun", 11, argv[10] )); 
  }
  arg11 = reinterpret_cast< TF_Status * >(argp11);
  TF_PRun(arg1,(char const *)arg2,(char const **)arg3,arg4,arg5,(char const **)arg6,arg7,arg8,(char const **)arg9,arg10,arg11);
  if (alloc2 == SWIG_NEWOBJ) delete[] buf2;
  return Qnil;
fail:
  if (alloc2 == SWIG_NEWOBJ) delete[] buf2;
  return Qnil;
}

.TF_PRunSetup(*args) ⇒ Object


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# File 'ext/Tensorflow_wrap.cxx', line 14432

SWIGINTERN VALUE
_wrap_TF_PRunSetup(int argc, VALUE *argv, VALUE self) {
  TF_Session *arg1 = (TF_Session *) 0 ;
  char **arg2 = (char **) 0 ;
  int arg3 ;
  char **arg4 = (char **) 0 ;
  int arg5 ;
  char **arg6 = (char **) 0 ;
  int arg7 ;
  char **arg8 = (char **) 0 ;
  TF_Status *arg9 = (TF_Status *) 0 ;
  void *argp1 = 0 ;
  int res1 = 0 ;
  void *argp2 = 0 ;
  int res2 = 0 ;
  int val3 ;
  int ecode3 = 0 ;
  void *argp4 = 0 ;
  int res4 = 0 ;
  int val5 ;
  int ecode5 = 0 ;
  void *argp6 = 0 ;
  int res6 = 0 ;
  int val7 ;
  int ecode7 = 0 ;
  void *argp8 = 0 ;
  int res8 = 0 ;
  void *argp9 = 0 ;
  int res9 = 0 ;
  
  if ((argc < 9) || (argc > 9)) {
    rb_raise(rb_eArgError, "wrong # of arguments(%d for 9)",argc); SWIG_fail;
  }
  res1 = SWIG_ConvertPtr(argv[0], &argp1,SWIGTYPE_p_TF_Session, 0 |  0 );
  if (!SWIG_IsOK(res1)) {
    SWIG_exception_fail(SWIG_ArgError(res1), Ruby_Format_TypeError( "", "TF_Session *","TF_PRunSetup", 1, argv[0] )); 
  }
  arg1 = reinterpret_cast< TF_Session * >(argp1);
  res2 = SWIG_ConvertPtr(argv[1], &argp2,SWIGTYPE_p_p_char, 0 |  0 );
  if (!SWIG_IsOK(res2)) {
    SWIG_exception_fail(SWIG_ArgError(res2), Ruby_Format_TypeError( "", "char const **","TF_PRunSetup", 2, argv[1] )); 
  }
  arg2 = reinterpret_cast< char ** >(argp2);
  ecode3 = SWIG_AsVal_int(argv[2], &val3);
  if (!SWIG_IsOK(ecode3)) {
    SWIG_exception_fail(SWIG_ArgError(ecode3), Ruby_Format_TypeError( "", "int","TF_PRunSetup", 3, argv[2] ));
  } 
  arg3 = static_cast< int >(val3);
  res4 = SWIG_ConvertPtr(argv[3], &argp4,SWIGTYPE_p_p_char, 0 |  0 );
  if (!SWIG_IsOK(res4)) {
    SWIG_exception_fail(SWIG_ArgError(res4), Ruby_Format_TypeError( "", "char const **","TF_PRunSetup", 4, argv[3] )); 
  }
  arg4 = reinterpret_cast< char ** >(argp4);
  ecode5 = SWIG_AsVal_int(argv[4], &val5);
  if (!SWIG_IsOK(ecode5)) {
    SWIG_exception_fail(SWIG_ArgError(ecode5), Ruby_Format_TypeError( "", "int","TF_PRunSetup", 5, argv[4] ));
  } 
  arg5 = static_cast< int >(val5);
  res6 = SWIG_ConvertPtr(argv[5], &argp6,SWIGTYPE_p_p_char, 0 |  0 );
  if (!SWIG_IsOK(res6)) {
    SWIG_exception_fail(SWIG_ArgError(res6), Ruby_Format_TypeError( "", "char const **","TF_PRunSetup", 6, argv[5] )); 
  }
  arg6 = reinterpret_cast< char ** >(argp6);
  ecode7 = SWIG_AsVal_int(argv[6], &val7);
  if (!SWIG_IsOK(ecode7)) {
    SWIG_exception_fail(SWIG_ArgError(ecode7), Ruby_Format_TypeError( "", "int","TF_PRunSetup", 7, argv[6] ));
  } 
  arg7 = static_cast< int >(val7);
  res8 = SWIG_ConvertPtr(argv[7], &argp8,SWIGTYPE_p_p_char, 0 |  0 );
  if (!SWIG_IsOK(res8)) {
    SWIG_exception_fail(SWIG_ArgError(res8), Ruby_Format_TypeError( "", "char **","TF_PRunSetup", 8, argv[7] )); 
  }
  arg8 = reinterpret_cast< char ** >(argp8);
  res9 = SWIG_ConvertPtr(argv[8], &argp9,SWIGTYPE_p_TF_Status, 0 |  0 );
  if (!SWIG_IsOK(res9)) {
    SWIG_exception_fail(SWIG_ArgError(res9), Ruby_Format_TypeError( "", "TF_Status *","TF_PRunSetup", 9, argv[8] )); 
  }
  arg9 = reinterpret_cast< TF_Status * >(argp9);
  TF_PRunSetup(arg1,(char const **)arg2,arg3,(char const **)arg4,arg5,(char const **)arg6,arg7,arg8,arg9);
  return Qnil;
fail:
  return Qnil;
}

.TF_Run(*args) ⇒ Object


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# File 'ext/Tensorflow_wrap.cxx', line 14323

SWIGINTERN VALUE
_wrap_TF_Run(int argc, VALUE *argv, VALUE self) {
  TF_Session *arg1 = (TF_Session *) 0 ;
  TF_Buffer *arg2 = (TF_Buffer *) 0 ;
  char **arg3 = (char **) 0 ;
  TF_Tensor **arg4 = (TF_Tensor **) 0 ;
  int arg5 ;
  char **arg6 = (char **) 0 ;
  TF_Tensor **arg7 = (TF_Tensor **) 0 ;
  int arg8 ;
  char **arg9 = (char **) 0 ;
  int arg10 ;
  TF_Buffer *arg11 = (TF_Buffer *) 0 ;
  TF_Status *arg12 = (TF_Status *) 0 ;
  void *argp1 = 0 ;
  int res1 = 0 ;
  void *argp2 = 0 ;
  int res2 = 0 ;
  void *argp3 = 0 ;
  int res3 = 0 ;
  void *argp4 = 0 ;
  int res4 = 0 ;
  int val5 ;
  int ecode5 = 0 ;
  void *argp6 = 0 ;
  int res6 = 0 ;
  void *argp7 = 0 ;
  int res7 = 0 ;
  int val8 ;
  int ecode8 = 0 ;
  void *argp9 = 0 ;
  int res9 = 0 ;
  int val10 ;
  int ecode10 = 0 ;
  void *argp11 = 0 ;
  int res11 = 0 ;
  void *argp12 = 0 ;
  int res12 = 0 ;
  
  if ((argc < 12) || (argc > 12)) {
    rb_raise(rb_eArgError, "wrong # of arguments(%d for 12)",argc); SWIG_fail;
  }
  res1 = SWIG_ConvertPtr(argv[0], &argp1,SWIGTYPE_p_TF_Session, 0 |  0 );
  if (!SWIG_IsOK(res1)) {
    SWIG_exception_fail(SWIG_ArgError(res1), Ruby_Format_TypeError( "", "TF_Session *","TF_Run", 1, argv[0] )); 
  }
  arg1 = reinterpret_cast< TF_Session * >(argp1);
  res2 = SWIG_ConvertPtr(argv[1], &argp2,SWIGTYPE_p_TF_Buffer, 0 |  0 );
  if (!SWIG_IsOK(res2)) {
    SWIG_exception_fail(SWIG_ArgError(res2), Ruby_Format_TypeError( "", "TF_Buffer const *","TF_Run", 2, argv[1] )); 
  }
  arg2 = reinterpret_cast< TF_Buffer * >(argp2);
  res3 = SWIG_ConvertPtr(argv[2], &argp3,SWIGTYPE_p_p_char, 0 |  0 );
  if (!SWIG_IsOK(res3)) {
    SWIG_exception_fail(SWIG_ArgError(res3), Ruby_Format_TypeError( "", "char const **","TF_Run", 3, argv[2] )); 
  }
  arg3 = reinterpret_cast< char ** >(argp3);
  res4 = SWIG_ConvertPtr(argv[3], &argp4,SWIGTYPE_p_p_TF_Tensor, 0 |  0 );
  if (!SWIG_IsOK(res4)) {
    SWIG_exception_fail(SWIG_ArgError(res4), Ruby_Format_TypeError( "", "TF_Tensor **","TF_Run", 4, argv[3] )); 
  }
  arg4 = reinterpret_cast< TF_Tensor ** >(argp4);
  ecode5 = SWIG_AsVal_int(argv[4], &val5);
  if (!SWIG_IsOK(ecode5)) {
    SWIG_exception_fail(SWIG_ArgError(ecode5), Ruby_Format_TypeError( "", "int","TF_Run", 5, argv[4] ));
  } 
  arg5 = static_cast< int >(val5);
  res6 = SWIG_ConvertPtr(argv[5], &argp6,SWIGTYPE_p_p_char, 0 |  0 );
  if (!SWIG_IsOK(res6)) {
    SWIG_exception_fail(SWIG_ArgError(res6), Ruby_Format_TypeError( "", "char const **","TF_Run", 6, argv[5] )); 
  }
  arg6 = reinterpret_cast< char ** >(argp6);
  res7 = SWIG_ConvertPtr(argv[6], &argp7,SWIGTYPE_p_p_TF_Tensor, 0 |  0 );
  if (!SWIG_IsOK(res7)) {
    SWIG_exception_fail(SWIG_ArgError(res7), Ruby_Format_TypeError( "", "TF_Tensor **","TF_Run", 7, argv[6] )); 
  }
  arg7 = reinterpret_cast< TF_Tensor ** >(argp7);
  ecode8 = SWIG_AsVal_int(argv[7], &val8);
  if (!SWIG_IsOK(ecode8)) {
    SWIG_exception_fail(SWIG_ArgError(ecode8), Ruby_Format_TypeError( "", "int","TF_Run", 8, argv[7] ));
  } 
  arg8 = static_cast< int >(val8);
  res9 = SWIG_ConvertPtr(argv[8], &argp9,SWIGTYPE_p_p_char, 0 |  0 );
  if (!SWIG_IsOK(res9)) {
    SWIG_exception_fail(SWIG_ArgError(res9), Ruby_Format_TypeError( "", "char const **","TF_Run", 9, argv[8] )); 
  }
  arg9 = reinterpret_cast< char ** >(argp9);
  ecode10 = SWIG_AsVal_int(argv[9], &val10);
  if (!SWIG_IsOK(ecode10)) {
    SWIG_exception_fail(SWIG_ArgError(ecode10), Ruby_Format_TypeError( "", "int","TF_Run", 10, argv[9] ));
  } 
  arg10 = static_cast< int >(val10);
  res11 = SWIG_ConvertPtr(argv[10], &argp11,SWIGTYPE_p_TF_Buffer, 0 |  0 );
  if (!SWIG_IsOK(res11)) {
    SWIG_exception_fail(SWIG_ArgError(res11), Ruby_Format_TypeError( "", "TF_Buffer *","TF_Run", 11, argv[10] )); 
  }
  arg11 = reinterpret_cast< TF_Buffer * >(argp11);
  res12 = SWIG_ConvertPtr(argv[11], &argp12,SWIGTYPE_p_TF_Status, 0 |  0 );
  if (!SWIG_IsOK(res12)) {
    SWIG_exception_fail(SWIG_ArgError(res12), Ruby_Format_TypeError( "", "TF_Status *","TF_Run", 12, argv[11] )); 
  }
  arg12 = reinterpret_cast< TF_Status * >(argp12);
  TF_Run(arg1,(TF_Buffer const *)arg2,(char const **)arg3,arg4,arg5,(char const **)arg6,arg7,arg8,(char const **)arg9,arg10,arg11,arg12);
  return Qnil;
fail:
  return Qnil;
}

.TF_Run_wrapper(*args) ⇒ Object


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# File 'ext/Tensorflow_wrap.cxx', line 14890

SWIGINTERN VALUE
_wrap_TF_Run_wrapper(int argc, VALUE *argv, VALUE self) {
  TF_Session *arg1 = (TF_Session *) 0 ;
  std::vector< std::string,std::allocator< std::string > > arg2 ;
  std::vector< TF_Tensor *,std::allocator< TF_Tensor * > > arg3 ;
  std::vector< std::string,std::allocator< std::string > > arg4 ;
  std::vector< TF_Tensor *,std::allocator< TF_Tensor * > > *arg5 = 0 ;
  std::vector< std::string,std::allocator< std::string > > arg6 ;
  TF_Status *arg7 = (TF_Status *) 0 ;
  void *argp1 = 0 ;
  int res1 = 0 ;
  void *argp5 = 0 ;
  int res5 = 0 ;
  void *argp7 = 0 ;
  int res7 = 0 ;
  
  if ((argc < 7) || (argc > 7)) {
    rb_raise(rb_eArgError, "wrong # of arguments(%d for 7)",argc); SWIG_fail;
  }
  res1 = SWIG_ConvertPtr(argv[0], &argp1,SWIGTYPE_p_TF_Session, 0 |  0 );
  if (!SWIG_IsOK(res1)) {
    SWIG_exception_fail(SWIG_ArgError(res1), Ruby_Format_TypeError( "", "TF_Session *","tensorflow::TF_Run_wrapper", 1, argv[0] )); 
  }
  arg1 = reinterpret_cast< TF_Session * >(argp1);
  {
    std::vector< std::string,std::allocator< std::string > > *ptr = (std::vector< std::string,std::allocator< std::string > > *)0;
    int res = swig::asptr(argv[1], &ptr);
    if (!SWIG_IsOK(res) || !ptr) {
      SWIG_exception_fail(SWIG_ArgError((ptr ? res : SWIG_TypeError)), Ruby_Format_TypeError( "", "std::vector< std::string,std::allocator< std::string > >","tensorflow::TF_Run_wrapper", 2, argv[1] )); 
    }
    arg2 = *ptr;
    if (SWIG_IsNewObj(res)) delete ptr;
  }
  {
    std::vector< TF_Tensor*,std::allocator< TF_Tensor * > > *ptr = (std::vector< TF_Tensor*,std::allocator< TF_Tensor * > > *)0;
    int res = swig::asptr(argv[2], &ptr);
    if (!SWIG_IsOK(res) || !ptr) {
      SWIG_exception_fail(SWIG_ArgError((ptr ? res : SWIG_TypeError)), Ruby_Format_TypeError( "", "std::vector< TF_Tensor *,std::allocator< TF_Tensor * > >","tensorflow::TF_Run_wrapper", 3, argv[2] )); 
    }
    arg3 = *ptr;
    if (SWIG_IsNewObj(res)) delete ptr;
  }
  {
    std::vector< std::string,std::allocator< std::string > > *ptr = (std::vector< std::string,std::allocator< std::string > > *)0;
    int res = swig::asptr(argv[3], &ptr);
    if (!SWIG_IsOK(res) || !ptr) {
      SWIG_exception_fail(SWIG_ArgError((ptr ? res : SWIG_TypeError)), Ruby_Format_TypeError( "", "std::vector< std::string,std::allocator< std::string > >","tensorflow::TF_Run_wrapper", 4, argv[3] )); 
    }
    arg4 = *ptr;
    if (SWIG_IsNewObj(res)) delete ptr;
  }
  res5 = SWIG_ConvertPtr(argv[4], &argp5, SWIGTYPE_p_std__vectorT_TF_Tensor_p_std__allocatorT_TF_Tensor_p_t_t,  0 );
  if (!SWIG_IsOK(res5)) {
    SWIG_exception_fail(SWIG_ArgError(res5), Ruby_Format_TypeError( "", "std::vector< TF_Tensor *,std::allocator< TF_Tensor * > > &","tensorflow::TF_Run_wrapper", 5, argv[4] )); 
  }
  if (!argp5) {
    SWIG_exception_fail(SWIG_ValueError, Ruby_Format_TypeError("invalid null reference ", "std::vector< TF_Tensor *,std::allocator< TF_Tensor * > > &","tensorflow::TF_Run_wrapper", 5, argv[4])); 
  }
  arg5 = reinterpret_cast< std::vector< TF_Tensor *,std::allocator< TF_Tensor * > > * >(argp5);
  {
    std::vector< std::string,std::allocator< std::string > > *ptr = (std::vector< std::string,std::allocator< std::string > > *)0;
    int res = swig::asptr(argv[5], &ptr);
    if (!SWIG_IsOK(res) || !ptr) {
      SWIG_exception_fail(SWIG_ArgError((ptr ? res : SWIG_TypeError)), Ruby_Format_TypeError( "", "std::vector< std::string,std::allocator< std::string > >","tensorflow::TF_Run_wrapper", 6, argv[5] )); 
    }
    arg6 = *ptr;
    if (SWIG_IsNewObj(res)) delete ptr;
  }
  res7 = SWIG_ConvertPtr(argv[6], &argp7,SWIGTYPE_p_TF_Status, 0 |  0 );
  if (!SWIG_IsOK(res7)) {
    SWIG_exception_fail(SWIG_ArgError(res7), Ruby_Format_TypeError( "", "TF_Status *","tensorflow::TF_Run_wrapper", 7, argv[6] )); 
  }
  arg7 = reinterpret_cast< TF_Status * >(argp7);
  tensorflow::TF_Run_wrapper(arg1,arg2,arg3,arg4,*arg5,arg6,arg7);
  return Qnil;
fail:
  return Qnil;
}

.TF_SetConfig(*args) ⇒ Object


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# File 'ext/Tensorflow_wrap.cxx', line 14126

SWIGINTERN VALUE
_wrap_TF_SetConfig(int argc, VALUE *argv, VALUE self) {
  TF_SessionOptions *arg1 = (TF_SessionOptions *) 0 ;
  void *arg2 = (void *) 0 ;
  size_t arg3 ;
  TF_Status *arg4 = (TF_Status *) 0 ;
  void *argp1 = 0 ;
  int res1 = 0 ;
  int res2 ;
  size_t val3 ;
  int ecode3 = 0 ;
  void *argp4 = 0 ;
  int res4 = 0 ;
  
  if ((argc < 4) || (argc > 4)) {
    rb_raise(rb_eArgError, "wrong # of arguments(%d for 4)",argc); SWIG_fail;
  }
  res1 = SWIG_ConvertPtr(argv[0], &argp1,SWIGTYPE_p_TF_SessionOptions, 0 |  0 );
  if (!SWIG_IsOK(res1)) {
    SWIG_exception_fail(SWIG_ArgError(res1), Ruby_Format_TypeError( "", "TF_SessionOptions *","TF_SetConfig", 1, argv[0] )); 
  }
  arg1 = reinterpret_cast< TF_SessionOptions * >(argp1);
  res2 = SWIG_ConvertPtr(argv[1],SWIG_as_voidptrptr(&arg2), 0, 0);
  if (!SWIG_IsOK(res2)) {
    SWIG_exception_fail(SWIG_ArgError(res2), Ruby_Format_TypeError( "", "void const *","TF_SetConfig", 2, argv[1] )); 
  }
  ecode3 = SWIG_AsVal_size_t(argv[2], &val3);
  if (!SWIG_IsOK(ecode3)) {
    SWIG_exception_fail(SWIG_ArgError(ecode3), Ruby_Format_TypeError( "", "size_t","TF_SetConfig", 3, argv[2] ));
  } 
  arg3 = static_cast< size_t >(val3);
  res4 = SWIG_ConvertPtr(argv[3], &argp4,SWIGTYPE_p_TF_Status, 0 |  0 );
  if (!SWIG_IsOK(res4)) {
    SWIG_exception_fail(SWIG_ArgError(res4), Ruby_Format_TypeError( "", "TF_Status *","TF_SetConfig", 4, argv[3] )); 
  }
  arg4 = reinterpret_cast< TF_Status * >(argp4);
  TF_SetConfig(arg1,(void const *)arg2,arg3,arg4);
  return Qnil;
fail:
  return Qnil;
}

.TF_SetStatus(*args) ⇒ Object


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# File 'ext/Tensorflow_wrap.cxx', line 13774

SWIGINTERN VALUE
_wrap_TF_SetStatus(int argc, VALUE *argv, VALUE self) {
  TF_Status *arg1 = (TF_Status *) 0 ;
  TF_Code arg2 ;
  char *arg3 = (char *) 0 ;
  void *argp1 = 0 ;
  int res1 = 0 ;
  int val2 ;
  int ecode2 = 0 ;
  int res3 ;
  char *buf3 = 0 ;
  int alloc3 = 0 ;
  
  if ((argc < 3) || (argc > 3)) {
    rb_raise(rb_eArgError, "wrong # of arguments(%d for 3)",argc); SWIG_fail;
  }
  res1 = SWIG_ConvertPtr(argv[0], &argp1,SWIGTYPE_p_TF_Status, 0 |  0 );
  if (!SWIG_IsOK(res1)) {
    SWIG_exception_fail(SWIG_ArgError(res1), Ruby_Format_TypeError( "", "TF_Status *","TF_SetStatus", 1, argv[0] )); 
  }
  arg1 = reinterpret_cast< TF_Status * >(argp1);
  ecode2 = SWIG_AsVal_int(argv[1], &val2);
  if (!SWIG_IsOK(ecode2)) {
    SWIG_exception_fail(SWIG_ArgError(ecode2), Ruby_Format_TypeError( "", "TF_Code","TF_SetStatus", 2, argv[1] ));
  } 
  arg2 = static_cast< TF_Code >(val2);
  res3 = SWIG_AsCharPtrAndSize(argv[2], &buf3, NULL, &alloc3);
  if (!SWIG_IsOK(res3)) {
    SWIG_exception_fail(SWIG_ArgError(res3), Ruby_Format_TypeError( "", "char const *","TF_SetStatus", 3, argv[2] ));
  }
  arg3 = reinterpret_cast< char * >(buf3);
  TF_SetStatus(arg1,arg2,(char const *)arg3);
  if (alloc3 == SWIG_NEWOBJ) delete[] buf3;
  return Qnil;
fail:
  if (alloc3 == SWIG_NEWOBJ) delete[] buf3;
  return Qnil;
}

.TF_SetTarget(*args) ⇒ Object


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# File 'ext/Tensorflow_wrap.cxx', line 14094

SWIGINTERN VALUE
_wrap_TF_SetTarget(int argc, VALUE *argv, VALUE self) {
  TF_SessionOptions *arg1 = (TF_SessionOptions *) 0 ;
  char *arg2 = (char *) 0 ;
  void *argp1 = 0 ;
  int res1 = 0 ;
  int res2 ;
  char *buf2 = 0 ;
  int alloc2 = 0 ;
  
  if ((argc < 2) || (argc > 2)) {
    rb_raise(rb_eArgError, "wrong # of arguments(%d for 2)",argc); SWIG_fail;
  }
  res1 = SWIG_ConvertPtr(argv[0], &argp1,SWIGTYPE_p_TF_SessionOptions, 0 |  0 );
  if (!SWIG_IsOK(res1)) {
    SWIG_exception_fail(SWIG_ArgError(res1), Ruby_Format_TypeError( "", "TF_SessionOptions *","TF_SetTarget", 1, argv[0] )); 
  }
  arg1 = reinterpret_cast< TF_SessionOptions * >(argp1);
  res2 = SWIG_AsCharPtrAndSize(argv[1], &buf2, NULL, &alloc2);
  if (!SWIG_IsOK(res2)) {
    SWIG_exception_fail(SWIG_ArgError(res2), Ruby_Format_TypeError( "", "char const *","TF_SetTarget", 2, argv[1] ));
  }
  arg2 = reinterpret_cast< char * >(buf2);
  TF_SetTarget(arg1,(char const *)arg2);
  if (alloc2 == SWIG_NEWOBJ) delete[] buf2;
  return Qnil;
fail:
  if (alloc2 == SWIG_NEWOBJ) delete[] buf2;
  return Qnil;
}

.TF_TensorByteSize(*args) ⇒ Object


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# File 'ext/Tensorflow_wrap.cxx', line 14030

SWIGINTERN VALUE
_wrap_TF_TensorByteSize(int argc, VALUE *argv, VALUE self) {
  TF_Tensor *arg1 = (TF_Tensor *) 0 ;
  void *argp1 = 0 ;
  int res1 = 0 ;
  size_t result;
  VALUE vresult = Qnil;
  
  if ((argc < 1) || (argc > 1)) {
    rb_raise(rb_eArgError, "wrong # of arguments(%d for 1)",argc); SWIG_fail;
  }
  res1 = SWIG_ConvertPtr(argv[0], &argp1,SWIGTYPE_p_TF_Tensor, 0 |  0 );
  if (!SWIG_IsOK(res1)) {
    SWIG_exception_fail(SWIG_ArgError(res1), Ruby_Format_TypeError( "", "TF_Tensor const *","TF_TensorByteSize", 1, argv[0] )); 
  }
  arg1 = reinterpret_cast< TF_Tensor * >(argp1);
  result = TF_TensorByteSize((TF_Tensor const *)arg1);
  vresult = SWIG_From_size_t(static_cast< size_t >(result));
  return vresult;
fail:
  return Qnil;
}

.TF_TensorData(*args) ⇒ Object


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# File 'ext/Tensorflow_wrap.cxx', line 14054

SWIGINTERN VALUE
_wrap_TF_TensorData(int argc, VALUE *argv, VALUE self) {
  TF_Tensor *arg1 = (TF_Tensor *) 0 ;
  void *argp1 = 0 ;
  int res1 = 0 ;
  void *result = 0 ;
  VALUE vresult = Qnil;
  
  if ((argc < 1) || (argc > 1)) {
    rb_raise(rb_eArgError, "wrong # of arguments(%d for 1)",argc); SWIG_fail;
  }
  res1 = SWIG_ConvertPtr(argv[0], &argp1,SWIGTYPE_p_TF_Tensor, 0 |  0 );
  if (!SWIG_IsOK(res1)) {
    SWIG_exception_fail(SWIG_ArgError(res1), Ruby_Format_TypeError( "", "TF_Tensor const *","TF_TensorData", 1, argv[0] )); 
  }
  arg1 = reinterpret_cast< TF_Tensor * >(argp1);
  result = (void *)TF_TensorData((TF_Tensor const *)arg1);
  vresult = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_void, 0 |  0 );
  return vresult;
fail:
  return Qnil;
}

.TF_TensorType(*args) ⇒ Object


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# File 'ext/Tensorflow_wrap.cxx', line 13950

SWIGINTERN VALUE
_wrap_TF_TensorType(int argc, VALUE *argv, VALUE self) {
  TF_Tensor *arg1 = (TF_Tensor *) 0 ;
  void *argp1 = 0 ;
  int res1 = 0 ;
  TF_DataType result;
  VALUE vresult = Qnil;
  
  if ((argc < 1) || (argc > 1)) {
    rb_raise(rb_eArgError, "wrong # of arguments(%d for 1)",argc); SWIG_fail;
  }
  res1 = SWIG_ConvertPtr(argv[0], &argp1,SWIGTYPE_p_TF_Tensor, 0 |  0 );
  if (!SWIG_IsOK(res1)) {
    SWIG_exception_fail(SWIG_ArgError(res1), Ruby_Format_TypeError( "", "TF_Tensor const *","TF_TensorType", 1, argv[0] )); 
  }
  arg1 = reinterpret_cast< TF_Tensor * >(argp1);
  result = (TF_DataType)TF_TensorType((TF_Tensor const *)arg1);
  vresult = SWIG_From_int(static_cast< int >(result));
  return vresult;
fail:
  return Qnil;
}