// pushes and pops at the back.
  std::stack<AccumulatorCallState> call_state_;
};

// Template instantiations here

inline bool IsDtypeTrainable(DataType dtype) {
  switch (dtype) {
    case DT_HALF:
    case DT_BFLOAT16:
    case DT_FLOAT:
    case DT_DOUBLE:
    case DT_COMPLEX64:
    case DT_COMPLEX128:
    case DT_RESOURCE:
    case DT_VARIANT:

  }

  tensorflow::AbstractTensorInterface* t =
      tensorflow::unwrap(ctx)->CreateTensor(
          static_cast<tensorflow::DataType>(dtype), dimvec);

  if (t == nullptr) {
    status->status =
        tensorflow::errors::InvalidArgument("Unsupported dtype: ", dtype);
    return nullptr;
  }

  return new TF_Tensor{t};
}

TFE_TensorHandle* TFE_NewTensorHandleFromTensor(TFE_Context* ctx, TF_Tensor* t,

Status DefaultPaddedShapeFn(const Tensor& tensor, xla::Shape* shape) {
  const tensorflow::XlaTensor* xla_tensor =
      tensorflow::XlaTensor::FromTensor(&tensor);
  if (xla_tensor == nullptr) {
    return TensorShapeToXLAShape(tensor.dtype(), tensor.shape(), shape);
  }

  const xla::ShapedBuffer& shaped_buffer = xla_tensor->shaped_buffer();
  *shape = shaped_buffer.on_device_shape();
  return absl::OkStatus();
}

    "'h_scale' type: 'float'} attr : { name: 'max_classes_per_detection' "
    "type: 'int'} attr : { name: 'max_detections' type: 'int'} attr : { "
    "name: 'nms_iou_threshold' type: 'float'} attr : { name: "
    "'nms_score_threshold' type: 'float'} attr : { name: 'num_classes' type: "
    "'int'} attr : { name: 'w_scale' type: 'float'} attr : { name: 'x_scale' "

// predeclared) type. The namedType for a type name is always found via
// reader.lookupType.
type namedType struct {
	doc  string       // doc comment for type
	name string       // type name
	decl *ast.GenDecl // nil if declaration hasn't been seen yet

	isEmbedded bool        // true if this type is embedded
	isStruct   bool        // true if this type is a struct
	embedded   embeddedSet // true if the embedded type is a pointer

	{"io.ReadWriter", "type ReadWriter interface{Reader; Writer}"},
	{"go/ast.Node", "type Node interface{End() go/token.Pos; Pos() go/token.Pos}"},
	{"go/types.Type", "type Type interface{String() string; Underlying() Type}"},
}

func TestImportedTypes(t *testing.T) {
	testenv.MustHaveGoBuild(t)

	// This package only handles gc export data.
	if runtime.Compiler != "gc" {

  CreateContext();

  std::vector<XlaCompiler::Argument> args(2);
  args[0].kind = XlaCompiler::Argument::kParameter;
  args[0].type = DT_INT32;
  args[0].shape = TensorShape({1, 3});
  args[1].kind = XlaCompiler::Argument::kParameter;
  args[1].type = DT_INT32;
  args[1].shape = TensorShape({1, 3});

  const XlaCompiler::CompilationResult* result;
  xla::PjRtLoadedExecutable* executable;

		}
		column, value := nameVal[0], nameVal[1]
		ctype, ok := c.db.columnType(stmt.table, column)
		if !ok {
			stmt.Close()
			return nil, errf("INSERT table %q references non-existent column %q", stmt.table, column)
		}
		stmt.colName = append(stmt.colName, column)

		if !strings.HasPrefix(value, "?") {
			var subsetVal any
			// Convert to driver subset type
			switch ctype {
			case "string":

    TF_Status* status);

// Information about the shape of a Tensor and its type.
struct TF_ShapeAndType {
  // Number of dimensions. -1 indicates unknown rank.
  int num_dims;
  // Array of dimensions. -1 indicates unknown dim.
  int64_t* dims;
  // The data type. May be 0 to denote unknown type.
  TF_DataType dtype;
};

typedef struct TF_ShapeAndType TF_ShapeAndType;

         << compilation_result.xla_output_shape.ToString() << '\n';

  for (const auto& xla_output_description : compilation_result.outputs) {
    output << "// XlaOutputDescription type="
           << DataTypeString(xla_output_description.type) << " shape=("
           << absl::StrJoin(xla_output_description.shape.dim_sizes(), ", ")
           << ')';
    if (xla_output_description.input_index >= 0)