builder->Set(*iter, static_cast<tensorflow::DataType>(value));
}

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>(

  virtual AbstractTensorInterface* CreateBoolScalar(bool value) = 0;

  // Tensor creation functions
  virtual AbstractTensorInterface* CreateTensor(
      DataType dtype, absl::Span<const int64_t> dim_sizes) = 0;

  typedef void (*MemoryReleaser)(void* data, size_t len, void* arg);

  // Create a tensor instance from the given data buffer and description.

			parsedField, ok = s.FieldsByName[f.Name]
		}

		if !ok {
			t.Errorf("schema %v failed to look up field with name %v", s, f.Name)
		} else {
			tests.AssertObjEqual(t, parsedField, f, "Name", "DBName", "BindNames", "DataType", "PrimaryKey", "AutoIncrement", "Creatable", "Updatable", "Readable", "HasDefaultValue", "DefaultValue", "NotNull", "Unique", "Comment", "Size", "Precision", "TagSettings")

			if f.DBName != "" {

class TapeTensor {
 public:
  explicit TapeTensor(AbstractTensorHandle* handle);
  TapeTensor(const TapeTensor& other);
  ~TapeTensor();

  int64_t GetID() const;
  tensorflow::DataType GetDType() const;

  AbstractTensorHandle* ZerosLike() const;

  AbstractTensorHandle* GetHandle() const;

 private:
  AbstractTensorHandle* handle_;
};

func (m Migrator) DataTypeOf(field *schema.Field) string {
	fieldValue := reflect.New(field.IndirectFieldType)
	if dataTyper, ok := fieldValue.Interface().(GormDataTypeInterface); ok {
		if dataType := dataTyper.GormDBDataType(m.DB, field); dataType != "" {
			return dataType
		}
	}

	return m.Dialector.DataTypeOf(field)
}

// FullDataTypeOf returns field's db full data type

            this.hideSuggestionBox();
            suggestingSts = false;
            return;
          }

          $.ajax({
            url: settingAjaxInfo.url,
            type: "get",
            dataType: "json",
            cache: false,
            data: {
              q: $textArea.val(),
              field: settingAjaxInfo.fn,
              num: settingAjaxInfo.num * 2,
              lang: settingAjaxInfo.lang

ttr("param-name")||c.attr("name"),i=function(a,b){b(a)};if(!h)throw new Error("Missing input name used for http requests made by server validator");g||(g={}),"string"==typeof g&&(g=a.parseJSON(g)),g[h]=d,a.ajax({url:b,type:"POST",cache:!1,data:g,dataType:"json",error:function(a){return i({valid:!1,message:"Connection failed with status: "+a.statusText},f),!1},success:function(a){i(a,f)}})};a.formUtils.addAsyncValidator({name:"server",validatorFunction:function(a,b,c,d,e,g){var h=c.valAttr("url")...

#include "tensorflow/core/util/debug_data_dumper.h"

using tensorflow::errors::InvalidArgument;

namespace tensorflow {
namespace {

absl::Status ValidateNonRefOutput(const Node* node, int idx) {
  const DataType& dt = node->output_type(idx);
  return IsRefType(dt)
             ? InvalidArgument("Output ", idx, " of node '", node->name(),
                               "' has a reference type ", DataTypeString(dt))

  EXPECT_NE(attr_found, attr_values.cend());
  EXPECT_EQ(attr_found->second.list().type(0), tensorflow::DataType::DT_BOOL);
  EXPECT_EQ(attr_found->second.list().type(1), tensorflow::DataType::DT_FLOAT);
  EXPECT_EQ(attr_found->second.list().type(2), tensorflow::DataType::DT_INT32);

  TFE_TensorHandle* retvals[1] = {nullptr};
  int num_retvals = 1;

    case TF_DataType::TF_COMPLEX128:
      dtype.code = DLDataTypeCode::kDLComplex;
      break;
    default:
      status->status = tensorflow::errors::InvalidArgument(
          DataType_Name(static_cast<DataType>(data_type)),
          " is not supported by dlpack");
      break;
  }
  return dtype;
}

// Gets DLPack's DLDevice from eager tensor handle.