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>(

						columnType)
				}
			case "name":
				dataType := DB.Dialector.DataTypeOf(stmt.Schema.LookUpField(columnType.Name()))
				if !strings.Contains(strings.ToUpper(dataType), strings.ToUpper(columnType.DatabaseTypeName())) {
					t.Fatalf("column name type should be correct, name: %v, length: %v, expects: %v, column: %#v",
						columnType.Name(), columnType.DatabaseTypeName(), dataType, columnType)
				}

	for _, ref := range relation.References {
		f := joinSchema.LookUpField(ref.ForeignKey.DBName)
		if f == nil {
			return fmt.Errorf("missing field %s for join table", ref.ForeignKey.DBName)
		}

		f.DataType = ref.ForeignKey.DataType
		f.GORMDataType = ref.ForeignKey.GORMDataType
		if f.Size == 0 {
			f.Size = ref.ForeignKey.Size
		}
		ref.ForeignKey = f
	}

			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 != "" {

              suggestingSts = false;
              return;
            }

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

								if field.DBName != "" {
									conds = append(conds, clause.Eq{Column: clause.Column{Table: curTable, Name: field.DBName}, Value: v})
								} else if field.DataType != "" {
									conds = append(conds, clause.Eq{Column: clause.Column{Table: curTable, Name: field.Name}, Value: v})
								}
							}
						}
					}
				case reflect.Slice, reflect.Array:

#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))

  desc->node_builder.Attr(attr_name, static_cast<DataType>(value));
}

void TF_SetAttrTypeList(TF_OperationDescription* desc, const char* attr_name,
                        const TF_DataType* values, int num_values) {
  desc->node_builder.Attr(
      attr_name, ArraySlice<const DataType>(
                     reinterpret_cast<const DataType*>(values), num_values));
}

    TF_CheckpointReader* reader, int index);
// Get the number of variable in the checkpoint
TF_CAPI_EXPORT extern int TF_CheckpointReaderSize(TF_CheckpointReader* reader);
// Get the DataType of a variable
TF_CAPI_EXPORT extern TF_DataType TF_CheckpointReaderGetVariableDataType(
    TF_CheckpointReader* reader, const char* name);
// Read the shape of a variable and write to `dims`

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")...