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 != "" {

                    return;
                }


                $.ajax({
                    url: settingAjaxInfo.url,
                    type:"get",
                    dataType: "jsonp",
                    cache : false,
                    data:{	query: $textArea.val(),
                        type: "suggest",
                        fn: settingAjaxInfo.fn,

              suggestingSts = false;
              return;
            }

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

      // Return values
      {"y: float"},
      // Attr def
      {},
      // Nodes
      {
          {{"assert"},
           "Assert",
           {"condition", "x"},
           {{"T", std::vector<DataType>{DT_FLOAT}}}},
          {{"y"},
           "CollectiveReduce",
           {"x"},
           {{"T", DT_FLOAT},
            {"group_size", static_cast<int>(devices.size())},
            {"group_key", 0},

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

  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;

    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`

    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.

  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));
}