TF_ShapeAndTypeList* TF_NewShapeAndTypeList(int num_items) {
  TF_ShapeAndTypeList* result = new TF_ShapeAndTypeList;
  result->num_items = num_items;
  result->items = (num_items == 0) ? nullptr : new TF_ShapeAndType[num_items]();
  return result;
}

void TF_ShapeAndTypeListSetShape(TF_ShapeAndTypeList* shape_list, int index,
                                 const int64_t* dims, int num_dims) {

                         int num_shapes) {
  std::vector<PartialTensorShape> shapes;
  shapes.reserve(num_shapes);
  for (int i = 0; i < num_shapes; ++i) {
    if (num_dims[i] < 0) {
      shapes.emplace_back();
    } else {
      shapes.emplace_back(ArraySlice<int64_t>(
          reinterpret_cast<const int64_t*>(dims[i]), num_dims[i]));
    }
  }

  const int batch_size = 4;
  const int num_dims = 2;
  int64_t* dims = new int64_t[num_dims];
  int64_t num_elements = 1;
  dims[0] = batch_size;
  dims[1] = 1;
  for (int64_t i = 0; i < num_dims; ++i) {
    num_elements *= dims[i];
  }
  TF_Tensor* t = TF_AllocateTensor(TF_STRING, dims, num_dims,
                                   sizeof(TF_TString) * num_elements);
  delete[] dims;

        // artificial restriction and require that when num_opers=-1, such
        // nodes must have a single output.
        if (node->num_outputs() != 1) {
          return InvalidArgument(
              "When `num_opers` is set to -1, nodes referenced in `inputs` "
              "must have a single output. Node ",
              node->name(), " has ", node->num_outputs(),

			n := 0
			for i := range in {
				if n > payloads {
					panic("too many requests")
				}

				// t.Log("Got request:", *i)
				out <- &testResponse{
					OrgNum:    i.Num + pp.Num,
					OrgString: pp.String + i.String,
					Embedded:  *i,
				}
				n++
			}
			return nil
		}))
	}
	register(local)
	register(remote)

	// local to remote

                    int num = Integer.parseInt(expression.substring(2, expression.length() - 1));
                    filters.add(new HighestVersionFilter(num));
                } else if ("l".equals(expression)) {
                    filters.add(new LowestVersionFilter());
                } else if (expression.startsWith("l(") && expression.endsWith(")")) {

}

TF_Tensor* TFE_AllocateHostTensor(TFE_Context* ctx, TF_DataType dtype,
                                  const int64_t* dims, int num_dims,
                                  TF_Status* status) {
  std::vector<int64_t> dimvec(num_dims);
  for (int i = 0; i < num_dims; ++i) {
    dimvec[i] = static_cast<int64_t>(dims[i]);
  }

  if (ctx == nullptr) {

			}
		}
	}

	if num, ok := field.TagSettings["AUTOINCREMENTINCREMENT"]; ok {
		field.AutoIncrementIncrement, _ = strconv.ParseInt(num, 10, 64)
	}

	if v, ok := field.TagSettings["DEFAULT"]; ok {
		field.HasDefaultValue = true
		field.DefaultValue = v
	}

	if num, ok := field.TagSettings["SIZE"]; ok {
		if field.Size, err = strconv.Atoi(num); err != nil {
			field.Size = -1
		}
	}

    # default is false: such cases aren't reported by default.
    check-type-assertions: false
    # report about assignment of errors to blank identifier: `num, _ := strconv.Atoi(numStr)`;
    # default is false: such cases aren't reported by default.
    check-blank: false
  govet:
    disable:
      # report about shadowed variables
      - shadow
  goimports:

suggest.field.tags=label
suggest.field.roles=role
suggest.field.index.contents=content,title
suggest.update.request.interval=0
suggest.update.doc.per.request=2
suggest.update.contents.limit.num.percentage=50%
suggest.update.contents.limit.num=10000
suggest.update.contents.limit.doc.size=50000
suggest.source.reader.scroll.size=1
suggest.popular.word.cache.size=1000
suggest.popular.word.cache.expire=60