public ExecutionResult waitForFinish() {
            ExecutionResult result = delegate.waitForFinish();
            results.add(result);
            return result;
        }

        @Override
        public ExecutionFailure waitForFailure() {
            ExecutionFailure failure = delegate.waitForFailure();
            results.add(failure);
            return failure;
        }

        @Override

// result, to the index of the same tensorlist in the arguments. For `If` op's
// branch functions, the results and arguments are not usually matched 1-1. This
// will let us konw which tensorlist result maps to which tensorlist in the
// arguments. Once we know this info it will help us decide the types of the
// result tensorlist based on the operand's of the `If` op.

    write->removeAttr(kDeviceAttr);
  }

  // As a part of lifting, we either reuse an existing slot for resource type
  // results or add a new slot. Resource type results should not have any uses
  // to begin with. So we can safely replace each old op result with the
  // corresponding new op result.
  int old_num_results = op_->getNumResults();
  op_->replaceAllUsesWith(new_op->getResults().take_front(old_num_results));
  op_->erase();

	results, err := modload.QueryPackages(ctx, pattern, query, selected, r.checkAllowedOr(query, selected))
	if len(results) > 0 {
		pkgMods = make([]module.Version, 0, len(results))
		for _, qr := range results {
			pkgMods = append(pkgMods, qr.Mod)
		}
	}
	return pkgMods, err
}

	params = p.parseParameters(scope, true)
	results = p.parseResult(scope)

	return
}

func (p *parser) parseFuncType() (*ast.FuncType, *ast.Scope) {
	if p.trace {
		defer un(trace(p, "FuncType"))
	}

	pos := p.expect(token.FUNC)
	scope := ast.NewScope(p.topScope) // function scope
	params, results := p.parseSignature(scope)

	return &ast.FuncType{pos, params, results}, scope
}

		a := SplitN([]byte(tt.s), []byte(tt.sep), tt.n)

		// Appending to the results should not change future results.
		var x []byte
		for _, v := range a {
			x = append(v, 'z')
		}

		result := sliceOfString(a)
		if !eq(result, tt.a) {
			t.Errorf(`Split(%q, %q, %d) = %v; want %v`, tt.s, tt.sep, tt.n, result, tt.a)
			continue
		}
		if tt.n == 0 || len(a) == 0 {
			continue
		}

// `src_names` are owned by and have the lifetime of `results`.
TF_CAPI_EXPORT extern void TF_ImportGraphDefResultsMissingUnusedInputMappings(
    TF_ImportGraphDefResults* results, int* num_missing_unused_input_mappings,
    const char*** src_names, int** src_indexes);

// Deletes a results object returned by TF_GraphImportGraphDefWithResults().

						if osIsNotExist(verr) {
							resp.Results[i] = checkPartVolumeNotFound
						}
						continue
					}
				}
			}
			if osErrToFileErr(err) == errFileNotFound {
				resp.Results[i] = checkPartFileNotFound
			}
			continue
		}
		if st.Mode().IsDir() {
			resp.Results[i] = checkPartFileNotFound
			continue
		}
		// Check if shard is truncated.

	// field results in a configuration error.
	//
	// - If `paramKind` is namespace-scoped, the namespace of the object being
	// evaluated for admission will be used when this field is left unset. Take
	// care that if this is left empty the binding must not match any cluster-scoped
	// resources, which will result in an error.
	//
	// +optional
	Namespace string

### `-tf-remove-unused-while-results`

_Removes unused results from tf.WhileRegion ops_

Removes unused results from `tf.WhileRegion` ops along with the defining
ops in the body, if it is safe to do so.
Currently, the pass detects results with following properties:
- the result is unused outside of the `tf.WhileRegion` op
- the defining op of the result in the body can be safely removed