#include <memory>

#include "tensorflow/c/eager/abstract_function.h"
#include "tensorflow/c/eager/abstract_operation.h"

namespace tensorflow {

// Abstract interface to a context.
//
// This serves as a factory for creating `AbstractOperation`s and for
// registering traced functions.
// Operations creation within a context can only be executed in that context
// (for now at least).

        "Encountered while processing output ", i, " from function '", fn_name,
        "'");
    TF_RETURN_WITH_CONTEXT_IF_ERROR(ValidateNonRefOutput(node, idx),
                                    "Encountered while creating function '",
                                    fn_name, "'");
    output_tensors->emplace_back(node, idx);
  }
  return absl::OkStatus();
}

//              The distinction between an empty array of operations and no
//              array of operations is necessary to distinguish the case of
//              creating a function with no body (e.g. identity or permutation)
//              and the case of creating a function whose body contains all
//              the nodes in the graph (except for the automatic skipping, see
//              below).

    std::vector<TensorHandlePtr> components, absl::Span<const int64_t> shape,
    TF_Status* status) {
  if (components.empty()) {
    TF_SetStatus(status, TF_INTERNAL,
                 "No components are provide for creating a ParallelTensor");
    return nullptr;
  }
  TFE_TensorHandleGetStatus(components[0].get(), status);
  if (!status->status.ok()) {
    return nullptr;
  }

  testsuite = ElemTree.SubElement(testsuites, 'testsuite')
  for url, invocation_results in result_store_dict.items():
    invocation_id = url.rsplit('/')[-1]
    if verbose:
      print(f'Creating testcase for invocation {invocation_id}')
    status = invocation_results['status']
    command = invocation_results.get('command')
    command_type = invocation_results.get('command_type')