# Find all one-step dependencies of those tests which are from //tensorflow
  # (since external deps will come from Python-level pip dependencies),
  # excluding dependencies and files that are known to be unneccessary.
  # This creates a list of targets under //tensorflow that are required for

    for (auto* abstract_output : outputs->outputs) {
      GraphTensor* output = dyn_cast<GraphTensor>(abstract_output);
      if (!output) {
        return errors::Unimplemented(
            "Returning a non-graph tensor from a function has not "
            "been implemented yet.");
      }
      graph_outputs.push_back(output->output_);
    }

    auto s = TF_NewStatus();

  std::function<void(BackwardFunction*)> backward_function_deleter;
};

// Map from tensor_id to internally-defined operation-id of the operation which
// produced this tensor. A value of -1 means that the tensor was directly
// watched and not the result of any operation in the tape.
using TensorTape = std::unordered_map<int64_t, int64_t>;

// Map from operation-id to tape entry.
template <typename BackwardFunction, typename TapeTensor>

}

// Note: this function looks up a thread local policy. So it should be called in
// the appropriate client thread. In particular, in async mode, it may not be
// safe to call this function from the async EagerExecutor threads.
extern TFE_ContextDevicePlacementPolicy TFE_ContextGetDevicePlacementPolicy(
    TFE_Context* ctx) {
  return static_cast<TFE_ContextDevicePlacementPolicy>(

TF_CAPI_EXPORT extern char* TF_FunctionDebugString(TF_Function* func,
                                                   size_t* len);

// On success, dequeues a tensor from a TF-managed FifoQueue given by
// `tensor_id`, associated with `session`. There must be a graph node named
// "fifo_queue_dequeue_<tensor_id>", to be executed by this API call.

  opts->opts.validate_colocation_constraints = enable;
}

// Load a Pluggable Device library.
// On success, returns the handle to library in result and return OK from the
// function. Otherwise return nullptr in result and error Status from the
// function.
//
// If `library_filename` has already been loaded, we return a cached handle.
// Device and Kernels/Ops are registered as globals when a library is loaded

  // its corresponding inputs from the input ParallelTensors. Wraps the
  // resulting per-device and per-output TFE_TensorHandles into one
  // ParallelTensor per output of the original operation.
  //
  // Attributes are forwarded to executed operations unmodified.
  //
  // The returned optional has a value if and only if `status` evaluates to
  // TF_OK. Bad statuses are forwarded from underlying `TFE_Execute` calls, or

    const TFE_OpAttrs* attributes, int expected_max_outputs,
    TF_Status* status) {
  absl::optional<std::vector<MaybeParallelTensorOwned>> result;
  // TODO(allenl): We should remove "TPU" from these op names at the very least,
  // or consider other ways of packing/unpacking parallel tensors.
  if (operation_name == std::string("TPUReplicatedInput")) {

# ==============================================================================

"""Extracts ResultStore links from a log containing Bazel invocations.

The links and the invocations' status can then be printed out, or output in the
form of JUnit-based XML.
"""
import argparse
import datetime
import os
import re
from typing import Dict, Union
import xml.etree.ElementTree as ElemTree

  tensorflow::FunctionDef fdef;
  status->status = tensorflow::GraphToFunctionDef(
      fn_body->graph, fn_name, append_hash_to_fn_name != 0,
      /*set_stateful_from_nodes=*/true,
      /*copy_placeholder_attrs_from_nodes=*/true, body_nodes, input_tensors,
      output_tensors, output_names_vec, control_output_nodes,
      control_output_names_vec, description, &fdef);
  if (TF_GetCode(status) != TF_OK) {