cat <<EOF
IMPORTANT: These tests ran under docker. This script does not clean up the
container for you! You can delete the container with:

$ docker rm -f tf

You can also execute more commands within the container with e.g.:

$ docker exec tf bazel clean
$ docker exec -it tf bash
EOF

Status BackEdgeHelper::Replace() {
  if (graph_ == nullptr) {
    return errors::Internal("BackEdgeHelper Replace called before Remove.");
  }
  if (replaced_) {
    return errors::Internal("BackEdgeHelper Replace called more than once.");
  }
  replaced_ = true;
  for (const BackEdge& be : back_edges_) {
    graph_->AddEdge(be.src, be.src_output, be.dst, be.dst_input);
  }
  return absl::OkStatus();
}

int64_t InferLookupTableFindV2Cost(const CostContext& context,
                                   mlir::TF::LookupTableFindV2Op op) {
  // tf.LookupTableFindV2 ops are usually more costly than tf.AddV2 with the
  // same input size, as it involves more operations like hashing, map lookup,
  // etc.
  constexpr int64_t kLookupTableFindCostScale = 8;
  constexpr int64_t kLookupTableFindStringKeyCostScale = 16;

#include "tensorflow/core/common_runtime/eager/eager_operation.h"
#include "tensorflow/core/common_runtime/function_optimization_registry.h"
#include "tensorflow/core/common_runtime/optimization_registry.h"
#include "tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h"
#include "tensorflow/core/framework/function.h"
#include "tensorflow/core/graph/graph.h"
#include "tensorflow/core/platform/casts.h"

      R"pbdoc(
      Convert a model represented in `input_contents`. `model_flags_proto`
      describes model parameters. `flags_proto` describes conversion
      parameters (see relevant .protos for more information). Returns a string
      representing the contents of the converted model. When extended_return
      flag is set to true returns a dictionary that contains string representation

#include "tensorflow/compiler/tf2xla/xla_compiler.h"
#include "xla/pjrt/pjrt_client.h"
#include "tensorflow/core/framework/device_base.h"
#include "tensorflow/core/framework/function.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/resource_mgr.h"
#include "tensorflow/core/lib/core/refcount.h"
#include "tensorflow/core/platform/status.h"
#include "tsl/platform/errors.h"
#include "tsl/platform/statusor.h"

set -euxo pipefail

function resultstore_extract_fallback {
  # In case the main script fails somehow.
  cat <<EOF
IMPORTANT: For bazel invocations that uploaded to ResultStore (e.g. RBE), you
can view more detailed results that are probably easier to read than this log.
Try the links below:
EOF
  # Find any "Streaming build results to" line, then print the last word in it,
  # and don't print duplicates

1. Creating a PR and observing the presubmit test results
2. Running the CI scripts locally, as explained below
3. **Google employees only**: Google employees can use an internal-only tool
called "MLCI" that makes testing more convenient: it can execute any full CI job
against a pending change. Search for "MLCI" internally to find it.

You may invoke a CI script of your choice by following these instructions:

```bash
cd tensorflow-git-dir

      `signature_keys` contains more than one signature key,
      `representative_datsaet` should be a mapping that maps each signature keys
      to the corresponding representative dataset.
    force_graph_mode_calibration: If set to true, it forces calibration in graph
      model instead of eager mode when the context is in eager mode.

  Raises:
    ValueError iff:

// tensorflow/lite/core/subgraph). That runtime logic will now be a no-op for
// models that were generated with this pass.
//
// For purposes of this pass, an operator is considered to have/depend on side
// effects if
//  - it involves calling a different function
//  - it involves accessing resource variables
//
// Note that these criteria are more restrictive than necessary: