else:
    print(f'Found {len(result_store_dict)} ResultStore link(s) for '
          f'Bazel invocations.\n'
          f'ResultStore contains individual representations of each target '
          f'that were run/built during the invocation.\n'
          f'These results are generally easier to read than looking through '
          f'the entire build log:\n')
  i = 1

  awk '/Streaming build results to/ {print $NF}' "$TFCI_OUTPUT_DIR/script.log" | uniq
}

# Print out any ResultStore URLs for Bazel invocations' results.
# Each failed target there will have its own representation, making failures
# easier to find and read.
function resultstore_extract {
  local \
    XML_PATH="$TFCI_OUTPUT_DIR/Bazel_Test_and_Build_Results/sponge_log.xml"

  python3 \

// A TF_AbstractOp is the metadata we need to execute an operation. E.g. this
// could contain the op type and other attributes.
typedef struct TF_AbstractOp TF_AbstractOp;

// Stores a function representation that can be used for execution or for
// setting functional attributes of other composite ops e.g. control flow.
typedef struct TF_AbstractFunction TF_AbstractFunction;

    *   `IntegerLookup` & `StringLookup`: build an index of categorical feature
        values
    *   `CategoryEncoding`: turn integer-encoded categories into one-hot,
        multi-hot, or tf-idf encoded representations
    *   `CategoryCrossing`: create new categorical features representing
        co-occurrences of previous categorical feature values
    *   `Hashing`: the hashing trick, for large-vocabulary categorical features

                                                   TF_Status* status);

// TODO(b/166642410): It would be nice, for custom devices and for other users,
// to have a non-string representation of devices (TF_Device) extracted from
// tensors/ops/etc. and usable in APIs like OpSetDevice/ResetOp/etc.

#define TFE_CUSTOM_DEVICE_VERSION 4

    TFE_TensorDebugInfo* debug_info);

// Returns the number of elements in dimension `dim_index`.
// Tensor representation on device can be transposed from its representation
// on host. The data contained in dimension `dim_index` on device
// can correspond to the data contained in another dimension in on-host
// representation. The dimensions are indexed using the standard TensorFlow
// major-to-minor order (slowest varying dimension first),

// Operations creation within a context can only be executed in that context
// (for now at least).
// Implementations of the context may contain some state e.g. an execution
// environment, a traced representation etc.
class AbstractContext {
 protected:
  enum AbstractContextKind { kGraph, kMlir, kEager, kTfrt, kTape, kOpHandler };
  explicit AbstractContext(AbstractContextKind kind) : kind_(kind) {}

//      non-NULL, in which case it must point to a `TF_Buffer` containing the
//      serialized representation of a `RunOptions` protocol buffer.
//    - `run_metadata` may be NULL, in which case it will be ignored; or
//      non-NULL, in which case it must point to an empty, freshly allocated
//      `TF_Buffer` that may be updated to contain the serialized representation
//      of a `RunMetadata` protocol buffer.
//

#include "tensorflow/core/platform/status.h"

namespace tensorflow {

class FunctionRecord;

// A traced function: this hides the complexity of converting the serialized
// representation between various supported formats e.g. FunctionDef and Mlir
// function.
class AbstractFunction : public core::RefCounted {
 protected:
  enum AbstractFunctionKind { kGraph, kMlir };