"$python" -c 'import tensorflow as tf; t1=tf.constant([1,2,3,4]); t2=tf.constant([5,6,7,8]); print(tf.add(t1,t2).shape)'
  "$python" -c 'import sys; import tensorflow as tf; sys.exit(0 if "keras" in tf.keras.__name__ else 1)'
fi
# VERY basic check to ensure the [and-cuda] package variant is installable.
# Checks TFCI_BAZEL_COMMON_ARGS for "gpu" or "cuda", implying that the test is

    UnifiedCAPI, GradientCheckerTest,
    ::testing::Combine(::testing::Values("graphdef"),
                       /*tfrt*/ ::testing::Values(false),
                       /*use_function*/ ::testing::Values(true, false)));
#else
INSTANTIATE_TEST_SUITE_P(
    UnifiedCAPI, GradientCheckerTest,
    ::testing::Combine(::testing::Values("graphdef"),
                       /*tfrt*/ ::testing::Values(false),

  # that they are not substituted for their corresponding commands in shell
  # scripts. By setting "expand_aliases", we enable alias expansion in
  # non-interactive shells as well.
  shopt -s expand_aliases
else
  echo '==TFCI==: Error: Cannot find path to grealpath or gstat'
  echo 'TF CI scripts require GNU core utilties to be installed. Please make'
  echo 'sure they are present on your system and try again.'
  exit 1
fi

  if (op_to_reset) {
    tensorflow::ImmediateExecutionOperation* op =
        tensorflow::unwrap(op_to_reset);
    op->Clear();
    status->status = op->Reset(op_or_function_name, raw_device_name);
  } else {
    TF_SetStatus(status, TF_INVALID_ARGUMENT,
                 "op_to_reset should not be nullptr");
  }
}

void TFE_ContextEnableGraphCollection(TFE_Context* ctx) {

      if (input_shape.has_value()) {
        TF_ShapeAndTypeListSetShape(input_shapes, i, input_shape->data(),
                                    input_shape->size());
      } else {
        TF_ShapeAndTypeListSetUnknownShape(input_shapes, i);
      }
    }
    TF_ShapeAndTypeList* output_shapes;
    TFE_InferShapes(op, input_shapes,
                    input_tensors.empty()

        ASSERT_TRUE(v.second) << "Duplicate edge " << iter->second << " -> "
                              << arg.name() << ". fdef: " << fdef.DebugString();
      } else {
        const auto& v = a_edges.insert({arg.name(), arg.name()});
        ASSERT_TRUE(v.second) << "Duplicate edge " << arg.name() << " -> "
                              << arg.name() << ". fdef: " << fdef.DebugString();

                                  const char* path, TF_TransactionToken** token,
                                  TF_Status* status);

  /// Returns transaction token for `path` if it is part of a transaction else
  /// starts a new transaction and adds `path` to that transaction
  ///
  /// Transaction token is returned in `token`. Ownership of the token is in

    *   Renaming of tensorflow::int64 to int_64_t in numerous places (the former
        is an alias for the latter) which could result in needing to regenerate
        selective op registration headers else execution would fail with
        unregistered kernels error.

*   Modular File System Migration:

    *   Support for S3 and HDFS file systems has been migrated to a modular file

    if user_input == 'y':
      print(yes_reply)
      var = True
    elif user_input == 'n':
      print(no_reply)
      var = False
    elif not user_input:
      if enabled_by_default:
        print(yes_reply)
        var = True
      else:
        print(no_reply)
        var = False
    else:
      print('Invalid selection: {}'.format(user_input_origin))
  return var

-#    define __mempcpy(dest, src, n) __builtin_mempcpy (dest, src, n)
-#   elif __GNUC_PREREQ (3, 0)
-#    define __mempcpy(dest, src, n) \
-  (__extension__ (__builtin_constant_p (src) && __builtin_constant_p (n)      \
-		  && __string2_1bptr_p (src) && n <= 8			      \
-		  ? __builtin_memcpy (dest, src, n) + (n)		      \
-		  : __mempcpy (dest, src, n)))
-#   else
-#    define __mempcpy(dest, src, n) \