// Check that newly-imported scalar and feed have control deps (neg3 will
  // inherit them from input)
  TF_Operation* control_inputs[100];
  int num_control_inputs = TF_OperationGetControlInputs(
      scalar3, control_inputs, TF_OperationNumControlInputs(scalar3));
  ASSERT_EQ(2, num_control_inputs);
  EXPECT_EQ(feed, control_inputs[0]);
  EXPECT_EQ(feed2, control_inputs[1]);

  num_control_inputs = TF_OperationGetControlInputs(

        with:
          github-token: ${{secrets.GITHUB_TOKEN}}
          script: |
            const script = require('./.github/workflows/create_issue.js')

# ==============================================================================
# Run any CI script and env configuration to bisect a failing target in some
# build configuration. You must set the following variables to control this
# script:
#
#   TF_BISECT_GOOD: Last known good commit (e.g. commit from the last passing job)
#   TF_BISECT_BAD: First bad commit (e.g. commit from the first failing job)

                                 TF_Operation** control_inputs,
                                 int max_control_inputs) {
  int count = 0;
  for (const auto* edge : oper->node.in_edges()) {
    if (edge->IsControlEdge() && !edge->src()->IsSource()) {
      if (count < max_control_inputs) {
        control_inputs[count] = ToOperation(edge->src());
      }
      ++count;
    }
  }

// 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;

// This allows the client to swap the implementation of the tracing engine.
// Any future call to TF_CreateFunction will use the implementation defined
// here.

        "//tensorflow/cc/saved_model:tag_constants",
        "//tensorflow/compiler/jit",
        "//tensorflow/core:array_ops_op_lib",
        "//tensorflow/core:bitwise_ops_op_lib",
        "//tensorflow/core:control_flow_ops_op_lib",
        "//tensorflow/core:core_cpu_internal",
        "//tensorflow/core:direct_session",
        "//tensorflow/core:framework",
        "//tensorflow/core:framework_internal",

              console.log(await script.filter({github, context, domain}));
              break;
            case "arm.com":
              console.log(await script.filter({github, context, domain}));
              break;
            case "google.com":
              console.log("Googler. No action necessary");
              break;
            default:
              console.log("Skip Trusted Partner Action");

      the copyright owner that is granting the License.

      "Legal Entity" shall mean the union of the acting entity and all
      other entities that control, are controlled by, or are under common
      control with that entity. For the purposes of this definition,
      "control" means (i) the power, direct or indirect, to cause the
      direction or management of such entity, whether by contract or

*   EagerTensor now supports numpy buffer interface for tensors.
*   Add toggles `tf.enable_control_flow_v2()` and `tf.disable_control_flow_v2()`
    for enabling/disabling v2 control flow.
*   Enable v2 control flow as part of `tf.enable_v2_behavior()` and
    `TF2_BEHAVIOR=1`.
*   AutoGraph translates Python control flow into TensorFlow expressions,
    allowing users to write regular Python inside `tf.function`-decorated

are skipped, and any checks implemented inside the shape inference code are not
executed.

The security impact of skipping those checks should be low, since the attack
scenario would require a malicious user to be able to control the model which as
stated above is already equivalent to code execution. In any case, the
recommendation is not to serve models using Eager mode since it also has
performance limitations.

## Multi-Tenant environments