// 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;
    }
  }

        "//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");

*   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

          << "Failed to find expected control edge " << e.ToString()
          << " in fdef: " << fdef.DebugString();
    }

    // If caller specified all edges, check that we have seen all
    if (is_exact_edges) {
      ASSERT_EQ(e_edges.size() + c_edges.size(), a_edges.size())
          << "Expected edges: " << ToString(e_edges)
          << " Expected Control edges: " << ToString(c_edges)

  }

  Status Compute(AbstractContext* ctx,
                 absl::Span<AbstractTensorHandle* const> grad_outputs,
                 absl::Span<AbstractTensorHandle*> grad_inputs) override {
    // TODO(vnvo2409): Add control dependency
    /* Given upstream grad U and a Log1p op: Y = log(1 + X), the gradients are:
     *
     *    dX = U / (1 + X)
     *
     */

    AbstractTensorHandle* upstream_grad = grad_outputs[0];