} else {
					missingSetResourceToContainerNames[diff].Insert(container.Name)
				}
			}
		}
	}
}

// podComputeUsageHelper can summarize the pod compute quota usage based on requests and limits
func podComputeUsageHelper(requests corev1.ResourceList, limits corev1.ResourceList) corev1.ResourceList {
	result := corev1.ResourceList{}

      %control_28 = tf_executor.island wraps "tf.Assert"(%outputs_26, %outputs_2, %outputs_4, %outputs_6, %outputs_22) {device = "/device:CPU:0", summarize = 3 : i64} : (tensor<*xi1>, tensor<!tf_type.string>, tensor<!tf_type.string>, tensor<!tf_type.string>, tensor<i32>) -> ()

Since included builds can be located anywhere on disk, their build path is determined by the name of the containing directory.
This can sometimes lead to conflicts.

To summarize, the included builds must fulfill these requirements:

* Each included build must have a unique build path.
* Each included build path must not conflict with any project path of the main build.

                                                      old_node->name(), " is "))
                                .FirstN(1000)
                                .Summarize(-1));
        new_output = print_op;
      }

      if (debugging_opts.check_output_numerics &&
          DataTypeIsFloating(new_output.type())) {
        ops::CheckNumerics check_numerics_op(

// Assert op pattern.
//===----------------------------------------------------------------------===//

// HLO and XLA doesn't support Assertions.
def LowerAssert : Pattern<(TF_AssertOp $condition, $data, $summarize), []>;

//===----------------------------------------------------------------------===//
// Binary op patterns.
//===----------------------------------------------------------------------===//

  // CHECK: [[tensor:%.*]] = tf_mlrt.await
  // CHECK: tf_mlrt.executeop([[tensor]])
  %0 = "tf.StringFormat"(%arg0) {__op_key = 0: i32, device = "/job:localhost/replica:0/task:0/device:CPU:0", placeholder = "{}", strtemplate = "%s", summarize = 3 : i64, template = "Outside compiled {}"} : (tensor<i32>) -> tensor<!tf_type.string>

    // CHECK: "tf.UnsupportedOp"
    // CHECK-SAME: _xla_outside_compilation
    // CHECK: "tf.Identity"
    // CHECK-NOT: _xla_outside_compilation
    %t = arith.constant dense<true> : tensor<i1>
    "tf.Assert"(%t, %arg0) {summarize = 3} : (tensor<i1>, tensor<!tf_type.string>) -> ()
    %1 = "tf.UnsupportedOp"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>
    %2 = "tf.Identity"(%1) : (tensor<i32>) -> tensor<i32>
    tf_device.return %2 : tensor<i32>

 * forms of variance -- that were computed from a single set of zero or more floating-point values.
 *
 * <p>There are two ways to obtain a {@code Stats} instance:
 *
 * <ul>
 *   <li>If all the values you want to summarize are already known, use the appropriate {@code
 *       Stats.of} factory method below. Primitive arrays, iterables and iterators of any kind of
 *       {@code Number}, and primitive varargs are supported.

 * forms of variance -- that were computed from a single set of zero or more floating-point values.
 *
 * <p>There are two ways to obtain a {@code Stats} instance:
 *
 * <ul>
 *   <li>If all the values you want to summarize are already known, use the appropriate {@code
 *       Stats.of} factory method below. Primitive arrays, iterables and iterators of any kind of
 *       {@code Number}, and primitive varargs are supported.

  // device which would be useful for debugging.
  //
  // Optional. If null, defaults to resolving the TFE_TensorHandle into a
  // TF_Tensor and summarizing that.
  TF_Buffer* (*summarize)(void* data, TF_Status* status) = nullptr;
} TFE_CustomDeviceTensorHandle;

// Creates a new TensorHandle from memory residing in a custom device. Takes