QuantizableResult,
                     TFL_SparseOp] # additional_traits> {
  let summary = opSummary # " operator";

  let description = [{
    Performs convolution operation on inputs.

    Inputs:
      `inputs[0]`: required: the input activation tensor
      `inputs[1]`: required: the filter weight tensor
      `inputs[2]`: optional: the bias tensor
  }];

          tensor_spec.TensorSpec(shape=(1, 3, 4, 3), dtype=dtypes.float32)
      ]
  )
  def conv(self, conv_input: core.Tensor) -> Mapping[str, core.Tensor]:
    """Performs a 2D convolution operation.

    Args:
      conv_input: Input tensor to perform convolution on.

    Returns:
      A map of: output key -> output result.
    """
    out = nn_ops.conv2d(
        conv_input,
        self.conv_filters,

           "callees.";
  let constructor = "TF::CreateResourceDeviceInferencePass()";
  let description = [{
    A pass that propagates device assignment of resources on a module. It
    performs in-function propagation, as well as cross-function propagation from
    callers to callees.

    This pass changes the module by adding "tf.device" attribute to function
    arguments and adding "device" attribute to TF ops.

        RemovalNotification<K, V> notification = RemovalNotification.create(key, value, cause);
        map.removalNotificationQueue.offer(notification);
      }
    }

    /**
     * Performs eviction if the segment is over capacity. Avoids flushing the entire cache if the
     * newest entry exceeds the maximum weight all on its own.
     *
     * @param newest the most recently added entry
     */

        RemovalNotification<K, V> notification = RemovalNotification.create(key, value, cause);
        map.removalNotificationQueue.offer(notification);
      }
    }

    /**
     * Performs eviction if the segment is over capacity. Avoids flushing the entire cache if the
     * newest entry exceeds the maximum weight all on its own.
     *
     * @param newest the most recently added entry
     */

    current_index[i]++;
    if (current_index[i] == result_shape[i]) {
      current_index[i] = 0;
    } else {
      break;
    }
  }
}

/// Performs const folding `calculate` with broadcast behavior on the two
/// attributes `operand1` and `operand2` and returns the result if possible.
/// This function assumes the both operands are verified to have value

#define GEN_PASS_DEF_LEGALIZEHLOTOTFPASS
#include "tensorflow/compiler/mlir/lite/stablehlo/transforms/passes.h.inc"

class LegalizeHloToTf : public impl::LegalizeHloToTfPassBase<LegalizeHloToTf> {
  /// Performs the legalization to the TF dialect.
  void runOnOperation() override;
};

using mhlo::DotDimensionNumbersAttr;

// Replaces `region`'s terminator to TF::Yield.

ignores element order, i.e. [1, 2] == [2, 1]. Concatenation on arrays with x-kubernetes-list-type use the semantics of the list type:\n  - 'set': `X + Y` performs a union where the array positions of all elements in `X` are preserved and\n    non-intersecting elements in `Y` are appended, retaining their partial order.\n  - 'map': `X + Y` performs a merge where the array positions of all keys in `X` are preserved but the values\n    are overwritten by values in `Y` when the key sets of `X` and `Y`...

		podUIDs[pod.UID] = true
	}
	for _, pod := range mirrorPods {
		podUIDs[pod.UID] = true
	}
	kl.statusManager.RemoveOrphanedStatuses(podUIDs)
}

// HandlePodCleanups performs a series of cleanup work, including terminating
// pod workers, killing unwanted pods, and removing orphaned volumes/pod
// directories. No config changes are sent to pod workers while this method

ignores element order, i.e. [1, 2] == [2, 1]. Concatenation on arrays with x-kubernetes-list-type use the semantics of the list type:\n  - 'set': `X + Y` performs a union where the array positions of all elements in `X` are preserved and\n    non-intersecting elements in `Y` are appended, retaining their partial order.\n  - 'map': `X + Y` performs a merge where the array positions of all keys in `X` are preserved but the values\n    are overwritten by values in `Y` when the key sets of `X` and `Y`...