"BroadcastArgs", "BroadcastGradientArgs", "OneHot", "Concat", "ConcatV2",
      "ConcatOffset", "Const", "MirrorPad", "MirrorPadGrad", "Pack", "Pad",
      "PadV2", "Reverse", "ReverseV2", "ReverseSequence", "Slice", "Split",
      "SplitV", "StridedSlice", "StridedSliceGrad",
      "ResourceStridedSliceAssign", "Tile", "Transpose", "InvertPermutation",
      "Unpack", "DeviceIndex", "TensorStridedSliceUpdate", "XlaConcatND",
      "XlaSplitND",

//===----------------------------------------------------------------------===//
// SplitOp
//===----------------------------------------------------------------------===//

// Verifies the input and split dimension operands for tf.Split/tf.SplitV.
// Writes the split dimension's index (adjusted with input rank) via `dim_index`
// if it's a constant.
template <class Op>
LogicalResult VerifySplitInputAndSplitDim(Op op,

    return success();
  }
};

// Converts the tf.SplitV op into a series of HLO slice ops when the tensor to
// be split has fully static shape and the dimension to split and split sizes
// are constants.
//
// This is similar to the conversion for tf.Split op other than that the size of
// each chunk on the dimension to split is explicitly given as an op operand
// and they are not necessarily the same.
//

#$ Used in:
#$ - site-src/guides/traffic-splitting.md
apiVersion: gateway.networking.k8s.io/v1beta1
kind: HTTPRoute
metadata:
  name: simple-split
spec:
  rules:
  - backendRefs:
    - name: foo-v1
      port: 8080
      weight: 90
    - name: foo-v2
      port: 8080

      Spliterator<? extends T> splitr = stream.spliterator();
      splitrsBuilder.add(splitr);
      characteristics &= splitr.characteristics();
      estimatedSize = LongMath.saturatedAdd(estimatedSize, splitr.estimateSize());
    }
    return StreamSupport.stream(
            CollectSpliterators.flatMap(
                splitrsBuilder.build().spliterator(),
                splitr -> (Spliterator<T>) splitr,

    SameOperandsAndResultsScale]> {
  let summary = "Splits a tensor into `num_split` tensors along one dimension.";

  let description = [{
    Splits the `value` tensor along `split_dim` into a number of sub-tensors
    with same shape as the original one, except for `split_dim`. The grouping
    of the resultant sub-tensors is decided by `size-splits`. Same as tf.SplitV.
  }];

  let arguments = (ins

// RUN: tf-opt -tfl-split-merged-operands %s | FileCheck %s

func.func @testSingleLstm(%arg0: tensor<4x4xf32>, %arg1: tensor<4xf32>, %arg2: tensor<4x4x4xf32>) -> tensor<4x4x4xf32> {
  // CHECK-LABEL: testSingleLstm
  // CHECK-DAG:  %[[CST_0:.*]] = "tfl.pseudo_const"() <{value = dense<0.000000e+00> : tensor<4x4xf32>}> : () -> tensor<4x4xf32>
  // CHECK-DAG:  %[[CST_1:.*]] = "tfl.pseudo_const"() <{value = dense<0.000000e+00> : tensor<4x4xf32>}> : () -> tensor<4x4xf32>

    return strategy.iterator(this, sequence);
  }

  /**
   * Splits {@code sequence} into string components and returns them as an immutable list. If you
   * want an {@link Iterable} which may be lazily evaluated, use {@link #split(CharSequence)}.
   *
   * @param sequence the sequence of characters to split
   * @return an immutable list of the segments split from the parameter
   * @since 15.0
   */

    return strategy.iterator(this, sequence);
  }

  /**
   * Splits {@code sequence} into string components and returns them as an immutable list. If you
   * want an {@link Iterable} which may be lazily evaluated, use {@link #split(CharSequence)}.
   *
   * @param sequence the sequence of characters to split
   * @return an immutable list of the segments split from the parameter
   * @since 15.0
   */

		return "", "", device
	}

	parts := strings.SplitN(device, "=", 2)
	if len(parts) != 2 || parts[0] == "" || parts[1] == "" {
		return "", "", device
	}

	name := parts[1]
	vendor, class := parseQualifier(parts[0])
	if vendor == "" {
		return "", "", device
	}

	return vendor, class, name
}

// parseQualifier splits a device qualifier into vendor and class.