[(HasRank<2> $input),
   (AreLastTwoDimsTransposed $perm_value),
   (IsBoolAttrEqual<"false"> $adj_y)]>;

// Replace conv-->transpose-->add with conv-->add-->transpose
// The bias needs only reshape (i.e. ReshapeNCHWBiasToNHWC) and not transpose
// because the bias's shape simply changes from NxCx1x1 to Nx1x1xC.
def ReorderNCHWTransposeAdd : Pat <
  (TFL_AddOp:$add_op

# CHECK:           %[[VAL_7:.*]]:2 = "tfl.split"(%[[VAL_6]], %[[VAL_0]]) <{num_splits = 2 : i32}> : (tensor<i32>, tensor<2x5x3xf32>) -> (tensor<1x5x3xf32>, tensor<1x5x3xf32>)
# CHECK:           %[[VAL_9:.*]] = "tfl.transpose"(%[[VAL_1]], %[[VAL_2]]) : (tensor<3x7xf32>, tensor<2xi32>) -> tensor<7x3xf32>

  y_op = tf.placeholder(dtype=tf.int32)
  less_op = tf.less(x_op, y_op)
  switch_op = control_flow_ops.switch(data, less_op)
  merge_op = control_flow_ops.merge(switch_op)[0]
  result = tf.transpose(merge_op)

  tensor_info_result = tf.compat.v1.saved_model.utils.build_tensor_info(result)

  signature_def = tf.saved_model.signature_def_utils.build_signature_def(
      inputs=None,

//
// Example: compilation using XLA (MHLO) lowering
//
//   %0 = "tf.Transpose"(%input, %perm)
//        : (tensor<?x?xf32>, tensor<2xi32>) -> tensor<?x?xf32>
//
//   XLAs `mhlo.transpose` operation requires permutation to be an attribute
//   (compile time value), so it means that if we want to put `tf.Transpose`
//   into a cluster that will be compiled with XLA, the `%perm` operand must

    "StridedSliceGrad",
    "TensorScatterAdd",
    "TensorScatterMax",
    "TensorScatterMin",
    "TensorScatterSub",
    "TensorStridedSliceUpdate",
    "Tile",
    "TileGrad",
    "Transpose",
    "Unique",
    "UniqueV2",
    "UniqueWithCounts",
    "UniqueWithCountsV2",
    "Unpack",
    "UnravelIndex",
    "Where",
    "ZerosLike",

//
// We only clone small host constants now to avoid increasing memory consumption
// too much.  Moreover, in practice the constants we have to duplicate are
// things like the `perm` input to `Transpose` and the `size` input to `Slice`
// which tend to be small anyway.

class CloneConstantsForBetterClusteringPass : public GraphOptimizationPass {
 public:
  CloneConstantsForBetterClusteringPass() = default;

  // has one result operand.
  return isa<TF::IdentityOp, TF::CastOp, TF::ReshapeOp, TF::XlaShardingOp,
             TF::GatherOp, TF::GatherV2Op, TF::XlaGatherOp, TF::ExpandDimsOp,
             TF::SqueezeOp, TF::TransposeOp>(op);
}

bool IsOpWithQuantizableTrait(Operation* op) {
  // Supported quantizable ops.
  return isa<TF::XlaConvV2Op, TF::XlaDotV2Op, TF::MatMulOp, TF::Conv2DOp,

	"fmt"
	"net/http"

	spdyStream "k8s.io/apimachinery/pkg/util/httpstream/spdy"
	"k8s.io/client-go/rest"
	"k8s.io/client-go/transport/spdy"
)

// roundTripperFor creates a SPDY upgrader that will work over custom transports.
func roundTripperFor(restConfig *rest.Config) (http.RoundTripper, spdy.Upgrader, error) {
	// Get the TLS config.
	tlsConfig, err := rest.TLSConfigFor(restConfig)
	if err != nil {

  } else if (index_vector_dim != indices_type.getRank() - 1) {
    // If index_vector_dim isn't the last dimension in indices then it isn't
    // supported yet.
    // TODO(tberghammer): Transpose indices to support this usecase.
    return rewriter.notifyMatchFailure(
        parent_op,
        "index vector dim isn't the last dimension in start indices");
  }
  return success();
}

// CHECK:           %[[VAL_4:.*]] = "tf.Transpose"(%[[VAL_0]], %[[VAL_3]]) : (tensor<2x3xf32>, tensor<2xi64>) -> tensor<3x2xf32>
// CHECK:           return %[[VAL_4]] : tensor<3x2xf32>
// CHECK:         }
func.func @transpose_2d(%arg0: tensor<2x3xf32>) -> tensor<3x2xf32> {
  %0 = mhlo.constant dense<[1, 0]> : tensor<2xi64>
  %1 = mhlo.constant dense<[1, 0]> : tensor<2xi64>