func.func @testConcatInvalidAxis(%arg0: tensor<1x2xi32>, %arg1: tensor<1x2xi32>) -> tensor<2x2xi32> {
  // expected-error @+1 {{'tfl.concatenation' op concatenation dimension must be in [-rank, rank)}}
  %0 = "tfl.concatenation"(%arg0, %arg1) {axis = 2 : i32, fused_activation_function = "NONE"} : (tensor<1x2xi32>, tensor<1x2xi32>) -> tensor<2x2xi32>
  func.return %0 : tensor<2x2xi32>
}

// -----

// `tfl.concatenation`.

func.func @concatenate_float(%arg0: tensor<3x2xf32>, %arg1: tensor<1x2xf32>) -> tensor<4x2xf32> {
  %0 = "stablehlo.concatenate"(%arg0, %arg1) {dimension = 0 : i64} : (tensor<3x2xf32>, tensor<1x2xf32>) -> tensor<4x2xf32>
  return %0 : tensor<4x2xf32>
}
// CHECK-LABEL: concatenate_float
// CHECK-NOT: tfl.concatenation
// CHECK: stablehlo.concatenate

// -----

    // Use `PackOp` for scalar concatenation because `ConcatV2Op` doesn't
    // support scalar concatenation.
    if (is_scalar) {
      auto pack = CreateTfOp<PackOp>(rewriter, op, result_type, args,
                                     rewriter.getI64IntegerAttr(axis));
      return pack.getResult();
    }

    // New concatenation axis.
    auto axis_type = tensorflow::GetTypeFromTFTensorShape(

//
// Additionally, files containing a certificate and matching private key
// for the server must be provided. If the certificate is signed by a
// certificate authority, the certFile should be the concatenation
// of the server's certificate, any intermediates, and the CA's certificate.
//
// ServeTLS always returns a non-nil error.
func ServeTLS(l net.Listener, handler Handler, certFile, keyFile string) error {

    return success();
  }
};

// Fuses Unpack with proceeding Concatenation to Reshape if output type has
// static shape and activation function is none. For example:
//
//   // %input: tensor<1x3x2xf32>
//   %unpack:3 = "tfl.unpack"(%input) {axis = 1 : i32, num = 3 : i32}
//   %res = "tfl.concatenation"(%unpack#0, %unpack#1, %unpack#2)
//        {axis = -1 : i32, fused_activation_function = "NONE"}
//

  return -1;
}

//===----------------------------------------------------------------------===//
// ConcatenationOp
//===----------------------------------------------------------------------===//
// TODO(ashwinm): Implement shape inference for Concatenation

namespace {

int64_t GetConcatenationOpAxis(ConcatenationOp op) {
  auto output_type = op.getOutput().getType().cast<RankedTensorType>();
  int32_t axis = op.getAxis();

  func.return %1 : tensor<2x3xi32>

// CHECK-LABEL: concat_v2_with_3_tensors
// CHECK: "tfl.concatenation"(%arg0, %arg1, %arg2) <{axis = -1 : i32, fused_activation_function = "NONE"}> : (tensor<2x1xi32>, tensor<2x1xi32>, tensor<2x1xi32>) -> tensor<2x3xi32>
}

      return TF::ArraysAreCastCompatible(l, r);
    }
  }];
}

def TFL_ConcatenationOp : TFL_Op<"concatenation",
  [
    Pure,
    TFL_SameFirstOperandAndFirstResultElementType,
    SameOperandsAndResultsScale,
    QuantizableResult
  ]> {
  let summary = "Concatenation operator";

  let description = [{
    Concatenates tensors along one dimension
  }];

  let arguments = (

};

// Rewrites quantized stablehlo.concatenate to tfl.concatenation.
// TODO: b/322428814 - Add StableHLO quantizer integration tests for ODML.
class RewriteQuantizedConcatenateOp
    : public OpRewritePattern<stablehlo::ConcatenateOp> {
 public:
  using OpRewritePattern<stablehlo::ConcatenateOp>::OpRewritePattern;

  LogicalResult match(stablehlo::ConcatenateOp op) const override {
    return success(IsOpFullyQuantized(op));

</pre>

<h4 id="String_concatenation">String concatenation</h4>

<p>
Strings can be concatenated using the <code>+</code> operator
or the <code>+=</code> assignment operator:
</p>

<pre>
s := "hi" + string(c)
s += " and good bye"
</pre>

<p>
String addition creates a new string by concatenating the operands.
</p>