conv.getTypeIDs[n.Go[:len(n.Go)-9]] = true
		}
	}
	for i, n := range names {
		if types[i] == nil {
			continue
		}
		pos := f.NamePos[n]
		f, fok := types[i].(*dwarf.FuncType)
		if n.Kind != "type" && fok {
			n.Kind = "func"
			n.FuncType = conv.FuncType(f, pos)
		} else {
			n.Type = conv.Type(types[i], pos)
			switch n.Kind {
			case "iconst":

			// longer than configured. Retry until
			// Dial takes less than maxElapsed or
			// the test times out.
			for {
				startTime := time.Now()
				conn2, err := Dial(network, addr)
				if err == nil {
					conn2.Close()
					t.Fatal("error expected")
				}
				elapsed := time.Since(startTime)
				if elapsed < maxElapsed {
					break
				}

};

using ConvertConv2DDynamic =
    ConvertConvDynamic<TF::Conv2DOp, /*num_spatial_dims=*/2>;

// Converts the TensorFlow conv op in template to the generic HLO conv op by
// converting TensorFlow op attributes to HLO op attributes.
//
// Sample result for Conv2D:
//
//   %conv = "mhlo.convolution"(%input, %filter) {
//     strides = [1, 2],
//     paddings = [[1, 0], [1, 1]],
//     ...
//   }
//

                maven { url "${mavenRepo.uri}" }
            }
            configurations {
                conf
                conf2
                conf3
                conf4
            }
            dependencies {
                conf 'org:a:1.0', 'org:b:1.0'
                conf2 'org:a:1.0', 'org:c:1.0'
                conf3 'org:b:1.0', 'org:c:1.0'
                conf4 'org:b:1.0', 'org:c:1.0', 'org:d:1.0'

	{"slicerunetostring", funcTag, 48},
	{"stringtoslicebyte", funcTag, 50},
	{"stringtoslicerune", funcTag, 53},
	{"slicecopy", funcTag, 54},
	{"decoderune", funcTag, 55},
	{"countrunes", funcTag, 56},
	{"convT", funcTag, 57},
	{"convTnoptr", funcTag, 57},
	{"convT16", funcTag, 59},
	{"convT32", funcTag, 61},
	{"convT64", funcTag, 62},
	{"convTstring", funcTag, 63},
	{"convTslice", funcTag, 66},
	{"assertE2I", funcTag, 67},

			withMounts(map[string]string{"/home/r2lib1": "/usr/r2lib1"}),
			withEnvs(map[string]string{"r2devices": "dev1 dev2"}),
		),
	)
	testManager.podDevices.insert("pod1", "con2", resourceName1,
		constructDevices([]string{"dev3"}),
		newContainerAllocateResponse(
			withDevices(map[string]string{"/dev/r1dev3": "/dev/r1dev3"}),
			withMounts(map[string]string{"/home/r1lib1": "/usr/r1lib1"}),
		),

// CHECK: %[[PAD:.*]] = "tf.PadV2"({{.*}}, %[[CONST]], %[[CONST_1]])
// CHECK: %[[CONV:.*]] = "tf.XlaConvV2"(%[[PAD]], %[[WEIGHT]]
// CHECK-SAME: (tensor<1x4x5x5x3xi8>, tensor<2x3x3x3x2xi8>, tensor<3xi32>, tensor<3x2xi32>, tensor<3xi32>, tensor<3xi32>, tensor<i32>) -> tensor<1x3x2x3x2xi32>
// CHECK: %[[SUB:.*]] = "tf.Sub"(%[[CONV]], %[[CONST_2]])
}

// -----

module attributes {tf_saved_model.semantics} {

      "BesselI1e",
      "Betainc",
      "BiasAddV1",
      "Bincount",
      "Bucketize",
      "Case",
      "CheckNumerics",
      "Cholesky",
      "ControlTrigger",
      "Conv",
      "Conv2D",
      "Conv2DBackpropFilter",
      "Conv2DBackpropInput",
      "Conv3D",
      "Conv3DBackpropFilterV2",
      "Conv3DBackpropInputV2",
      "Cross",
      "Cumprod",
      "Cumsum",

    Option<"is_signed_", "is-signed", "bool", "false",
               "Is the corresponding integer signed">,
  ];
}

def IdentifyDilatedConvPass : Pass<"tfl-identify-dilated-conv", "mlir::func::FuncOp"> {
  let summary = "Convert dense tensor to sparse format.";
  let constructor = "CreateIdentifyDilatedConvPass()";
  let dependentDialects = ["TFL::TensorFlowLiteDialect"];
}

  // TFR_ConstantTensorOp (
  //   ConstantOp (ConstAttr<F32Attr (in_scale[0] * in_scale[1] /
  //   out_scale))
  // )
  // Currently, all decompositions using this pattern (Conv2D, FC) have the
  // following preconditions:
  // * out_scale: float scalar attribute
  // * in_scale[0] (input scale): float scalar, given by tf.Const -> tfr.cast
  // * in_scale[1] (filter scale): float scalar/vector