// 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"}),
		),

      "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"];
}

    ConstBoolAttrTrue, $asymmetric_quantize_inputs),
  [(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 <

    quantization_axis = -1 : i64, quantization_min_val = -128 : i64,
    quantization_max_val = 127 : i64
  } : (
    tensor<3x3x10x20x!tf_type.qint8>, tensor<f32>, tensor<i32>
  ) -> tensor<3x3x10x20xf32>
  %0 = "tf.Conv2D"(%input, %filter_new) {
    Tin = "tfdtype$DT_FLOAT", Tout = "tfdtype$DT_FLOAT",
    attr_map = "", batch_group_count = 1 : i64,
    explicit_padding = [], feature_group_count = 1 : i64, lhs_dilation = [1, 1],

table Conv2DOptions {
  padding:Padding;
  stride_w:int;
  stride_h:int;
  fused_activation_function:ActivationFunctionType;
  dilation_w_factor:int = 1;
  dilation_h_factor:int = 1;
  // Parameters for Conv2D version 8 or above.
  // When set, quantized_bias_type defines the dtype for both bias and accumulator.
  quantized_bias_type: TensorType;
}

// Options for both Conv3D and Conv3DTranspose.
table Conv3DOptions {