const auto module_op = op->getParentOfType<ModuleOp>();
    const SymbolTable symbol_table(module_op);
    func::FuncOp func = symbol_table.lookup<func::FuncOp>(function_name);

    if (function_name.contains("conv")) {
      return (*(func.getOps<mlir::stablehlo::ConvolutionOp>().begin()))
          .getDimensionNumbers()
          .getKernelOutputFeatureDimension();
    } else if (function_name.contains("dot_general")) {

// or `std::nullopt` if the given op is not per-channel quantizable.
std::optional<int64_t> GetDotGeneralQuantizationDim(
    ::mlir::stablehlo::DotGeneralOp dot_general_op);

// Checks if a `StringRef` contains 'conv' or 'dot_general'.
bool ContainsConvOrDot(StringRef str);

}  // namespace mlir::quant

		ir.ORECOVERFP,
		ir.OSTR2BYTES,
		ir.OSTR2BYTESTMP,
		ir.OSTR2RUNES:

		if isRuneCount(n) {
			// len([]rune(s)) is rewritten to runtime.countrunes(s) later.
			conv := n.(*ir.UnaryExpr).X.(*ir.ConvExpr)
			conv.X = o.expr(conv.X, nil)
		} else {
			o.call(n)
		}

		if lhs == nil || lhs.Op() != ir.ONAME || base.Flag.Cfg.Instrumenting {
			return o.copyExpr(n)
		}
		return n

                    canBeConsumed = false
                }
                conf2 {
                    canBeResolved = false
                    assert canBeConsumed
                }
            }

            dependencies {
                conf1 "org:foo:1.0"
                conf2 "org:bar:1.0"
                implementation "org:foo:1.1"
            }
        """

        when:

    const auto float_graph = model_->subgraphs()->Get(subgraph_idx);
    ASSERT_EQ(quantized_graph->tensors()->size(),
              float_graph->tensors()->size());
    // Make sure the graph only has one Conv operation.
    ASSERT_EQ(quantized_graph->operators()->size(), 1);
    const auto op = quantized_graph->operators()->Get(0);
    const uint32_t op_code_idx = op->opcode_index();

			// 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
				}

	{"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},

    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"];
}

    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 {