//	go doc template.new
//		Show documentation for html/template's New function.
//		(html/template is lexically before text/template)
//	go doc text/template.new # One argument
//		Show documentation for text/template's New function.
//	go doc text/template new # Two arguments
//		Show documentation for text/template's New function.
//
//	At least in the current tree, these invocations all print the

}

ElementsAttr ExpandTo4DForDepthwiseConv(Attribute a) {
  return ExpandTo4DForConvImpl(a, true);
}

TypeAttr RescaleQtype(Type input, Attribute factor) {
  return quant::RescaleQuantizedType(input, factor);
}

// Returns `true` if reducing `axes` in `input` with `keep_dims=true` results in
// the specified `shape` and `false` otherwise.
static bool ShapeMatchesReduceWithKeepAxes(Value input,

      "dev": true
    },
    "node_modules/function-bind": {
      "version": "1.1.1",
      "resolved": "https://registry.npmjs.org/function-bind/-/function-bind-1.1.1.tgz",
      "integrity": "sha512-yIovAzMX49sF8Yl58fSCWJ5svSLuaibPxXQJFLmBObTuCr0Mf1KiPopGM9NiFjiYBCbfaa2Fh6breQ6ANVTI0A==",
      "dev": true
    },
    "node_modules/function.prototype.name": {
      "version": "1.1.5",

		return true // eliminates special-casing 2.
	} else if (x & 1) == 0 {
		return false // eliminates even numbers >2.
	}

	limit := uint64(math.Sqrt(float64(x)))
	for factor := uint64(3); factor <= limit; factor += 2 {
		if (x % factor) == 0 {
			return false
		}
	}
	return true
}

func validateSubset(subset *networking.Subset) error {
	return appendErrors(validateSubsetName(subset.Name),

    // CHECK: return
    // CHECK-SAME: tensor<1x2x3xf32>
    func.return %0 : tensor<*xf32>
  }

  // Verify shape propagation from function arg -> if region body -> if region output -> function return type
  // CHECK-LABEL: shape_from_if_to_region_bodies_to_output
  // CHECK-SAME: -> tensor<1x2x3xf32>

    QuantizableResult,
    PredOpTrait<"input and output must have same element type",
      TFL_TCresVTEtIsSameAsOp<0, 0>>]> {
  let summary = "Hardswish activation function.";
  let description = [{
    Computes hard-swish activation function
      f(x) -> (x * relu6(x+3))/6
    element-wise.
  }];

  let arguments = (ins TFL_TensorOf<[F32, QUI8, QI8]>:$input);

        // expected-remark@above {{Successors: {4}}}
        %0 = "tf.XlaLaunch"(%input) {function = @func, operandSegmentSizes = array<i32: 0, 1, 0>, device = "CPU:0"} : (tensor<f32>) -> tensor<f32>
        // expected-remark@above {{ID: 0}}
        // expected-remark@above {{Successors: {1}}}
        %1 = "tf.XlaLaunch"(%input) {function = @func, operandSegmentSizes = array<i32: 0, 1, 0>, device = "CPU:0"} : (tensor<f32>) -> tensor<f32>

func.func @call_func(%arg0: tensor<i32>) -> tensor<i32> {
  func.return %arg0 : tensor<i32>
}

// -----

// Tests that undefined call function produces error.
func.func @test_undefined_function() -> () {
  // expected-error @below {{'f' attribute refers to an undefined function: undefined_func}}
  "tf.LegacyCall"() {f = @undefined_func} : () -> ()
  func.return
}

// -----

    override val function: KaCallableSymbol,
    override val containingClass: KaClassLikeSymbol,
    firDiagnostic: KtPsiDiagnostic,
    token: KaLifetimeToken,
) : KaAbstractFirDiagnostic<KtFunction>(firDiagnostic, token), KaFirDiagnostic.AbstractFunctionInNonAbstractClass

internal class AbstractFunctionWithBodyImpl(
    override val function: KaCallableSymbol,
    firDiagnostic: KtPsiDiagnostic,

    if (called_computations.size() != 1) {
      return op.emitOpError()
             << "ApproxTopK takes exactly 1 called_computation.";
    }
    mlir::func::FuncOp callee = module_op_->lookupSymbol<mlir::func::FuncOp>(
        mlir::cast<FlatSymbolRefAttr>(op.getCalledComputations()[0]));
    mlir::FunctionType callee_type = callee.getFunctionType();
    SmallVector<Type, 4> expected_callee_input_types;