LogicalResult matchAndRewrite(TFL::GatherOp op,
                                PatternRewriter &rewriter) const override {
    auto const_type = mlir::dyn_cast<ShapedType>(op.getOperand(0).getType());
    auto result_type = mlir::dyn_cast<ShapedType>(op.getResult().getType());
    if (!const_type || !const_type.getElementType().isSignlessInteger(4) ||
        !result_type || !result_type.getElementType().isSignlessInteger(8)) {
      return failure();
    }

                  int32_t dim) {
  Type attribute_type = builder.getI64Type();
  return builder.create<TF::StridedSliceOp>(
      loc,
      RankedTensorType::get(
          {}, mlir::cast<ShapedType>(shape_value.getType()).getElementType()),
      /*input=*/shape_value,
      /*begin=*/Create1DConstValue<int32_t>(builder, loc, {dim}),
      /*end=*/Create1DConstValue<int32_t>(builder, loc, {dim + 1}),

                .addModifiers(Modifier.PUBLIC, Modifier.STATIC)
                .addParameter(typeName(callable.getOwner().getType()), SELF_PARAMETER_NAME)
                .addParameters(parameters)
                .addCode(generateMethodBody(implementation, callable, implementationExtra))
                .returns(typeName(callable.getReturnType().getType()))
                .addAnnotations(getAnnotations(implementationExtra))
                .build();

    if (!mlir::cast<ShapedType>(lhs.getType()).hasStaticShape() ||
        !mlir::cast<ShapedType>(rhs.getType()).hasStaticShape() ||
        !mlir::cast<ShapedType>(cond.getType()).hasStaticShape()) {
      return rewriteOpWithDynamicInput(op, rewriter);
    }

    auto lhs_shape = mlir::cast<ShapedType>(lhs.getType()).getShape();
    auto rhs_shape = mlir::cast<ShapedType>(rhs.getType()).getShape();

      DenseIntElementsAttr::get(
          RankedTensorType::get(/*shape=*/{}, builder.getI1Type()), true));

  SmallVector<Value, 4> while_input_values;
  SmallVector<Type, 4> while_input_types;
  while_input_values.push_back(const_true.getResult());
  while_input_types.push_back(const_true.getResult().getType());
  for (int i = 1; i < reduce_dataset.getNumOperands(); ++i) {

    return mlir::cast<TensorType>(input_tensor.getType()).hasStaticShape() &&
           mlir::cast<TensorType>(kernel_tensor.getType()).hasStaticShape() &&
           mlir::cast<TensorType>(window_strides.getType()).hasStaticShape() &&
           mlir::cast<TensorType>(padding.getType()).hasStaticShape() &&
           mlir::cast<TensorType>(lhs_dilation.getType()).hasStaticShape() &&

Value AccumulateBuffers(Value a, Value b, OpBuilder builder, Location loc) {
  if (getElementTypeOrSelf(a.getType()) == builder.getI1Type()) {
    return builder.create<TF::LogicalOrOp>(loc, ArrayRef<Type>{a.getType()},
                                           ArrayRef<Value>{a, b});
  }
  return builder.create<TF::AddV2Op>(loc, ArrayRef<Type>{a.getType()},
                                     ArrayRef<Value>{a, b});
}

namespace {

    ) {
        if (owner.isInterceptSubtypes()) {
            code.beginControlFlow("if ($N.isInstanceOf(owner, $S))", METADATA_FIELD, owner.getType().getInternalName());
        } else {
            code.beginControlFlow("if (owner.equals($S))", owner.getType().getInternalName());
        }
        for (CallInterceptionRequest request : requestsForOwner) {
            CodeBlock.Builder nested = CodeBlock.builder();

    [{
  Type resultType;
  if (x.getType().isa<UnrankedTensorType>() ||
      y.getType().isa<UnrankedTensorType>()) {
    resultType = UnrankedTensorType::get($_builder.getI1Type());
  } else {
    SmallVector<int64_t, 4> resultShape;
    if (!OpTrait::util::getBroadcastedShape(
            x.getType().cast<ShapedType>().getShape(),
            y.getType().cast<ShapedType>().getShape(), resultShape)) {

     * - `Unit` type for statements;
     */
    public fun KtExpression.getKaType(): KaType? =
        withValidityAssertion { analysisSession.expressionTypeProvider.getKtExpressionType(this) }

    public fun KtExpression.getKtType(): KaType? = getKaType()

    /**
     * Returns the return type of the given [KtDeclaration] as [KaType].
     *