PatternRewriter& rewriter, llvm::StringMap<Attribute>& identifier_to_attr) {
  ArrayAttr stride = mlir::dyn_cast<ArrayAttr>(identifier_to_attr["strides"]);
  const int stride_h = mlir::cast<IntegerAttr>(stride[1]).getInt();
  const int stride_w = mlir::cast<IntegerAttr>(stride[2]).getInt();
  return rewriter.getI64ArrayAttr({stride_h, stride_w});
}

Attribute GetLhsDilationValue(PatternRewriter& rewriter,

  %dconv = "tfl.depthwise_conv_2d"(%arg0, %w, %b) {depth_multiplier = 4 : i32, dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "VALID", stride_h = 4 : i32, stride_w = 5 : i32} : (tensor<1x224x224x3xf32>, tensor<64x3x3x3xf32>, tensor<64xf32>)...

   // Unsupported strides
   %3 = "tf.DepthwiseConv2dNative"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", data_format = "NHWC", dilations = [1, 1, 1, 1], padding = "SAME", strides = [2, 1, 1, 1]} : (tensor<256x32x32x3xf32>, tensor<3x3x3x4xf32>) -> tensor<256x30x30x12xf32>

// CHECK:           %[[VAL_5:.*]] = "tfl.average_pool_2d"(%[[VAL_4]]) <{filter_height = 3 : i32, filter_width = 3 : i32, fused_activation_function = "NONE", padding = "VALID", stride_h = 1 : i32, stride_w = 1 : i32}> : (tensor<1x6x6x3xf32>) -> tensor<1x4x4x3xf32>
// CHECK:           %[[VAL_6:.*]] = arith.constant dense<[0, 3, 1, 2]> : tensor<4xi32>

  %dconv = "tfl.depthwise_conv_2d"(%0, %w, %b) {depth_multiplier = 4 : i32, dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "VALID", stride_h = 4 : i32, stride_w = 5 : i32} : (tensor<1x224x224x3xf32>, tensor<64x3x3x3xf32>, tensor<64xf32>)...

      return op.emitOpError() << "requires begin, end and strides to have the "
                                 "same number of elements";
    }
  }

  // If strides are constants, verify that none of the element is zero.
  DenseIntElementsAttr strides;
  if (matchPattern(op.getStrides(), m_Constant(&strides))) {
    if (llvm::is_contained(strides.getValues<APInt>(), 0))

    EAGER {
      @Override
      Striped<Lock> get(int stripes) {
        return Striped.lock(stripes);
      }
    },
    LAZY_SMALL {
      @Override
      Striped<Lock> get(int stripes) {
        return new Striped.SmallLazyStriped<>(stripes, LOCK_SUPPLIER);
      }
    },
    LAZY_LARGE {
      @Override
      Striped<Lock> get(int stripes) {
        return new Striped.LargeLazyStriped<>(stripes, LOCK_SUPPLIER);

  %0 = "tfl.conv_2d"(%arg0, %arg1, %arg2) {dilation_h_factor = 2 : i32, dilation_w_factor = 2 : i32, fused_activation_function = "NONE", padding = "VALID", stride_h = 1 : i32, stride_w = 1 : i32} : (tensor<1x112x80x128xf32>, tensor<128x3x3x128xf32>, tensor<128xf32>) -> t...

        rewriter.create<arith::ConstantOp>(op.getLoc(), slice_size_attr);

    ArrayRef<int64_t> strides = op.getStrides();
    // If stride of every dimension is 1, create tfl.slice and return early.
    // Otherwise, create tfl.strided_slice instead.
    if (llvm::all_of(strides, [](int64_t stride) { return stride == 1; })) {
      rewriter.replaceOpWithNewOp<TFL::SliceOp>(

        $stride_h, $stride_w, ActFnAttr),
    [(HasOneUse $pool_out)]>;
  def FuseActivationFuncWithMaxPool#ActFnOp#ActFnAttr : Pat<
    (ActFnOp (TFL_MaxPool2DOp:$pool_out $input, $padding, $stride_w, $stride_h,
                  $filter_width, $filter_height, TFL_AF_None)),
    (TFL_MaxPool2DOp $input, $padding, $stride_w, $stride_h,
        $filter_width, $filter_height, ActFnAttr),
    [(HasOneUse $pool_out)]>;
}