%0 = "tfl.dequantize"(%arg0) : (tensor<1x6x6x16x!quant.uniform<u8:f32, 7.812500e-03:128>>) -> tensor<1x6x6x16xf32>
  %1 = "tfl.average_pool_2d"(%0) {
      name = "avgpool", filter_height = 3 : i32, filter_width = 6 : i32, fused_activation_function = "NONE", padding = "VALID", stride_h = 3 : i32, stride_w = 1 : i32
    } : (tensor<1x6x6x16xf32>) -> tensor<1x1x1x16xf32>
  func.return %1 : tensor<1x1x1x16xf32>

  /// format to TFLite DepthwiseConv2D op filter data format and return Value
  /// for the converted filter.  TensorFlow filter data format is
  /// [filter_height, filter_width, in_channels, channel_multiplier] and TFLite
  /// filter data format is [1, filter_height, filter_width, out_channels].
  /// Requires that filter is verified by the match method that it is a 4-D
  /// RankedTensorType.

^bb0(%arg0: tensor<256x32x32x3xf32>):
  // CHECK: "tfl.max_pool_2d"(%arg0) <{filter_height = 1 : i32, filter_width = 1 : i32, fused_activation_function = "RELU6", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32}> : (tensor<256x32x32x3xf32>) -> tensor<?xf32>

  %6 = arith.addf %2, %5 : tensor<1x1x1x16xf32>
  func.return %6 : tensor<1x1x1x16xf32>

// CHECK-LABEL: func @avgPool2D
// CHECK:  "tfl.average_pool_2d"(%arg0) <{filter_height = 3 : i32, filter_width = 6 : i32, fused_activation_function = "NONE", padding = "VALID", stride_h = 3 : i32, stride_w = 1 : i32}> : (tensor<1x6x6x16xf32>) -> tensor<1x1x1x16xf32>
// CHECK:  %1 = "tf.AvgPool"(%arg0)
// CHECK:  %2 = "tf.AvgPool"(%arg0)

`[filter_height, filter_width, in_channels, out_channels]`, this op
performs the following:

1. Flattens the filter to a 2-D matrix with shape
   `[filter_height * filter_width * in_channels, output_channels]`.
2. Extracts image patches from the input tensor to form a *virtual*
   tensor of shape `[batch, out_height, out_width,
   filter_height * filter_width * in_channels]`.

              conv_output_type, rewriter);
    }
    Value output;
    if (is_depthwise_conv && num_spatial_dims == 2) {
      // Reshapes filter format to [filter_height, filter_width, in_channels,
      // channel_multiplier] from HLO's [filter_height, filter_width, 1,
      // in_channels * channel_multiplier] format.
      auto filter_type = mlir::cast<ShapedType>(rhs.getType());

  %1 = "tfl.relu"(%0) : (tensor<1x73x73x16xf32>) -> tensor<1x73x73x16xf32>
  func.return %1 : tensor<1x73x73x16xf32>

  auto _stride_w = _o->stride_w;
  auto _stride_h = _o->stride_h;
  auto _filter_width = _o->filter_width;
  auto _filter_height = _o->filter_height;
  auto _fused_activation_function = _o->fused_activation_function;
  return tflite::CreatePool2DOptions(
      _fbb,
      _padding,
      _stride_w,
      _stride_h,
      _filter_width,
      _filter_height,
      _fused_activation_function);
}

  let description = [{
    Performs average-pooling operation on input.
  }];

  let arguments = (
    ins TFL_TensorOf<[F32, QI8, QUI8, QI16]>:$input,
    I32Attr:$filter_height,
    I32Attr:$filter_width,
    TFL_PaddingAttr:$padding,
    I32Attr:$stride_h,
    I32Attr:$stride_w,
    TFL_AFAttr:$fused_activation_function
  );

  let results = (outs TFL_TensorOf<[F32, QI8, QUI8, QI16]>:$output);

// CHECK-SAME: %[[ARG1:.*]]: tensor<!quant.uniform<i8:f32, 3.000000e-01:-5>>
// CHECK: %[[MAX_POOL:.*]] = "tfl.max_pool_2d"(%[[ARG0]])
// CHECK-SAME: {filter_height = 3 : i32, filter_width = 4 : i32, fused_activation_function = "NONE", padding = "VALID", stride_h = 2 : i32, stride_w = 3 : i32}