// CHECK:           %[[VAL_4:.*]] = "tfl.pad"(%[[VAL_2]], %[[VAL_3]]) : (tensor<1x6x6x3xf32>, tensor<4x2xi32>) -> tensor<1x6x6x3xf32>
// 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>

^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]`.

  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);

  pool_params.stride_height = op.stride_h().getSExtValue();
  pool_params.stride_width = op.stride_w().getSExtValue();
  pool_params.filter_height = op.filter_h().getSExtValue();
  pool_params.filter_width = op.filter_w().getSExtValue();
  const auto padding = GetTflitePadding(inst, op.padding());
  if (padding) {
    pool_params.padding = *padding;
    pool_params.activation = kTfLiteActNone;