// CHECK-SAME: data_format = "NCHW"
  // CHECK-SAME: dilations = [1, 4, 2, 3]
  // CHECK-SAME: explicit_paddings = [1, 2, 7, 8, 3, 4, 5, 6]
  // CHECK-SAME: padding = "EXPLICIT"
  // CHECK-SAME: strides = [5, 8, 6, 7]
  // CHECK-SAME: (tensor<1x3x32x32xf32>, tensor<1x1x3x8xf32>) -> tensor<1x8x7x7xf32>

  // CHECK: %[[RES_PERM:.*]] = "tf.Const"() <{value = dense<[0, 2, 3, 1]> : tensor<4xi64>}>

// CHECK: %[[RES0:.*]] = "tfl.conv_2d"(%arg0, %arg1, %[[CST]]) <{dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32}> : (tensor<256x32x32x3xf32>, tensor<16x3x3x3xf32>, none) -> tensor<256x32x32x16xf32>
// CHECK: return %[[RES0]] : tensor<256x32x32x16xf32>

{
  "version": 3,
  "operator_codes": [
    {

	switch src.SubsampleRatio {

	case image.YCbCrSubsampleRatio444:
		for y, sy := y0, sp.Y; y != y1; y, sy = y+1, sy+1 {
			dpix := dst.Pix[y*dst.Stride:]
			yi := (sy-src.Rect.Min.Y)*src.YStride + (sp.X - src.Rect.Min.X)

			ci := (sy-src.Rect.Min.Y)*src.CStride + (sp.X - src.Rect.Min.X)
			for x := x0; x != x1; x, yi, ci = x+4, yi+1, ci+1 {

				// This is an inline version of image/color/ycbcr.go's func YCbCrToRGB.

      return failure();

    // We also need constant begin/end indices and strides to perform padding
    // calculations.
    // Bounded shape after performing strided slice
    SmallVector<int64_t, 4> shape;
    // Bounded begin, end, and strides for strided slice
    SmallVector<int64_t, 4> begin_indices, end_indices, strides;
    if (!op.GetSlicedShapeAndBoundRanges(&shape, &begin_indices, &end_indices,

		b[unzig[zig]] *= qt[zig]
	}
	idct(b)
	dst, stride := []byte(nil), 0
	if d.nComp == 1 {
		dst, stride = d.img1.Pix[8*(by*d.img1.Stride+bx):], d.img1.Stride
	} else {
		switch compIndex {
		case 0:
			dst, stride = d.img3.Y[8*(by*d.img3.YStride+bx):], d.img3.YStride
		case 1:
			dst, stride = d.img3.Cb[8*(by*d.img3.CStride+bx):], d.img3.CStride
		case 2:
			dst, stride = d.img3.Cr[8*(by*d.img3.CStride+bx):], d.img3.CStride

    padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32
  } : (tensor<1x5x5x2xf32>, tensor<3x1x1x2xf32>, tensor<3xf32>) -> tensor<1x5x5x3xf32>
  %conv2 = "tfl.conv_2d"(%0, %w, %b2) {
    dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "RELU",
    padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32

  // CHECK-NEXT:      builtin_options_type: DepthwiseConv2DOptions,
  // CHECK-NEXT:      builtin_options: {
  // CHECK-NEXT:        padding: VALID,
  // CHECK-NEXT:        stride_w: 5,
  // CHECK-NEXT:        stride_h: 4,
  // CHECK-NEXT:        depth_multiplier: 4
  // CHECK-NEXT:      }
  // CHECK-NEXT:    } ],
  // CHECK-NEXT:    name: "main"
  // CHECK-NEXT:  } ],

// CHECK-NEXT:      inputs: [ 0, 1, 2 ],
// CHECK-NEXT:      outputs: [ 3 ],
// CHECK-NEXT:      builtin_options_type: TransposeConvOptions,
// CHECK-NEXT:      builtin_options: {
// CHECK-NEXT:        stride_w: 2,
// CHECK-NEXT:        stride_h: 2
// CHECK-NEXT:      }
// CHECK-NEXT:    } ],
// CHECK-NEXT:    name: "main"
// CHECK-NEXT:  } ],
// CHECK-NEXT:  description: "MLIR Converted.",
// CHECK-NEXT:  buffers: [ {
// CHECK-EMPTY:

            1,
            -1
          ],
          "outputs": [
            3
          ],
          "builtin_options_type": "Conv2DOptions",
          "builtin_options": {
            "stride_w": 1,
            "stride_h": 1
          }
        }
      ],
      "name": "main"
    }
  ],
  "description": "MLIR Converted."

  // CHECK-NEXT:      builtin_options_type: DepthwiseConv2DOptions,
  // CHECK-NEXT:      builtin_options: {
  // CHECK-NEXT:        padding: VALID,
  // CHECK-NEXT:        stride_w: 5,
  // CHECK-NEXT:        stride_h: 4,
  // CHECK-NEXT:        depth_multiplier: 4,
  // CHECK-NEXT:        dilation_w_factor: 2,
  // CHECK-NEXT:        dilation_h_factor: 2
  // CHECK-NEXT:      }
  // CHECK-NEXT:    } ],