%cst_3 = arith.constant dense<2.0> : tensor<4x3x3x3xf32>
  %0 = "tfl.conv_2d"(%arg0, %cst_3, %cst) {dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "RELU6", padding = "VALID", stride_h = 2 : i32, stride_w = 2 : i32} : (tensor<?x19x19x3xf32>, tensor<4x3x3x3xf32>, tensor<4xf32>) -> tensor<?x9x9x4xf32>
  func.return %0 : tensor<?x9x9x4xf32>

# MLIR:         %[[conv:.*]] = "tfl.conv_2d"(%[[ARG_0]], %[[weight]], %[[bias]]) <{dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32}
# MLIR:         %[[reshape:.*]] = "tfl.reshape"(%[[conv]], %[[shape]]) : (tensor<1x1x1x186x!quant.uniform<i8:f32, 0.09363494573854933:22>>, tensor<3xi32>)

  // CHECK: "VALID"

// Tests that the stride_w is set to 2.

  // CHECK: %[[ARG3:.*]] = "tfl.transpose_conv"(%arg0, %[[ARG0]], %arg2, %[[CST_0]]) <{fused_activation_function = "NONE", padding = "VALID", stride_h = 2 : i32, stride_w = 2 : i32}> : (tensor<4xi32>, tensor<1x3x3x32xf32>, tensor<15x14x14x32xf32>, none) -> tensor<15x28x28x1xf32>...

  }

  /**
   * Creates a {@code Striped<Lock>} with eagerly initialized, strongly referenced locks. Every lock
   * is reentrant.
   *
   * @param stripes the minimum number of stripes (locks) required
   * @return a new {@code Striped<Lock>}
   */
  public static Striped<Lock> lock(int stripes) {
    return custom(stripes, PaddedLock::new);
  }

  /**

	"444": `
		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 {
	`,
	"422": `
		ciBase := (sy-src.Rect.Min.Y)*src.CStride - src.Rect.Min.X/2
		for x, sx := x0, sp.X; x != x1; x, sx, yi = x+4, sx+1, yi+1 {
			ci := ciBase + sx/2
	`,
	"420": `
		ciBase := (sy/2-src.Rect.Min.Y/2)*src.CStride - src.Rect.Min.X/2

  %5 = "tfl.conv_2d"(%2, %4, %cst) {dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 2 : i32, stride_w = 2 : i32} : (tensor<1x224x224x3xf32>, tensor<32x3x3x3xf32>, tensor<32xf32>) -> tensor<1x112x112x32xf32>

    // CHECK-NEXT: %3 = mhlo.reshape %2 : (tensor<1xi32>) -> tensor<i32>
    // CHECK-NEXT: %4 = mhlo.convert %3 : tensor<i32>
    // CHECK-NEXT: %5 = "mhlo.slice"(%1) <{limit_indices = dense<2> : tensor<1xi64>, start_indices = dense<1> : tensor<1xi64>, strides = dense<1> : tensor<1xi64>}> : (tensor<4xi32>) -> tensor<1xi32>

  %0 = "tfl.conv_2d"(%arg0, %dq, %cst_0) {dilation_h_factor = 2 : i32, dilation_w_factor = 3 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 4 : i32, stride_w = 5 : i32} : (tensor<256x32x32x3xf32>, tensor<3x3x3x3xf32>, tensor<3xf32>) -> tensor<256x8x7x3xf32>
  %1 = "tfl.mul"(%0, %cst) {fused_activation_function = "NONE"} : (tensor<256x8x7x3xf32>, tensor<3xf32>) -> tensor<256x8x7x3xf32>