func.func @shape_from_const_input(%arg0: tensor<3x3x32x64xf32>, %arg1: tensor<200x24x24x64xf32>) -> tensor<?x?x?x?xf32> {
    %0 = "tf.Const"() {value = dense<[200, 26, 26, 32]> : tensor<4xi32>} : () -> tensor<4xi32>
    // CHECK: tf.Conv2DBackpropInput
    // CHECK-SAME: (tensor<4xi32>, tensor<3x3x32x64xf32>, tensor<200x24x24x64xf32>) -> tensor<200x26x26x32xf32>

  }

  // CHECK-LABEL: @max_pool_grad_valid
  // CHECK-SAME: %[[INPUT:.*]]: tensor<10x24x24x64xf32>, %arg1: tensor<10x12x12x64xf32>, %[[GRAD:.*]]: tensor<10x12x12x64xf32>
  func.func @max_pool_grad_valid(%orig_input: tensor<10x24x24x64xf32>, %orig_output: tensor<10x12x12x64xf32>, %grad: tensor<10x12x12x64xf32>) -> tensor<10x24x24x64xf32> {
    // CHECK: %[[ZERO:.*]] = mhlo.constant dense<0.000000e+00> : tensor<f32>

  func.return %result : tensor<10x24x24x64xf32>
}

// -----

func.func @testMaxPoolGrad(%orig_input: tensor<10x24x24x64xf32>, %orig_output: tensor<10x12x12x64xf32>, %grad: tensor<12x12x64xf32>) -> tensor<10x24x24x64xf32> {
  // expected-error @+1 {{requires grad to be rank 4}}
  %result = "tf.MaxPoolGrad"(%orig_input, %orig_output, %grad) {
     data_format = "NHWC",
     ksize = [1, 2, 2, 1],

  // CHECK: }) : (tensor<10x24x24x64xf32>, tensor<10x12x12x64xf32>, tensor<f32>) -> tensor<10x24x24x64xf32>
  // CHECK: return %[[RESULT]] : tensor<10x24x24x64xf32>
  %result = "tf.MaxPoolGrad"(%orig_input, %orig_output, %grad) {
     data_format = "NHWC",
     ksize = [1, 2, 2, 1],
     padding = "VALID",
     strides = [1, 2, 2, 1]