// CHECK-LABEL: testSpaceToDepth
func.func @testSpaceToDepthF32(%arg0: tensor<1x2x2x1xf32>) -> tensor<1x1x1x4xf32> {
  // CHECK: %[[ARG:.*]]: tensor<1x2x2x1xf32>
  // CHECK: "tfl.space_to_depth"(%[[ARG]]) <{block_size = 2 : i32}> : (tensor<1x2x2x1xf32>) -> tensor<1x1x1x4xf32>
  %0 = "tfl.space_to_depth"(%arg0) {block_size = 2: i32} : (tensor<1x2x2x1xf32>) -> tensor<1x1x1x4xf32>
  func.return %0 : tensor<1x1x1x4xf32>
}

// -----

    quantization_axis = -1 : i64, quantization_min_val = -128 : i64, quantization_max_val = 127 : i64
  } : (tensor<1x2x2x1x!tf_type.qint32>, tensor<f32>, tensor<i32>) -> tensor<1x2x2x1xf32>

  return %output : tensor<1x2x2x1xi32>

// CHECK:           %[[VAL_2:.*]] = "tf.Conv2D"(%[[VAL_0]], %[[VAL_1]]) <{data_format = "NHWC", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true}> : (tensor<1x6x6x207xf32>, tensor<3x3x207x16xf32>) -> tensor<1x4x4x16xf32>
// CHECK:           return %[[VAL_2]] : tensor<1x4x4x16xf32>
// CHECK:         }

  func.return %0 : tensor<5x2x5x10xf32>
}

// -----

func.func @testBatchMatMulV2DynamicOutputBatchDimension(%lhs: tensor<1x2x5x10xf32>, %rhs: tensor<1x10xf32>) -> tensor<?x2x5x10xf32> {
  %0 = "tf.BatchMatMulV2"(%lhs, %rhs) : (tensor<1x2x5x10xf32>, tensor<1x10xf32>) -> tensor<?x2x5x10xf32>
  func.return %0 : tensor<?x2x5x10xf32>
}

// -----

// -----

// CHECK-LABEL: conv_valid_padding
func.func @conv_valid_padding(%arg0: tensor<1x4x5x1xf32>, %arg1: tensor<3x3x1x1xf32>) -> tensor<1x2x3x1xf32> {
  // CHECK: mhlo.convolution(%arg0, %arg1)

  %0 = "tf.Conv2D"(%arg0, %arg1) {data_format = "NHWC", dilations = [1, 1, 1, 1], padding = "VALID", strides = [1, 1, 1, 1]} : (tensor<1x4x5x1xf32>, tensor<3x3x1x1xf32>) -> tensor<1x2x3x1xf32>
  func.return %0 : tensor<1x2x3x1xf32>
}