}

func.func @gatherV2VectorIndices_I64Axis(%arg0 : tensor<1x2x20xf32>, %arg1 : tensor<3x5xi32>) -> tensor<1x3x5x20xf32> {
  %0 = "tf.Const"() { value = dense<[1]> : tensor<1xi64> } : () -> tensor<1xi64>
  %1 = "tf.GatherV2"(%arg0, %arg1, %0) : (tensor<1x2x20xf32>, tensor<3x5xi32>, tensor<1xi64>) -> tensor<1x3x5x20xf32>
  func.return %1 : tensor<1x3x5x20xf32>

func.func @quantized_dot_general_not_converted(%arg0: tensor<1x1x512xf32>, %arg1: tensor<512x512x!quant.uniform<i8:f32, 0.00285>>) -> tensor<1x1x512xf32> {
  %0 = "mhlo.dot_general"(%arg0, %arg1) {
    dot_dimension_numbers = #mhlo.dot<
      lhs_contracting_dimensions = [2],
      rhs_contracting_dimensions = [0]
    >} : (tensor<1x1x512xf32>, tensor<512x512x!quant.uniform<i8:f32, 0.00285>>) -> tensor<1x1x512xf32>
  func.return %0 : tensor<1x1x512xf32>
}

// -----

// 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>
}