// CHECK:           return %[[VAL_14]] : tensor<16x32x256xbf16>
// CHECK:         }
func.func @convert_conv1d(%arg0: tensor<16x32x256xbf16>, %arg1: tensor<1x256x256xbf16>) -> tensor<16x32x256xbf16> {
	%0 = "mhlo.convolution"(%arg0, %arg1) {
    batch_group_count = 1 : i64,
    dimension_numbers = #mhlo.conv<[b, 0, f]x[0, i, o]->[b, 0, f]>,
    feature_group_count = 1 : i64,
    lhs_dilation = dense<1> : tensor<1xi64>,

    // all spatial dimensions.
    const int64_t filter_channels = GetDimSize(filter_ty, num_spatial_dims);
    // TensorFlow convolution op verifies that the number of input channels is
    // divisible by the number of filter channels.
    // For depthwise convolution the feature_group_count argument would be set
    // to the input feature dimension.
    const int64_t feature_group_count =

// -----

// CHECK-LABEL: conv_simple
func.func @conv_simple(%arg0: tensor<256x32x32x6xf32>, %arg1: tensor<3x3x3x16xf32>) -> tensor<256x8x7x16xf32> {

  // CHECK: mhlo.convolution(%arg0, %arg1)
  // CHECK-SAME: dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f]
  // CHECK-SAME{LITERAL}: window = {stride = [4, 5], pad = [[0, 1], [2, 3]], rhs_dilate = [2, 3]}
  // CHECK-SAME: batch_group_count = 1