func.func @broadcast_to_i16_low_dim(%arg0: tensor<3xi16>, %arg1: tensor<2xi32>) -> tensor<3x3xi16> {
  %0 = "tfl.broadcast_to"(%arg0, %arg1) : (tensor<3xi16>, tensor<2xi32>) -> tensor<3x3xi16>
  return %0 : tensor<3x3xi16>
  // CHECK:  %cst = arith.constant dense<1> : tensor<3x3xi16>
  // CHECK:  %0 = tfl.mul(%arg0, %cst) <{fused_activation_function = "NONE"}> : (tensor<3xi16>, tensor<3x3xi16>) -> tensor<3x3xi16>
  // CHECK:  return %0 : tensor<3x3xi16>

				for j := 0; j < hi*vi; j++ {
					// The blocks are traversed one MCU at a time. For 4:2:0 chroma
					// subsampling, there are four Y 8x8 blocks in every 16x16 MCU.
					//
					// For a sequential 32x16 pixel image, the Y blocks visiting order is:
					//	0 1 4 5
					//	2 3 6 7
					//
					// For progressive images, the interleaved scans (those with nComp > 1)

  %rhs_dilation = "tf.Const"() {value = dense<1> : tensor<3xi32>} : () -> tensor<3xi32>
  %padding = "tf.Const"() {value = dense<0> : tensor<3x2xi32>} : () -> tensor<3x2xi32>
  %strides = "tf.Const"() {value = dense<[3, 1, 1]> : tensor<3xi32>} : () -> tensor<3xi32>

// CHECK: %[[PAD:.*]] = "tf.PadV2"({{.*}}, %[[CONST]], %[[CONST_1]])
// CHECK: %[[CONV:.*]] = "tf.XlaConvV2"(%[[PAD]], %[[WEIGHT]]
// CHECK-SAME: (tensor<1x4x5x5x3xi8>, tensor<2x3x3x3x2xi8>, tensor<3xi32>, tensor<3x2xi32>, tensor<3xi32>, tensor<3xi32>, tensor<i32>) -> tensor<1x3x2x3x2xi32>
// CHECK: %[[SUB:.*]] = "tf.Sub"(%[[CONV]], %[[CONST_2]])
}

// -----

module attributes {tf_saved_model.semantics} {

func.func @dot_general_upstream_srq_per_axis_quantized_filter(%arg0: tensor<1x3x!quant.uniform<i8:f32, 5.000000e+05:-100>>) -> tensor<1x2x!quant.uniform<i8:f32, 4.000000e+04:127>> {
  %0 = stablehlo.constant() {value = dense<1> : tensor<3x2xi8>} : () -> tensor<3x2x!quant.uniform<i8:f32:1,{2.000000e+02, 3.000000e+03}>>

// Returns a RankedTensorType which is similar to `input_type` but replaces the
// dimension size of `dim` with `dim_size`.  For example,
// `SubstituteRankedTensorTypeDimSize(tensor<3x4xi32>, 1, 2)` returns
// `tensor<3x2xi32>`.
static RankedTensorType SubstituteRankedTensorTypeDimSize(
    RankedTensorType input_type, int64_t dim, int64_t dim_size) {
  auto shape = input_type.getShape().vec();
  shape[dim] = dim_size;