func.return %0 : tensor<3x2x3xi16>
}

// -----

func.func @testBitcast_v8(%arg0: tensor<3x2xi32>) -> tensor<3x2xi16> {
  // expected-error @+1 {{rank of input tensor is 2. rank of output tensor is expected to be 3, instead of 2.}}
  %0 = "tf.Bitcast"(%arg0) : (tensor<3x2xi32>) -> tensor<3x2xi16>
  func.return %0 : tensor<3x2xi16>
}

// -----

func.func @bitcastI32ToI16(%arg0: tensor<8xi32>) -> tensor<8x2xi16> {
  %0 = "tf.Bitcast"(%arg0) : (tensor<8xi32>) -> tensor<8x2xi16>
  func.return %0 : tensor<8x2xi16>

// CHECK-LABEL: bitcastI32ToI16
// CHECK: %[[RES0:.*]] = "tfl.bitcast"(%arg0) : (tensor<8xi32>) -> tensor<8x2xi16>
// CHECK:  return %[[RES0]] : tensor<8x2xi16>
}

func.func @bitcastI16ToUI32(%arg0: tensor<8x2xi16>) -> tensor<8xui32> {

  func.func @fill_with_shape_op(%arg0: tensor<3x2xi32>, %arg1: tensor<i32>) -> (tensor<*xi32>) {
    // CHECK: %[[SHAPE:.*]] = "tf.Shape"(%{{.*}}) : (tensor<3x2xi32>) -> tensor<2xi32>
    // CHECK: %[[FILL:.*]] = "tf.Fill"(%[[SHAPE]], %{{.*}}) : (tensor<2xi32>, tensor<i32>) -> tensor<3x2xi32>
    // CHECK: return %[[FILL]] : tensor<3x2xi32>
    %0 = "tf.Shape"(%arg0) : (tensor<3x2xi32>) -> tensor<2xi32>

}

// -----

// CHECK-LABEL: testPad
func.func @testPad(tensor<2x1x3xf32>, tensor<3x2xi32>) -> tensor<? x f32> {
^bb0(%arg0: tensor<2x1x3xf32>, %arg1: tensor<3x2xi32>):
  // CHECK: "tfl.pad"(%arg0, %arg1)
  %0 = "tfl.pad"(%arg0, %arg1) : (tensor<2x1x3xf32>, tensor<3x2xi32>) -> tensor<? x f32>
  func.return %0#0 : tensor<? x f32>
}

// -----

// CHECK-LABEL: testPad5D

  %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>

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;