return %0 : tensor<1x3xf32>
  }
}

// CHECK-LABEL: quantize_dot_general_per_tensor
// CHECK-SAME: %[[ARG0:.+]]: tensor<1x2xf32>
// CHECK: %[[CST:.+]] = stablehlo.constant() <{value = dense<127> : tensor<2x3xi8>}> : () -> tensor<2x3x!quant.uniform<i8<-127:127>:f32, 0.0023622048182750312>>

  func.return %1 : tensor<2x2x!quant.uniform<u8:f32, 7.8431372549019615E-4:128>>

// CHECK:  %[[cst:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<2x2x!quant.uniform<u8:f32, 7.8431372549019615E-4:128>>, value = dense<0> : tensor<2x2xi8>}>
// CHECK:  return %[[cst]]
}

// CHECK-LABEL: QuantizeFloatConst4Bits
func.func @QuantizeFloatConst4Bits() -> tensor<2x4x!quant.uniform<i4:f32, 2.500000e-01:-1>> {

# CHECK-DAG: tf.InfeedDequeueTuple{{.+}}-> (tensor<?x?x?xui16>, tensor<*xui32>, tensor<7x56xui64>)
# CHECK-DAG: tf.InfeedDequeue{{.+}}-> tensor<?x8x?xi8>
# CHECK-DAG: tf.InfeedDequeue{{.+}}-> tensor<8x64xui8>

// CHECK:           return %[[VAL_2]] : tensor<1x4xi8>
// CHECK:         }
func.func @bitwise_or_broadcast_chlo(%arg0: tensor<1xi8>, %arg1: tensor<1x4xi8>) -> tensor<1x4xi8> {
  %0 = "chlo.broadcast_or"(%arg0, %arg1) {broadcast_dimensions = array<i64: 1>} : (tensor<1xi8>, tensor<1x4xi8>) -> tensor<1x4xi8>
  func.return %0 : tensor<1x4xi8>
}

// CHECK-LABEL:   func @bitwise_or_dynamic(

func.func @no_pushdown_gather_with_no_reduction(%arg0: tensor<2xi32>) -> tensor<2x2xf32> {
  %w = "tfl.pseudo_qconst"() {qtype = tensor<2x2x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>, value = dense<127> : tensor<2x2xi8>} : () -> tensor<2x2x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>
  %dq_w = "tfl.dequantize"(%w) : (tensor<2x2x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>) -> tensor<2x2xf32>

// TF-DAG: %[[cc_0:.*]] = "tf.ConcatV2"(%[[pc_1]], %[[pc_5]], %[[cst_0]]) : (tensor<2x2xi8>, tensor<2x2xi8>, tensor<i32>) -> tensor<2x4xi8>
// TF-DAG: %[[pc_7:.*]] = "tf.PartitionedCall"(%[[cc_0]], %[[cst_6]], %[[cst_5]]) <{config = "", config_proto = "", executor_type = "", f = @dequantize_i8}
// TF-DAG: return %[[pc_7]]

  %weight_zps = "tf.Const"() { value = dense<3> : tensor<i32> } : () -> tensor<i32>

  // CHECK: "tf.AddV2"
  // CHECK: mhlo.constant
  // CHECK-SAME{LITERAL}: dense<[[1, 2], [3, 4]]> : tensor<2x2xi8>
  %0 = "tf.AddV2"(%input, %input) : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>

  // CHECK: "mhlo.dot"
  // CHECK-SAME: (tensor<?x?xf32>, tensor<2x2x!quant.uniform<i8:f32, 1.000000e+00:3>>) -> tensor<?x?xf32>

  %outputs_18 = "tf.ExpandDims"(%outputs_16, %outputs_0) {device = ""} : (tensor<?x?x3xui8>, tensor<i32>) -> tensor<1x?x?x3xui8>
  %outputs_20 = "tf.ResizeBilinear"(%outputs_18, %outputs) {align_corners = false, device = "", half_pixel_centers = false} : (tensor<1x?x?x3xui8>, tensor<2xi32>) -> tensor<1x224x224x3xf32>

// CHECK: func.func private @quantize_dot_general_fn(%[[ARG_0:.+]]: tensor<1x2xf32>) -> tensor<1x3xf32> attributes {tf._original_func_name = "main_0"}
// CHECK: %[[CONST_0:.+]] = stablehlo.constant() <{value = dense<{{.*}}> : tensor<2x3xi8>}> : () -> tensor<2x3x!quant.uniform<i8<-127:127>:f32:1, {{.*}}>
// CHECK: %[[UNIFORM_QUANTIZE_0:.+]] = stablehlo.uniform_quantize %[[ARG_0]] : (tensor<1x2xf32>) -> tensor<1x2x!quant.uniform<i8:f32, {{.*}}>>

// CHECK-LABEL: func @DynamicStitch_uint8
func.func @DynamicStitch_uint8(%arg0: tensor<2x2xui8>) -> tensor<2x2xui8> {
  // CHECK-NOT: tf.DynamicStitch

  %indices = "tf.Const"() {value = dense<[1, 0]> : tensor<2xi32>} : () -> tensor<2xi32>
  %0 = "tf.DynamicStitch"(%indices, %arg0) : (tensor<2xi32>, tensor<2x2xui8>) -> tensor<2x2xui8>
  func.return %0 : tensor<2x2xui8>
}

// CHECK-LABEL: func @DynamicStitch_scalar_item