return %0 : tensor<1x3xf32>
}

// CHECK-LABEL: @_func
func.func private @_func(%arg0: tensor<1x3xf32>) -> (tensor<1x3xf32>) {
  return %arg0 : tensor<1x3xf32>
}
}


// -----
// SPMD: one input and no return
//
// CHECK-LABEL: func.func @serving_default(%arg0: tensor<1x3xf32>) {
// CHECK-NEXT:  "tf.IfrtCall"(%arg0)
// CHECK-SAME:       {program_id = [[PROGRAM_ID:.*]] : i64, variable_arg_indices = []}

// CHECK:           return %[[VAL_3]] : tensor<2xf32>
// CHECK:         }
func.func @floordiv_f32(%arg0: tensor<2xf32>) -> tensor<2xf32> {
  %0 = mhlo.divide %arg0, %arg0 : tensor<2xf32>
  %1 = mhlo.divide %arg0, %arg0 : tensor<2xf32>
  %2 = "mhlo.floor"(%1) : (tensor<2xf32>) -> tensor<2xf32>
  func.return %2 : tensor<2xf32>
}

// CHECK-LABEL:   func @floordiv_f16_broadcast(

// CHECK-LABEL: func private @batched_function
func.func private @batched_function(%arg0: tensor<1x3xf32>) -> tensor<1x3xf32> {
  %2 = "tf.Identity"(%arg0) : (tensor<1x3xf32>) -> tensor<1x3xf32>
  func.return %2 : tensor<1x3xf32>
}

// CHECK-LABEL: func @main
func.func @main(%arg0: tensor<1x3xf32>) -> tensor<*xf32> {
  // CHECK:  "tf.BatchFunction"
  // CHECK-SAME: allowed_batch_sizes = [6]

// CHECK-LABEL: nchw_conv
// CHECK-SAME: %[[ARG:.+]]: tensor<1x8x4x4xf32>
func.func @nchw_conv(%arg0: tensor<1x8x4x4xf32>) -> tensor<1x8x4x4xf32> {
  %0 = stablehlo.constant() {value = dense<7.000000e+00> : tensor<8x8x3x3xf32>} : () -> tensor<8x8x3x3xf32>

}

func.func @RemoveTrivialAdd(%arg0: tensor<2x2xf32>) -> tensor<2x2xf32> {
  %cst = arith.constant dense<0.0> : tensor<2x2xf32>
  %0 = "tf.Add"(%arg0, %cst) : (tensor<2x2xf32>, tensor<2x2xf32>) -> tensor<2x2xf32>
  func.return %0 : tensor<2x2xf32>

  // CHECK-LABEL: RemoveTrivialAdd
  // CHECK-NEXT: return %arg0 : tensor<2x2xf32>
}

func.func @RemoveTrivialAddBf16RHS(%arg0: tensor<2x2xbf16>) -> tensor<2x2xbf16> {

    return
  }
  // CHECK-LABEL: func private @foo.impl
  func.func private @foo.impl(%arg0: tensor<1xf32>, %arg1: tensor<2xf32>) -> (tensor<2xf32>, tensor<1xf32>) {
    return %arg1, %arg0 : tensor<2xf32>, tensor<1xf32>
  }

// RUN:   -split-input-file | FileCheck %s --check-prefix=STATIC-RANGE-PTQ-TO-COMPUTE-HEAVY

// STATIC-RANGE-PTQ-TO-COMPUTE-HEAVY: @main
func.func @main(%arg0: tensor<1x2xf32>) -> tensor<1x2xf32> {
  %0 = stablehlo.add %arg0, %arg0 : tensor<1x2xf32>
  return %0 : tensor<1x2xf32>
}
// Tests that `composite_add_fn_1` does not quantize when quantizing
// only compute-heavy ops.

func.func @testNoClusterFuncOpPasses(%arg0: tensor<4x?x!tf_type.stringref>) -> tensor<4x2x!tf_type.string> {
  %0 = "tf.Identity"(%arg0) : (tensor<4x?x!tf_type.stringref>) -> tensor<4x2x!tf_type.string>
  func.return %0 : tensor<4x2x!tf_type.string>
}

// -----

func.func @_func(%arg0: tensor<i32>) -> tensor<i32> {
  func.return %arg0 : tensor<i32>
}

func.func @testClusterFuncOpFails(%arg0: tensor<i32>) -> tensor<i32> {

}

// CHECK-LABEL: gelu_aten
// CHECK: %0 = "tfl.gelu"(%arg0) <{approximate = false}> : (tensor<5x10xf32>) -> tensor<5x10xf32>

func.func private @gelu_decomp_2(%arg0: tensor<5x10xf32>) -> tensor<5x10xf32>
func.func @gelu_aten_approximate(%arg0: tensor<5x10xf32>) -> (tensor<*xf32>) {

  }

  func.func private @composite_dot_general_fn(%arg0: tensor<1x2xf32>, %arg1: tensor<2x3xf32>) -> tensor<1x3xf32> attributes {_from_xla_call_module} {
    %0 = stablehlo.dot_general %arg0, %arg1, contracting_dims = [1] x [0] : (tensor<1x2xf32>, tensor<2x3xf32>) -> tensor<1x3xf32>
    return %0 : tensor<1x3xf32>
  }
}

// CHECK-LABEL: quantize_dot_general_per_tensor
// CHECK-SAME: %[[ARG0:.+]]: tensor<1x2xf32>