// CHECK-NOT: "tfl.batch_matmul"
func.func @BatchmatmulToReduceSumF32(%arg0: tensor<1x16384x257xf32>) -> (tensor<1x1x257xf32>) {
  %0 = arith.constant dense<1.0> : tensor<1x1x16384xf32>
  %1 = "tfl.batch_matmul"(%0, %arg0) {adj_x = false, adj_y = false} : (tensor<1x1x16384xf32>, tensor<1x16384x257xf32>) -> tensor<1x1x257xf32>
  func.return %1 : tensor<1x1x257xf32>

    %output_0, %min_1, %max_2, %histogram_3 = "tf.CustomAggregator"(%0) <{calibration_method = 1 : i32, id = "1", max_percentile = 0.000000e+00 : f32, min_percentile = 0.000000e+00 : f32, num_bins = 0 : i32}> : (tensor<10x1x3xf32>) -> (tensor<10x1x3xf32>, tensor<f32>, tensor<f32>, tensor<0xi64>)
    return %output_0 : tensor<10x1x3xf32>
  }
  // CHECK-LABEL: @main

  func.return %result : tensor<1x1x512xf32>

    %1 = stablehlo.maximum %0, %cst : tensor<10x1x3xf32>
    return %1 : tensor<10x1x3xf32>
  }
}

// -----

module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 1836 : i32}, tf_saved_model.semantics} {

  %0 = "tfl.var_handle"() {container = "", shared_name = "states"} : () -> tensor<!tf_type.resource<tensor<1x2x3xf32>>>
  %1 = "tfl.read_variable"(%0) : (tensor<!tf_type.resource<tensor<1x2x3xf32>>>) -> tensor<1x2x3xf32>
  %2 = "tfl.quantize"(%1) {qtype = tensor<1x2x3x!quant.uniform<u8<1:255>:f32, 0.047244094488188976:128>>} : (tensor<1x2x3xf32>) -> tensor<1x2x3x!quant.uniform<u8<1:255>:f32, 0.047244094488188976:128>>

  return %0 : tensor<1x1x1x5xf32>
}
func.func private @XlaCallModule_aten.avg_pool2d.default.impl_6(%arg0: tensor<1x1x1x8xf32>) -> tensor<1x1x1x5xf32>

// CHECK-LABEL: avg_pool2d_7
// CHECK: %cst = arith.constant dense<[0, 2, 3, 1]> : tensor<4xi32>

}

// -----

func.func @testSlice(%arg0: tensor<2x3x5xf32>, %arg1: tensor<3xi32>, %arg2: tensor<3xi32>) -> tensor<?x3x5xf32> {
  %0 = "tfl.slice"(%arg0, %arg1, %arg2) : (tensor<2x3x5xf32>, tensor<3xi32>, tensor<3xi32>) -> tensor<?x3x5xf32>
  func.return %0 : tensor<?x3x5xf32>
}

// -----

func.func @testSliceBadBeginDimension(%arg0: tensor<2x3x5xf32>, %arg1: tensor<2xi32>, %arg2: tensor<3xi32>) -> tensor<?x3x5xf32> {

  // CHECK:  return
}

func.func @pow(%arg0: tensor<2x1x3xf32>, %arg1: tensor<2x1x1xf32>) -> tensor<2x1x3xf32> {
  %0 = "tf.Pow"(%arg0, %arg1) : (tensor<2x1x3xf32>, tensor<2x1x1xf32>) -> tensor<2x1x3xf32>
  func.return %0 : tensor<2x1x3xf32>

  // CHECK-LABEL: pow
  // CHECK:  %[[pow:.*]] = tfl.pow(%arg0, %arg1) : (tensor<2x1x3xf32>, tensor<2x1x1xf32>) -> tensor<2x1x3xf32>
  // CHECK:  return
}

func.func @quantized_dot_general_not_converted(%arg0: tensor<1x1x512xf32>, %arg1: tensor<512x512x!quant.uniform<i8:f32, 0.00285>>) -> tensor<1x1x512xf32> {
  %0 = "mhlo.dot_general"(%arg0, %arg1) {
    dot_dimension_numbers = #mhlo.dot<
      lhs_contracting_dimensions = [2],
      rhs_contracting_dimensions = [0]
    >} : (tensor<1x1x512xf32>, tensor<512x512x!quant.uniform<i8:f32, 0.00285>>) -> tensor<1x1x512xf32>
  func.return %0 : tensor<1x1x512xf32>
}

func.func @leaky_relu_grad(%arg0: tensor<1x4x4xf32>, %arg1: tensor<1x4x4xf32>) -> tensor<1x4x4xf32> attributes {tf.entry_function = {}} {
    // CHECK-NEXT: %[[ALPHA:.*]] = "chlo.constant_like"(%arg1) <{value = 2.000000e-01 : f32}> : (tensor<1x4x4xf32>) -> tensor<1x4x4xf32>
    // CHECK-NEXT: %[[ZERO:.*]] = "chlo.constant_like"(%arg1) <{value = 0.000000e+00 : f32}> : (tensor<1x4x4xf32>) -> tensor<1x4x4xf32>