func.func @EliminateReduceOpsFloat(%arg: tensor<1x2x1x3xf32>, %arg_scalar: tensor<f32>, %arg_unknown: tensor<?xf32>, %axis_unknown: tensor<?xi32>) -> (tensor<f32>, tensor<f32>, tensor<2x1x3xf32>, tensor<1x2x1x3xf32>, tensor<1x1x1x3xf32>, tensor<1x1x3xf32>, tensor<1x2x3xf32>, tensor<1x2x1x3xf32>, tensor<1x2x1xf32>, tensor<1x2x1x1xf32>, tensor<?xf32>) {
  %axis_0 = arith.constant dense<0> : tensor<1xi32>

  %0 = "mhlo.broadcast_in_dim"(%arg0) <{broadcast_dimensions = dense<[1, 2, 3]> : tensor<3xi64>, name = "broadcast.0"}> : (tensor<8x1x16xf32>) -> tensor<3x8x8x16xf32>
  func.return %0 : tensor<3x8x8x16xf32>
}

// CHECK-LABEL:   func @broadcast_in_dim_general_case(
// CHECK-SAME:                                        %[[VAL_0:.*]]: tensor<3x1x16xf32>) -> tensor<3x8x8x16xf32> {

  func.return %0 : tensor<2x2xf16>
}

// -----

// CHECK-LABEL: squeeze
func.func @squeeze(%arg0: tensor<1x1x10xf32>) -> tensor<1x10xf32> {
  // CHECK: mhlo.reshape
  %0 = "tf.Squeeze"(%arg0) : (tensor<1x1x10xf32>) -> tensor<1x10xf32>
  func.return %0 : tensor<1x10xf32>
}

// -----

// CHECK-LABEL: squeeze_ranked