func.func @batchmatmulv2_basic(%arg0: tensor<1x4x2xf32>, %arg1: tensor<3x2x4xf32>) -> tensor<3x4x4xf32> {
// CHECK-LABEL:   func @batchmatmulv2_basic
// CHECK-SAME:        ([[LHS:%.*]]: tensor<1x4x2xf32>, [[RHS:%.*]]: tensor<3x2x4xf32>) -> tensor<3x4x4xf32>
// CHECK:           [[LHSSHAPE:%.*]] = shape.shape_of [[LHS]] : tensor<1x4x2xf32>
// CHECK:           [[RHSSHAPE:%.*]] = shape.shape_of [[RHS]] : tensor<3x2x4xf32>
// CHECK:           [[CM2:%.*]] = arith.constant -2 : index

    // CHECK: mhlo.reduce
    // CHECK: mhlo.dot_general
    // CHECK: mhlo.transpose
    %0 = "tf.BatchMatMulV2"(%arg0, %arg1) {T = f32, adj_x = false, adj_y = false, grad_x = false, grad_y = false, device = ""} : (tensor<1x4x2xf32>, tensor<3x2x4xf32>) -> tensor<3x4x4xf32>
    func.return %0 : tensor<3x4x4xf32>
  }

  // CHECK-LABEL: approx_topk

  // CHECK-NEXT: %8 = "mhlo.set_dimension_size"(%arg0, %7) <{dimension = 0 : i64}> : (tensor<3x4xi32>, tensor<i32>) -> tensor<3x4xi32>
  // CHECK-NEXT: %9 = mhlo.constant dense<4> : tensor<i32>
  // CHECK-NEXT: %10 = "mhlo.set_dimension_size"(%8, %9) <{dimension = 1 : i64}> : (tensor<3x4xi32>, tensor<i32>) -> tensor<3x4xi32>
  // CHECK-NEXT: %11 = mhlo.constant dense<0> : tensor<i32>

module attributes {tf.versions = {producer = 888 : i32}} {
func.func @testReshapeShapeInference(%arg0: tensor<3x4xi32>) -> tensor<*xi32> {
  %cst = arith.constant dense<[1, 6, 2]> : tensor<3xi32>
  // CHECK: "tfl.reshape"(%arg0, %cst) : (tensor<3x4xi32>, tensor<3xi32>) -> tensor<1x6x2xi32>
  %0 = "tfl.reshape"(%arg0, %cst) : (tensor<3x4xi32>, tensor<3xi32>) -> tensor<*xi32>
  func.return %0 : tensor<*xi32>
}
}

// -----

// CHECK-NEXT:    %[[BMM_0:.*]] = "tfl.batch_matmul"(%[[RESHAPED_0]], %[[RESHAPED_1]]) <{adj_x = false, adj_y = false, asymmetric_quantize_inputs = false}> : (tensor<3x5x12xf32>, tensor<3x12x4xf32>) -> tensor<3x5x4xf32>
// CHECK-NEXT:    %[[RESHAPED_BMM:.*]] = mhlo.reshape %[[BMM_0]]
// CHECK-NEXT:    return %[[RESHAPED_BMM]] : tensor<3x5x1x4xf32>
}

func.func @valid_unranked_inputs_on_reshape(%arg0: tensor<3x4xi32>, %arg1: tensor<*xi32>) -> tensor<3x4xi32> {
  // CHECK: "tfl.reshape"(%arg0, %arg1)
  %0 = "tfl.reshape"(%arg0, %arg1) : (tensor<3x4xi32>, tensor<*xi32>) -> tensor<3x4xi32>
  func.return %0 : tensor<3x4xi32>
}

// -----

// CHECK-LABEL: valid_one_dynamic_dim_on_reshape
func.func @valid_one_dynamic_dim_on_reshape(%arg0: tensor<3x4xi32>) -> tensor<1x3x4xi32> {

    slice_sizes = #vhlo.tensor_v1<dense<[1, 2, 2]> : tensor<3xi64>>,
    indices_are_sorted = #vhlo.bool_v1<false>
  }> : (tensor<3x4x2xi32>, tensor<2x3x2xi64>) -> tensor<2x3x2x2xi32>
  return %result : tensor<2x3x2x2xi32>
}


// CHECK: func.func private @gather(%arg0: tensor<3x4x2xi32>, %arg1: tensor<2x3x2xi64>) -> tensor<2x3x2x2xi32> {

  %0 = "tf.InplaceUpdate"(%arg0, %arg2, %arg1) : (tensor<8x8x4xf32>, tensor<3xi32>, tensor<3x8x4xf32>) -> tensor<8x8x4xf32>

  // CHECK:  return [[UPDATE3]] : tensor<8x8x4xf32>
  func.return %0 : tensor<8x8x4xf32>
}

// -----

// CHECK-LABEL: xla_dynamic_update_slice

// tensorflow/lite/kernels/transpose_test.cc
func.func @transpose_3d() -> tensor<4x2x3xi32> {
  %cst = arith.constant dense<[[[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]], [[12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23]]]> : tensor<2x3x4xi32>
  %cst_perm = arith.constant dense<[2, 0, 1]> : tensor<3xi32>

  %0 = stablehlo.constant dense<1> : tensor<3x4x2xi32>
  %1 = stablehlo.constant dense<1> : tensor<2x3x2xi64>
  %2 = "stablehlo.gather"(%0, %1) {
  dimension_numbers = #stablehlo.gather<
    offset_dims = [2, 3],
    collapsed_slice_dims = [0],
    start_index_map = [1, 0],
    index_vector_dim = 2>,
  slice_sizes = array<i64: 1, 2, 2>,
  indices_are_sorted = false
} : (tensor<3x4x2xi32>, tensor<2x3x2xi64>) -> tensor<2x3x2x2xi32>