%shape_64 = "tf.TensorListElementShape"(%tl_1) : (tensor<!tf_type.variant<tensor<?x1xf32>>>) -> tensor<?xi64>
    // CHECK: %[[CAST:.*]] = "tf.Cast"(%[[ELEMENT_SHAPE]]){{.*}}: (tensor<2xi32>) -> tensor<2xi64>
    // CHECK: "tf._SomeOtherOp"(%[[ELEMENT_SHAPE]], %[[CAST]])
    "tf._SomeOtherOp"(%shape_32, %shape_64) : (tensor<?xi32>, tensor<?xi64>) -> ()
    func.return
  }

  // CHECK-LABEL: refine_pop_back_results_from_operands

  %2 = mhlo.constant dense<0.000000e+00> : tensor<f32>

// CHECK: return %[[GATHER]] : tensor<1x300x10xf32>

// -----

module {
  func.func @propagate_xlagather(%arg0: tensor<10x2xi32>) -> tensor<1x300x10xf32> {
    %cst = "tf.Const"() {value = dense<[1, 1, 300]> : tensor<3xi64>} : () -> tensor<3xi64>

  // CHECK-NEXT:  return [[FIND]] : tensor<*xi64>
  func.return %1 : tensor<*xi64>
}

// -----

// Test for case with int64 -> string hashtable.
func.func @hashtable_int64_to_string(%arg0: tensor<i64>) -> tensor<*x!tf_type.string> {
  %cst = arith.constant dense<"f"> : tensor<!tf_type.string>

  // CHECK: %[[CST:.*]] = "tf.Const"() <{value = dense<[0, 2, 3, 1]> : tensor<4xi64>}>
  // CHECK: %[[R0:.*]] = "tf.Transpose"(%[[ARG0]], %[[CST]])
  // CHECK: %[[R1:.*]] = "tf.BiasAdd"(%[[R0]], %[[ARG1]]) <{data_format = "NHWC"}> {device = ""}
  // CHECK: %[[CST_0:.*]] = "tf.Const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi64>}>
  // CHECK: "tf.Transpose"(%[[R1]], %[[CST_0]])

  %2 = "mhlo.dynamic_broadcast_in_dim"(%cst_0, %1) <{broadcast_dimensions = dense<3> : tensor<1xi64>}> : (tensor<2xf32>, tensor<4xindex>) -> tensor<?x256x256x2xf32>
  %3 = mhlo.multiply %0, %2 : tensor<?x256x256x2xf32>
  %4 = "mhlo.dynamic_broadcast_in_dim"(%cst_1, %1) <{broadcast_dimensions = dense<3> : tensor<1xi64>}> : (tensor<2xf32>, tensor<4xindex>) -> tensor<?x256x256x2xf32>
  %5 = mhlo.add %3, %4 : tensor<?x256x256x2xf32>

  %0 = "tf.Const"() {value = dense<42> : tensor<1xi64>} : () -> tensor<1xi64>
  return
}
func.func @init2() attributes {tf_saved_model.exported_names = ["init2"]} {
  %0 = "tf.Const"() {value = dense<43> : tensor<1xi64>} : () -> tensor<1xi64>
  return
}
func.func @init3() attributes {tf_saved_model.exported_names = ["init3"]} {
  %0 = "tf.Const"() {value = dense<44> : tensor<1xi64>} : () -> tensor<1xi64>
  return
}

// CHECK-LABEL: func @transposeConv2D
func.func @transposeConv2D(%input: tensor<1x32x32x3xf32>, %filter: tensor<1x1x3x8xf32>) -> tensor<1x7x7x8xf32> {

  // CHECK: %[[ARG_PERM:.*]] = "tf.Const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi64>}>
  // CHECK: %[[ARG_TRANSPOSE:[0-9]*]] = "tf.Transpose"(%arg0, %[[ARG_PERM]])

  // CHECK: %[[CONV2D:[0-9]*]] = "tf.Conv2D"(%[[ARG_TRANSPOSE]], %arg1)
  // CHECK-SAME: data_format = "NCHW"

  func.return %2, %3 : tensor<*xi64>, tensor<*xi64>
}

// -----

// CHECK-LABEL: @not_eliminate_multinomial
func.func @not_eliminate_multinomial(%0: tensor<*xf32>, %1: tensor<*xi32>) -> (tensor<*xi64>, tensor<*xi64>) {
  // CHECK-NEXT: tf.Multinomial
  // CHECK-SAME: seed = 0
  // CHECK-NEXT: tf.Multinomial
  // CHECK-SAME: seed = 1
  // CHECK-NEXT: tf.Multinomial

  func.return %cst : tensor<1x750xf32>

  // CHECK-DAG: %0 = mhlo.constant dense<7.680000e+02> : tensor<f32>
  // CHECK: %1 = "mhlo.broadcast_in_dim"(%0) <{broadcast_dimensions = dense<> : tensor<0xi64>}> : (tensor<f32>) -> tensor<1x750xf32>
  // CHECK: return %1 : tensor<1x750xf32>
}

// CHECK-LABEL: @unfold_splat_constant_integer
func.func @unfold_splat_constant_integer() -> tensor<1x750xi32> {