func.func @slice_variable_start(%arg0: tensor<3x4xi32>, %arg1: tensor<2xi64>) -> tensor<1x4xi32> {
  // CHECK: %[[SLICED_START1:.*]] = "mhlo.slice"(%arg1)
  // CHECK-DAG-SAME: {limit_indices = dense<1> : tensor<1xi64>,
  // CHECK-DAG-SAME: start_indices = dense<0> : tensor<1xi64>,
  // CHECK-DAG-SAME: strides = dense<1> : tensor<1xi64>} : (tensor<2xi64>) -> tensor<1xi64>

// CHECK-NEXT:      %16 = "tfl.pseudo_const"() <{value = dense<[0, 1]> : tensor<2xi64>}> : () -> tensor<2xi64>
// CHECK-NEXT:      %17 = "tfl.gather"(%14, %16) <{axis = 0 : i32, batch_dims = 0 : i32}> : (tensor<3xi32>, tensor<2xi64>) -> tensor<2xi32>
// CHECK-NEXT:      %18 = "tfl.pseudo_const"() <{value = dense<1> : tensor<1xi64>}> : () -> tensor<1xi64>

        padding = #vhlo.tensor_v1<dense<[[0, 0], [159, 0], [0, 0]]> : tensor<3x2xi64>>,
        window_dilations = #vhlo.tensor_v1<dense<1> : tensor<3xi64>>,
        window_dimensions = #vhlo.tensor_v1<dense<[1, 160, 1]> : tensor<3xi64>>,
        window_strides = #vhlo.tensor_v1<dense<1> : tensor<3xi64>>}> ({
    ^bb0(%arg23: tensor<f32>, %arg24: tensor<f32>):

// CHECK: %[[arg2_i64:.*]] = "tf.Cast"(%arg2) <{Truncate = false}> : (tensor<2xi32>) -> tensor<2xi64>
// CHECK: %[[slice:.*]] = "tf.Slice"(%arg0, %[[tensor_scatter_update]], %[[arg2_i64]]) : (tensor<?x2xf32>, tensor<2xi64>, tensor<2xi64>) -> tensor<*xf32>
// CHECK: %[[reshape:.*]] = "tf.Reshape"(%[[slice]], %[[cst_1]]) : (tensor<*xf32>, tensor<1xi64>) -> tensor<*xf32>

    %16 = "tf.BroadcastArgs"(%11, %14) : (tensor<1xi64>, tensor<1xi64>) -> tensor<1xi64>
    %17 = "tf.Concat"(%cst_2, %16, %12) : (tensor<i32>, tensor<1xi64>, tensor<2xi64>) -> tensor<3xi64>
    %18 = "tf.Concat"(%cst_2, %16, %15) : (tensor<i32>, tensor<1xi64>, tensor<2xi64>) -> tensor<3xi64>
    %19 = "tf.BroadcastTo"(%4, %17) : (tensor<2x?x?xi8>, tensor<3xi64>) -> tensor<2x?x?xi8>

  %end = "tf.Const"() { value = dense<[5, 10]> : tensor<2xi64> } : () -> tensor<?xi64>
  %strides = "tf.Const"() { value = dense<[2, 3, 4]> : tensor<3xi64> } : () -> tensor<?xi64>

  // expected-error @+1 {{to have the same number of elements}}
  %1 = "tf.StridedSlice"(%input, %begin, %end, %strides) : (tensor<4x8xf32>, tensor<?xi64>, tensor<?xi64>, tensor<?xi64>) -> tensor<?x?xf32>
}

  // CHECK-DAG: %[[cst:.*]] = arith.constant dense<[2, 5, 21]> : tensor<3xi64>
  // CHECK-DAG: %[[cst_1:.*]] = arith.constant dense<[21, 4]> : tensor<2xi64>
  // CHECK: %[[v0:.*]] = "tf.Reshape"(%arg0, %[[cst]]) : (tensor<2x5x7x3xf32>, tensor<3xi64>) -> tensor<2x5x21xf32>
  // CHECK: %[[v1:.*]] = "tf.Reshape"(%arg1, %[[cst_1]]) : (tensor<7x3x4xf32>, tensor<2xi64>) -> tensor<21x4xf32>

  // CHECK-DAG: %[[MATMUL_LHS_SHAPE:.*]] = "tf.Const"() <{value = dense<[4, 5]> : tensor<2xi64>}> : () -> tensor<2xi64>
  // CHECK-DAG: %[[MATMUL_RHS_SHAPE:.*]] = "tf.Const"() <{value = dense<[5, 6]> : tensor<2xi64>}> : () -> tensor<2xi64>

  // CHECK: %[[LHS_1:.*]] = "tf.Reshape"(%arg0, %[[MATMUL_LHS_SHAPE]]) : (tensor<1x4x5xf32>, tensor<2xi64>) -> tensor<4x5xf32>

  %cst0 = mhlo.constant dense<[1, 2, 3, 4]> : tensor<4xi32>
  // CHECK: %[[BROADCAST:.*]] = "mhlo.broadcast_in_dim"(%[[CONST]]) <{broadcast_dimensions = dense<3> : tensor<1xi64>}> : (tensor<4xi32>) -> tensor<1x1x2x4xi32>
  %0 = "mhlo.broadcast_in_dim"(%cst0) <{broadcast_dimensions = dense<3> : tensor<1xi64>}> : (tensor<4xi32>) -> tensor<1x1x2x4xi32>
  // CHECK: %[[MUL:.*]] = mhlo.multiply %[[BROADCAST]], %[[ARG]] : tensor<1x1x2x4xi32>

    // CHECK: "mhlo.pad"(%[[ARG0]], %[[ARG1]])
    // CHECK-SAME-DAG: edge_padding_high = dense<[1, 2]> : tensor<2xi64>
    // CHECK-SAME-DAG: edge_padding_low = dense<[2, 1]> : tensor<2xi64>
    // CHECK-SAME-DAG: interior_padding = dense<[1, 0]> : tensor<2xi64>

    %0 = mhlo.constant dense<[2, 1]> : tensor<2xi32>
    %1 = mhlo.constant dense<[1, 2]> : tensor<2xi32>