break;
      }
    }
    TF::TPUReplicatedInputOp input =
        llvm::cast<TF::TPUReplicatedInputOp>(input_ptr);

    // Validate that all users of the TPUReplicatedInput are ReadVariable
    // or AssignVariable ops and check if any are outside the forwards pass.
    bool duplicate_needed = false;
    for (Operation* next_user : input.getOutput().getUsers()) {

  func.return %5, %6 : tensor<*xi32> , tensor<*xi1>
}

// -----

// Tests with input sharding following a ReadVariable op.
// CHECK-LABEL: func @check_sharding_after_read_variable
func.func @check_sharding_after_read_variable(%arg0: tensor<*xi32>, %arg1: tensor<*xi1>) {

    "REAL",
    "COMPLEX_ABS",
    "HASHTABLE",
    "HASHTABLE_FIND",
    "HASHTABLE_IMPORT",
    "HASHTABLE_SIZE",
    "REDUCE_ALL",
    "CONV_3D_TRANSPOSE",
    "VAR_HANDLE",
    "READ_VARIABLE",
    "ASSIGN_VARIABLE",
    "BROADCAST_ARGS",
    "RANDOM_STANDARD_NORMAL",
    "BUCKETIZE",
    "RANDOM_UNIFORM",
    "MULTINOMIAL",
    "GELU",
    "DYNAMIC_UPDATE_SLICE",

        break;
      }
    }
    TF::TPUReplicatedInputOp input =
        llvm::cast<TF::TPUReplicatedInputOp>(input_ptr);

    // Validate that all users of the TPUReplicatedInput are ReadVariable
    // or AssignVariable ops and check if any are outside the forwards pass.
    bool duplicate_needed = false;
    for (Operation* next_user : input.getOutput().getUsers()) {

  // CHECK-SAME: tensor<*xi32>
  func.return %1 : tensor<*xi32>
}

// If multiple types are used across reads and writes, check that the read uses
// the most refined type. The first ReadVariable should refine the type from
// *xi32 to ?xi32 and the assign should refine it further to 4xi32.
// CHECK-LABEL: func @type_refinement_use_refined_type
func.func @type_refinement_use_refined_type() -> tensor<4xi32> {

*   `tf.lite`:

    *   The recommended Android NDK version for building TensorFlow Lite has
        been changed from r18b to r19c.
    *   Supports int64 for mul.
    *   Supports native variable builtin ops - ReadVariable, AssignVariable.
    *   Converter:
        *   Experimental support for variables in TFLite. To enable through
            conversion, users need to set