// Note that we are using the same `result_type` for both the
    // `TensorListStackOp` and `ConcatOp`, since the first dimension of the
    // shape specified by `result_type` is -1.
    auto stacked_extended_part = rewriter->create<TF::TensorListStackOp>(
        loc, result_type, extended_part,
        /*element_shape=*/CreateI32SplatConst(loc, rewriter, {}, -1),

    Type operand_type = std::get<0>(entry).getType();
    Value result = std::get<1>(entry);
    TensorType result_type = mlir::cast<TensorType>(result.getType());
    Type inferred_type = TypeMeet(result_type, operand_type);
    if (result_type == inferred_type) continue;

    if (!UpdateTypeAndInsertIncompatibleUseCasts(inferred_type, result))
      continue;
    changed = true;
  }

    if (x_type != result_type) {
      x = builder.create<BroadcastInDimOp>(loc, result_type, x, broadcast_dims);
    }
    if (y_type != result_type) {
      y = builder.create<BroadcastInDimOp>(loc, result_type, y,
                                           larger_broadcast_dims);
    }
  } else {
    if (x_type != result_type) {
      x = builder.create<BroadcastInDimOp>(loc, result_type, x,

      os << operand_type;
    }
  }
  os << ") -> (";
  if (!inst->getResultTypes().empty()) {
    bool first = true;
    for (Type result_type : inst->getResultTypes()) {
      os << (!first ? ", " : "");
      first = false;
      os << result_type;
    }
  }
  os << ")";
  // Print out attributes except for large elementsattributes (which should

    *   Add ellipsis (...) support for `tf.einsum()`.
    *   Add expand_composites argument to all `nest.*` methods.
    *   Added `strings.byte_split`.
    *   Add a new "result_type" parameter to `tf.strings.split`.
    *   Add name argument to `tf.string_split` and `tf.strings_split`.
    *   Extend `tf.strings.split` to support inputs with any rank.
    *   Added `tf.random.binomial`.