code: def stack(
      values: typing.List[ragged_tensor.RaggedOrDense],
      axis=0,
      name=None,
  ):
      """Stacks a list of rank-`R` tensors into one rank-`(R+1)` `RaggedTensor`.
  
      Given a list of tensors or ragged tensors with the same rank `R`
      (`R >= axis`), returns a rank-`R+1` `RaggedTensor` `result` such that
      `result[i0...iaxis]` is `[value[i0...iaxis] for value in values]`.
  
      #### Examples:
  
      >>> # Stacking two ragged tensors.
      >>> t1 = tf.ragged.constant([[1, 2], [3, 4, 5]])
      >>> t2 = tf.ragged.constant([[6], [7, 8, 9]])
      >>> tf.ragged.stack([t1, t2], axis=0)
      <tf.RaggedTensor [[[1, 2], [3, 4, 5]], [[6], [7, 8, 9]]]>
      >>> tf.ragged.stack([t1, t2], axis=1)
      <tf.RaggedTensor [[[1, 2], [6]], [[3, 4, 5], [7, 8, 9]]]>
  
      >>> # Stacking two dense tensors with different sizes.
      >>> t3 = tf.constant([[1, 2, 3], [4, 5, 6]])
      >>> t4 = tf.constant([[5], [6], [7]])
      >>> tf.ragged.stack([t3, t4], axis=0)
      <tf.RaggedTensor [[[1, 2, 3], [4, 5, 6]], [[5], [6], [7]]]>
  
      Args:
        values: A list of `tf.Tensor` or `tf.RaggedTensor`.  May not be empty. All
          `values` must have the same rank and the same dtype; but unlike
          `tf.stack`, they can have arbitrary dimension sizes.
        axis: A python integer, indicating the dimension along which to stack.
          (Note: Unlike `tf.stack`, the `axis` parameter must be statically known.)
          Negative values are supported only if the rank of at least one
          `values` value is statically known.
        name: A name prefix for the returned tensor (optional).
  
      Returns:
        A `RaggedTensor` with rank `R+1` (if `R>0`).
        If `R==0`, then the result will be returned as a 1D `Tensor`, since
        `RaggedTensor` can only be used when `rank>1`.
        `result.ragged_rank=1+max(axis, max(rt.ragged_rank for rt in values]))`.
  
      Raises:
        ValueError: If `values` is empty, if `axis` is out of bounds or if
          the input tensors have different ranks.
      """
      if not isinstance(values, (list, tuple)):
          values = [values]
      with ops.name_scope(name, "RaggedConcat", values):
          return _ragged_stack_concat_helper(
              values, axis, stack_values=True
          )
  
file_path: tensorflow/tensorflow/python/ops/ragged/ragged_concat_ops.py
imports: import typing
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import array_ops_stack
  from tensorflow.python.ops import check_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops.ragged import ragged_gather_ops
  from tensorflow.python.ops.ragged import ragged_tensor
  from tensorflow.python.ops.ragged import ragged_util
  from tensorflow.python.util import dispatch
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 2 for calling a global function with example usage: stack(values, axis, name) and returns: _ragged_stack_concat_helper