code: def _interleave_and_merge(
      tensors,
      pre_merge_dynamic_shape_tensor,
      keep_rank,
      is_sparse=False,
  ):
      """Concatenates a list of tensors in an interleaved manner.
  
      For example, suppose `pre_merge_dynamic_shape_tensor` is `[B, D_1, D_2, ...,
      D_d]`, where `B` is the batch size. For sparse tensors (i.e., when `is_sparse`
      is `True`), the interleaving is obtained by first expanding the dimension of
      each tensor on axis 1 and then concatenating the tensors along axis 1. For
      dense tensors (i.e., when `is_sparse` is `False`), the interleaving is
      obtained by stacking tensors along axis 1. In both cases, the resulting shape
      of the interleaved tensor will be `[B, N, D_1, D_2, ...D_d]`, where `N` is the
      number of entries in `tensors`. If `keep_rank` is `True`, the original rank
      and the original sizes of all dimensions except for the first dimension are
      retained; the interleaved tensor is reshaped to `[(BxN), D_1, D_2, ...D_d]`.
      If `keep_rank` is `False`, then the interleaved tensor is returned as is.
  
      Args:
        tensors: List of tensors with compatible shapes. Either all of them should
          be dense or all of them should be sparse.
        pre_merge_dynamic_shape_tensor: A 1-D tensor representing the dynamic shape
          of each tensor in `tensors`.
        keep_rank: Boolean indicating whether to retain the rank from the input or
          to introduce a new dimension (axis 1).
        is_sparse: (optional) Boolean indicating if entries in `tensors` are sparse
          or not.
  
      Returns:
        An interleaved concatenation of `tensors`. If `keep_rank` is `True`, the
        rank is the same compared to entries in `tensors`, but the size of its first
        dimension is multiplied by a factor of the number of entries in `tensors`.
        Otherwise, the result will have rank one more than the rank of `tensors`,
        where the size of the new dimension (axis 1) is equal to the
        number of entries in `tensors`.  Note that if `tensors` is empty, then a
        value of `None` is returned.
  
      Raises:
        ValueError: If any entry in `tensors` has an incompatible shape.
      """
      if not tensors:
          return None
      # The first dimension in the resulting interleaved tensor will be inferred.
      merged_shape = tf.concat(
          [tf.constant([-1]), pre_merge_dynamic_shape_tensor[1:]],
          axis=0,
      )
  
      if is_sparse:
          # This is the equivalent of tf.stack() for sparse tensors.
          concatenated_tensors = tf.sparse.concat(
              axis=1,
              sp_inputs=[tf.sparse.expand_dims(t, 1) for t in tensors],
          )
          return (
              tf.sparse.reshape(
                  concatenated_tensors, shape=merged_shape
              )
              if keep_rank
              else concatenated_tensors
          )
      else:
          stacked_tensors = tf.stack(tensors, axis=1)
          return (
              tf.reshape(stacked_tensors, shape=merged_shape)
              if keep_rank
              else stacked_tensors
          )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import neural_structured_learning.configs
  import six
  import tensorflow
label_desc: Use image node_id 13 for calling a global function with example usage: _interleave_and_merge(tensors, pre_merge_dynamic_shape_tensor, keep_rank, is_sparse) and returns: None, unknown, unknown