code: def embedding_lookup_v2(params, ids, max_norm=None, name=None):
      """Looks up embeddings for the given `ids` from a list of tensors.
  
      This function is used to perform parallel lookups on the list of tensors in
      `params`.  It is a generalization of `tf.gather`, where `params` is
      interpreted as a partitioning of a large embedding tensor.
  
      If `len(params) > 1`, each element `id` of `ids` is partitioned between the
      elements of `params` according to the "div" partition strategy, which means we
      assign ids to partitions in a contiguous manner. For instance, 13 ids are
      split across 5 partitions as:
      `[[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10], [11, 12]]`.
  
      If the id space does not evenly divide the number of partitions, each of the
      first `(max_id + 1) % len(params)` partitions will be assigned one more id.
  
      The results of the lookup are concatenated into a dense
      tensor. The returned tensor has shape `shape(ids) + shape(params)[1:]`.
  
      Args:
        params: A single tensor representing the complete embedding tensor, or a
          list of tensors all of same shape except for the first dimension,
          representing sharded embedding tensors following "div" partition strategy.
        ids: A `Tensor` with type `int32` or `int64` containing the ids to be looked
          up in `params`.
        max_norm: If not `None`, each embedding is clipped if its l2-norm is larger
          than this value.
        name: A name for the operation (optional).
  
      Returns:
        A `Tensor` with the same type as the tensors in `params`.
  
        For instance, if `params` is a 5x2 matrix:
  
        ```python
        [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]]
        ```
  
        or a list of matrices:
  
        ```python
        params[0]: [[1, 2], [3, 4]]
        params[1]: [[5, 6], [7, 8]]
        params[2]: [[9, 10]]
        ```
  
        and `ids` is:
  
        ```python
        [0, 3, 4]
        ```
  
        The output will be a 3x2 matrix:
  
        ```python
        [[1, 2], [7, 8], [9, 10]]
        ```
  
      Raises:
        ValueError: If `params` is empty.
      """
      return embedding_lookup(
          params, ids, "div", name, max_norm=max_norm
      )
  
imports: from tensorflow.python.compat import compat
  from tensorflow.python.framework import composite_tensor
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import indexed_slices
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import sparse_tensor
  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 clip_ops
  from tensorflow.python.ops import data_flow_grad
  from tensorflow.python.ops import data_flow_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import resource_variable_ops
  from tensorflow.python.ops import sparse_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.types import core
  from tensorflow.python.util import dispatch
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 5 for calling a global function with example usage: embedding_lookup_v2(params, ids, max_norm, name) and returns: embedding_lookup