code: def embedding_lookup(
      params,
      ids: ragged_tensor.Ragged,
      partition_strategy="mod",
      name=None,
      validate_indices=True,  # pylint: disable=unused-argument
      max_norm=None,
  ):
      """Look up the ragged ids in a list of embedding tensors.
  
      Args:
        params: A tensor representing the complete embedding tensor having the shape
          [e1, ...eM]
        ragged_ids: A 'RaggedTensor' with type 'int32' or 'int64' containing the ids
          to be looked up in 'params' of shape [r0, ..rN]. Values must be in the
          range '[0, params.shape[0]]'.
        partition_strategy: A string specifying the partitioning strategy.
        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 ragged tensor of shape [r0, r1, ...rN, e1, ...eM].
  
      Raises:
        ValueError: When params is empty or the type of the ids is not int32 or
          int64.
      """
      if params is None:
          raise ValueError("params must be specified.")
      if isinstance(params, (list, tuple)) and not params:
          raise ValueError("params should not be empty.")
      if ids.dtype != dtypes.int32 and ids.dtype != dtypes.int64:
          raise ValueError(
              "The values contained by the inputs have type "
              f"{str(ids.dtype)}"
              " and cannot be processed. All values"
              " should be indices, either of type `int32` or `int64`."
          )
  
      with ops.name_scope(name, "embedding_lookup_ragged") as name:
          looked_up_ragged = ragged_functional_ops.map_flat_values(
              embedding_ops.embedding_lookup,
              params=params,
              ids=ids,
              partition_strategy=partition_strategy,
              max_norm=max_norm,
          )
  
          return looked_up_ragged
  
file_path: tensorflow/tensorflow/python/ops/ragged/ragged_embedding_ops.py
imports: from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import array_ops_stack
  from tensorflow.python.ops import embedding_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import resource_variable_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.ops.ragged import ragged_array_ops
  from tensorflow.python.ops.ragged import ragged_functional_ops
  from tensorflow.python.ops.ragged import ragged_tensor
  from tensorflow.python.util import dispatch
label_desc: Use image node_id 1 for calling a global function with example usage: embedding_lookup(params, ids, partition_strategy, name, validate_indices, max_norm) and returns: looked_up_ragged