code: def embedding(
      weights: Union[tf.Tensor, tf.Variable],
      indices: Union[tf.Tensor, tf.Variable],
      /,
      *,
      max_norm: Optional[float] = None,
      out: Optional[Union[tf.Tensor, tf.Variable]] = None,
  ) -> Union[tf.Tensor, tf.Variable]:
      ivy.utils.assertions.check_equal(
          len(weights.shape),
          2,
          message="weights must be 2-d",
          as_array=False,
      )
      return tf.nn.embedding_lookup(weights, indices, max_norm=max_norm)
  
imports: import math
  from typing import Union, Optional, Tuple, List, Literal, Sequence, Callable
  import tensorflow
  from ivy.func_wrapper import inputs_to_ivy_arrays, output_to_native_arrays, with_unsupported_dtypes, with_supported_dtypes, with_supported_device_and_dtypes
  from ..None import backend_version
  import ivy
  from ivy.functional.ivy.layers import _handle_padding, _get_num_padded_values, _validate_max_pool_params, _depth_max_pooling_helper
  from ivy.functional.ivy.experimental.layers import _padding_ceil_mode, _get_size
label_desc: Use image node_id 20 for calling a global function with example usage: embedding() and returns: tf