code: def embedding(
      weights: torch.Tensor,
      indices: torch.Tensor,
      /,
      *,
      max_norm: Optional[int] = None,
      out: Optional[torch.Tensor] = None,
  ) -> torch.Tensor:
      ivy.utils.assertions.check_equal(
          len(weights.shape),
          2,
          message="weights must be 2-d",
          as_array=False,
      )
      return torch.nn.functional.embedding(
          indices, weights, max_norm=max_norm
      )
  
imports: from typing import Optional, Union, Tuple, List, Literal, Sequence, Callable
  import torch
  import math
  import ivy
  from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
  from .None import backend_version
  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, _broadcast_pooling_helper
label_desc: Use image node_id 19 for calling a global function with example usage: embedding() and returns: torch