code: def _get_embedding_args(draw):
      num_embeddings = draw(st.integers(min_value=1, max_value=10))
      embedding_dim = draw(st.integers(min_value=1, max_value=10))
      dtype_indices, indices = draw(
          helpers.dtype_and_values(
              available_dtypes=["int32", "int64"],
              min_num_dims=2,
              min_dim_size=1,
              min_value=0,
              max_value=num_embeddings - 1,
          ).filter(lambda x: x[1][0].shape[-1] == embedding_dim)
      )
      padding_idx = draw(
          st.integers(min_value=0, max_value=num_embeddings - 1)
      )
      max_norm = draw(
          st.one_of(st.none(), st.floats(min_value=1, max_value=5))
      )
  
      return (
          num_embeddings,
          embedding_dim,
          dtype_indices,
          indices,
          padding_idx,
          max_norm,
      )
  
imports: import numpy
  from hypothesis import assume
  from hypothesis import strategies
  import ivy
  import ivy_tests.test_ivy.helpers
  from ivy.data_classes.container import Container
  from ivy.functional.ivy.gradients import _variable
  from ivy.functional.ivy.layers import _deconv_length
  from ivy_tests.test_ivy.helpers import handle_method
  from ivy_tests.test_ivy.helpers.assertions import assert_same_type_and_shape
  from ivy_tests.test_ivy.test_functional.test_experimental.test_nn import test_layers
  from ivy_tests.test_ivy.test_functional.test_experimental.test_nn.test_layers import _valid_dct
label_desc: Use image node_id 2 for calling a global function with example usage: _get_embedding_args(draw) and returns: num_embeddings,  embedding_dim,  dtype_indices,  indices,  padding_idx,  max_norm