code: def sparse_to_dense(
      sparse_indices, output_shape, sparse_values, default_value=0
  ):
      """Converts a sparse representation into a dense tensor.
  
      Parameters
      ----------
      sparse_indices : relay.Expr
          A 0-D, 1-D, or 2-D tensor of integers containing location of sparse values.
  
      output_shape : relay.Expr
          A list of integers. Shape of the dense output tensor.
  
      sparse_values : relay.Expr
          A 0-D or 1-D tensor containing the sparse values for the sparse indices.
  
      default_value : relay.Expr, optional
          A 0-D tensor containing the default value for the remaining locations.
          Defaults to 0.
  
      Returns
      -------
      result : relay.Expr
          Dense tensor of shape output_shape. Has the same type as sparse_values.
  
      Examples
      -------
      .. code-block:: python
  
          relay.sparse_to_dense([[0, 0], [1, 1]], [2, 2], [3, 3], 0) =
              [[3, 0],
               [0, 3]]
      """
      if default_value == 0:
          default_value = const(0)
      if isinstance(output_shape, Expr):
          return _dyn_make.sparse_to_dense(
              sparse_indices, output_shape, sparse_values, default_value
          )
      return _make.sparse_to_dense(
          sparse_indices, output_shape, sparse_values, default_value
      )
  
imports: from ...tir import expr
  from ..expr import Constant, Expr, Tuple, TupleWrapper, const
  from .None import _make
  from .dyn import _make
  from .tensor import shape_of
label_desc: Use image node_id 38 for calling a global function with example usage: sparse_to_dense(sparse_indices, output_shape, sparse_values, default_value) and returns: _make, _dyn_make