class_docstr: A specialized KerasTensor representation for `tf.sparse.SparseTensor`s.
  
  Specifically, it specializes the conversion to a placeholder in order
  to maintain dense shape information.
class_docstr_tok: ["A","specialized","KerasTensor","representation","for","`","tf.sparse.SparseTensor","`","s",".","Specifically",",","it","specializes","the","conversion","to","a","placeholder","in","order","to","maintain","dense","shape","information","."]
code: def _to_placeholder(self):
      spec = self.type_spec
  
      # nest.map_structure loses dense shape information for sparse tensors.
      # So, we special-case sparse placeholder creation.
      # This only preserves shape information for top-level sparse tensors;
      # not for sparse tensors that are nested inside another composite
      # tensor.
      return array_ops.sparse_placeholder(
          dtype=spec.dtype, shape=spec.shape
      )
  
imports: from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import sparse_tensor
  from tensorflow.python.framework import tensor
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.framework import type_spec
  from tensorflow.python.keras.utils import object_identity
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops.ragged import ragged_operators
  from tensorflow.python.ops.ragged import ragged_tensor
  from tensorflow.python.util import nest
label_desc: Use image node_id 1 for calling the SparseKerasTensor obj's underlying member method code with example usage: obj._to_placeholder() and returns: array_ops