code: def sparse_tensor_to_dense_with_shape(
      x: tf.SparseTensor,
      shape: Union[tf.TensorShape, Iterable[int]],
      default_value: Union[tf.Tensor, int, float, str] = 0,
  ) -> tf.Tensor:
      """Converts a `SparseTensor` into a dense tensor and sets its shape.
  
      Args:
        x: A `SparseTensor`.
        shape: The desired shape of the densified `Tensor`.
        default_value: (Optional) Value to set for indices not specified. Defaults
          to zero.
  
      Returns:
        A `Tensor` with the desired shape.
  
      Raises:
        ValueError: If input is not a `SparseTensor`.
      """
      if not isinstance(x, tf.SparseTensor):
          raise ValueError("input must be a SparseTensor")
      new_dense_shape = [
          x.dense_shape[i] if size is None else size
          for i, size in enumerate(shape)
      ]
      dense = tf.raw_ops.SparseToDense(
          sparse_indices=x.indices,
          output_shape=new_dense_shape,
          sparse_values=x.values,
          default_value=default_value,
      )
      dense.set_shape(shape)
      return dense
  
imports: import os
  from typing import Any, Callable, Iterable, Optional, Tuple, Union
  import tensorflow
  from tensorflow_transform import analyzers
  from tensorflow_transform import common
  from tensorflow_transform import common_types
  from tensorflow_transform import gaussianization
  from tensorflow_transform import schema_inference
  from tensorflow_transform import tf_utils
  from tfx_bsl.types import tfx_namedtuple
label_desc: Use image node_id 3 for calling a global function with example usage: sparse_tensor_to_dense_with_shape(x, shape, default_value) and returns: dense