code: def _broadcast_to_uniform_shape(
      rt_input, shape, broadcast_inner_dimensions
  ):
      """Broadcasts rt_input to the uniform shape `shape`."""
      if isinstance(rt_input, ragged_tensor.RaggedTensor):
          raise ValueError(
              "Incompatible with shape: ragged rank mismatch"
          )
      if broadcast_inner_dimensions:
          return array_ops.broadcast_to(rt_input, shape.inner_dim_sizes)
      else:
          return rt_input
  
imports: from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.framework import tensor_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import array_ops_stack
  from tensorflow.python.ops import control_flow_assert
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops.ragged import ragged_array_ops
  from tensorflow.python.ops.ragged import ragged_config
  from tensorflow.python.ops.ragged import ragged_tensor
  from tensorflow.python.ops.ragged import ragged_util
label_desc: Use image node_id 3 for calling a global function with example usage: _broadcast_to_uniform_shape(rt_input, shape, broadcast_inner_dimensions) and returns: array_ops, rt_input