code: def with_dependencies(dependencies, output_tensor, name=None):
      """Produces the content of `output_tensor` only after `dependencies`.
  
      In some cases, a user may want the output of an operation to be consumed
      externally only after some other dependencies have run first. This function
      returns `output_tensor`, but only after all operations in `dependencies` have
      run. Note that this means that there is no guarantee that `output_tensor` will
      be evaluated after any `dependencies` have run.
  
      See also `tf.tuple` and `tf.group`.
  
      Args:
        dependencies: Iterable of operations to run before this op finishes.
        output_tensor: A `Tensor` or `IndexedSlices` that will be returned.
        name: (Optional) A name for this operation.
  
      Returns:
        output_with_deps: Same as `output_tensor` but with embedded dependencies.
  
      Raises:
        TypeError: if `output_tensor` is not a `Tensor` or `IndexedSlices`.
      """
      if tf.executing_eagerly():
          return output_tensor
      with tf.name_scope(name or "control_dependency") as name:
          with tf.control_dependencies(
              d for d in dependencies if d is not None
          ):
              output_tensor = tf.convert_to_tensor(output_tensor)
              if isinstance(output_tensor, tf.Tensor):
                  return tf.identity(output_tensor, name=name)
              else:
                  return tf.IndexedSlices(
                      tf.identity(output_tensor.values, name=name),
                      output_tensor.indices,
                      output_tensor.dense_shape,
                  )
  
imports: import functools
  import hashlib
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow.python.util import tf_inspect
label_desc: Use image node_id 41 for calling a global function with example usage: with_dependencies(dependencies, output_tensor, name) and returns: output_tensor, tf, tf