parameters: (self, handle, fetches, feed_dict=None)
return_statement: return self._run(handle, fetches, feed_dict, None, None)
docstring: Continues the execution with more feeds and fetches.
  
      This is EXPERIMENTAL and subject to change.
  
      To use partial execution, a user first calls `partial_run_setup()` and
      then a sequence of `partial_run()`. `partial_run_setup` specifies the
      list of feeds and fetches that will be used in the subsequent
      `partial_run` calls.
  
      The optional `feed_dict` argument allows the caller to override
      the value of tensors in the graph. See run() for more information.
  
      Below is a simple example:
  
      ```python
      a = array_ops.placeholder(dtypes.float32, shape=[])
      b = array_ops.placeholder(dtypes.float32, shape=[])
      c = array_ops.placeholder(dtypes.float32, shape=[])
      r1 = math_ops.add(a, b)
      r2 = math_ops.multiply(r1, c)
  
      h = sess.partial_run_setup([r1, r2], [a, b, c])
      res = sess.partial_run(h, r1, feed_dict={a: 1, b: 2})
      res = sess.partial_run(h, r2, feed_dict={c: res})
      ```
  
      Args:
        handle: A handle for a sequence of partial runs.
        fetches: A single graph element, a list of graph elements,
          or a dictionary whose values are graph elements or lists of graph
          elements (see documentation for `run`).
        feed_dict: A dictionary that maps graph elements to values
          (described above).
  
      Returns:
        Either a single value if `fetches` is a single graph element, or
        a list of values if `fetches` is a list, or a dictionary with the
        same keys as `fetches` if that is a dictionary
        (see documentation for `run`).
  
      Raises:
        tf.errors.OpError: Or one of its subclasses on error.
docstring_summary: Continues the execution with more feeds and fetches.
docstring_tokens: Continues the execution with more feeds and fetches .
function: def partial_run(self, handle, fetches, feed_dict=None):
      """Continues the execution with more feeds and fetches.
  
      This is EXPERIMENTAL and subject to change.
  
      To use partial execution, a user first calls `partial_run_setup()` and
      then a sequence of `partial_run()`. `partial_run_setup` specifies the
      list of feeds and fetches that will be used in the subsequent
      `partial_run` calls.
  
      The optional `feed_dict` argument allows the caller to override
      the value of tensors in the graph. See run() for more information.
  
      Below is a simple example:
  
      ```python
      a = array_ops.placeholder(dtypes.float32, shape=[])
      b = array_ops.placeholder(dtypes.float32, shape=[])
      c = array_ops.placeholder(dtypes.float32, shape=[])
      r1 = math_ops.add(a, b)
      r2 = math_ops.multiply(r1, c)
  
      h = sess.partial_run_setup([r1, r2], [a, b, c])
      res = sess.partial_run(h, r1, feed_dict={a: 1, b: 2})
      res = sess.partial_run(h, r2, feed_dict={c: res})
      ```
  
      Args:
        handle: A handle for a sequence of partial runs.
        fetches: A single graph element, a list of graph elements,
          or a dictionary whose values are graph elements or lists of graph
          elements (see documentation for `run`).
        feed_dict: A dictionary that maps graph elements to values
          (described above).
  
      Returns:
        Either a single value if `fetches` is a single graph element, or
        a list of values if `fetches` is a list, or a dictionary with the
        same keys as `fetches` if that is a dictionary
        (see documentation for `run`).
  
      Raises:
        tf.errors.OpError: Or one of its subclasses on error.
      """
      # TODO(touts): Support feeding and fetching the same tensor.
      return self._run(handle, fetches, feed_dict, None, None)
function_tokens: def partial_run ( self , handle , fetches , feed_dict = None ) : # TODO(touts): Support feeding and fetching the same tensor. return self . _run ( handle , fetches , feed_dict , None , None )