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def run(self, fetches, feed_dict=None, options=None, run_metadata=None):  """Runs operations and evaluates tensors in `fetches`.  功能：This method runs one "step" of TensorFlow computation, by running the necessary graph fragment（碎片） to execute every `Operation` and evaluate every `Tensor` in `fetches`, substituting(取代) the values in `feed_dict` for the corresponding input values.  fetches数据类型：The `fetches` argument may be a single graph element, or an arbitrarily nested list, tuple, namedtuple, dict, or OrderedDict containing graph elements at its leaves.  A graph element can be one of the following types:

什么时候需要使用fetches?  当有feed_dict需求时

fetches格式是什么，输出格式就是什么1、fetches是字典，值为z，tf会获取z的值，返回字典

import tensorflow as tfx_input = tf.placeholder(dtype=tf.float32,name='x',shape=[None,])y_input = tf.placeholder(dtype=tf.float32,name='y',shape=[None,])z = x_input+y_inputwith tf.Session() as sess:    print(sess.run(fetches={'a':z},feed_dict={'x:0':[1],'y:0':[2]}))    print(sess.run(fetches={'a':z},feed_dict={x_input:[1],y_input:[2]}))

{'a': array([3.], dtype=float32)} {'a': array([3.], dtype=float32)}2、fetches是列表，值为z，tf会获取z的值，返回列表

x_input = tf.placeholder(dtype=tf.float32,name='x',shape=[None,])y_input = tf.placeholder(dtype=tf.float32,name='y',shape=[None,])z = x_input+y_inputwith tf.Session() as sess:    print(sess.run(fetches=[z,tf.constant([1,2,3])],feed_dict={'x:0':[1],'y:0':[2]}))    print(sess.run(fetches=[tf.constant('afaf'),z],feed_dict={x_input:[1],y_input:[2]}))

[array([3.], dtype=float32), array([1, 2, 3])][b'afaf', array([3.], dtype=float32)]  * An @{tf.Operation}.    The corresponding fetched value will be `None`.  * A @{tf.Tensor}.    The corresponding fetched value will be a numpy ndarray containing the    value of that tensor.  * A @{tf.SparseTensor}.    The corresponding fetched value will be a    @{tf.SparseTensorValue}    containing the value of that sparse tensor.  * A `get_tensor_handle` op.  The corresponding fetched value will be a    numpy ndarray containing the handle of that tensor.  * A `string` which is the name of a tensor or operation in the graph.  The value returned by `run()` has the same shape as the `fetches` argument,where the leaves are replaced by the corresponding values returned by TensorFlow.  Example:  ```python     a = tf.constant([10, 20])     b = tf.constant([1.0, 2.0])     # 'fetches' can be a singleton     v = session.run(a)     # v is the numpy array [10, 20]     # 'fetches' can be a list.     v = session.run([a, b])     # v is a Python list with 2 numpy arrays: the 1-D array [10, 20] and the     # 1-D array [1.0, 2.0]     # 'fetches' can be arbitrary lists, tuples, namedtuple, dicts:     MyData = collections.namedtuple('MyData', ['a', 'b'])     v = session.run({'k1': MyData(a, b), 'k2': [b, a]})     # v is a dict with     # v['k1'] is a MyData namedtuple with 'a' (the numpy array [10, 20]) and     # 'b' (the numpy array [1.0, 2.0])     # v['k2'] is a list with the numpy array [1.0, 2.0] and the numpy array     # [10, 20].  ```  feed_dict数据类型：The optional `feed_dict` argument allows the caller to override the value of tensors in the graph. Each key in `feed_dict` can be one of the following types:  * If the key is a @{tf.Tensor}, the value may be a Python scalar, string, list, or numpy ndarray that can be converted to the same `dtype` as that tensor. Additionally, if the key is a @{tf.placeholder}, the shape of the value will be checked for compatibility with the placeholder.1、tf.Tensor,字典键可以是变量名，也可以是变量属性

import tensorflow as tfx_input = tf.placeholder(dtype=tf.float32,name='x',shape=[None,])y_input = tf.placeholder(dtype=tf.float32,name='y',shape=[None,])z = x_input+y_inputwith tf.Session() as sess:    print(sess.run(fetches={'a':z},feed_dict={'x:0':[1],'y:0':[2]}))    print(sess.run(fetches={'a':z},feed_dict={x_input:[1],y_input:[2]}))

* If the key is a @{tf.SparseTensor},the value should be a @{tf.SparseTensorValue}.  * If the key is a nested tuple of `Tensor`s or `SparseTensor`s, the value should be a nested tuple with the same structure that maps to their     corresponding values as above.  Each value in `feed_dict` must be convertible to a numpy array of the dtype of the corresponding key.  The optional `options` argument expects a [`RunOptions`] proto. The options allow controlling the behavior of this particular step (e.g. turning tracing on).  The optional `run_metadata` argument expects a [`RunMetadata`] proto. When  appropriate, the non-Tensor output of this step will be collected there. For  example, when users turn on tracing in `options`, the profiled info will be  collected into this argument and passed back.  Args:    fetches: A single graph element, a list of graph elements,      or a dictionary whose values are graph elements or lists of graph      elements (described above).    feed_dict: A dictionary that maps graph elements to values      (described above).    options: A [`RunOptions`] protocol buffer    run_metadata: A [`RunMetadata`] protocol buffer  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 (described above).  Raises:    RuntimeError: If this `Session` is in an invalid state (e.g. has been      closed).    TypeError: If `fetches` or `feed_dict` keys are of an inappropriate type.    ValueError: If `fetches` or `feed_dict` keys are invalid or refer to a      `Tensor` that doesn't exist.  """

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