Class Tensor

tensorflow/python/framework/ops.py:288–851

Represents one of the outputs of an `Operation`. A `Tensor` is a symbolic handle to one of the outputs of an `Operation`. It does not hold the values of that operation's output, but instead provides a means of computing those values in a TensorFlow `tf.compat.v1.Session`. This class has

@tf_export("Tensor")
class Tensor(_TensorLike):
  """Represents one of the outputs of an `Operation`.

  A `Tensor` is a symbolic handle to one of the outputs of an
  `Operation`. It does not hold the values of that operation's output,
  but instead provides a means of computing those values in a
  TensorFlow `tf.compat.v1.Session`.

  This class has two primary purposes:

  1. A `Tensor` can be passed as an input to another `Operation`.
     This builds a dataflow connection between operations, which
     enables TensorFlow to execute an entire `Graph` that represents a
     large, multi-step computation.

  2. After the graph has been launched in a session, the value of the
     `Tensor` can be computed by passing it to
     `tf.Session.run`.
     `t.eval()` is a shortcut for calling
     `tf.compat.v1.get_default_session().run(t)`.

  In the following example, `c`, `d`, and `e` are symbolic `Tensor`
  objects, whereas `result` is a numpy array that stores a concrete
  value:

  ```python
  # Build a dataflow graph.
  c = tf.constant([[1.0, 2.0], [3.0, 4.0]])
  d = tf.constant([[1.0, 1.0], [0.0, 1.0]])
  e = tf.matmul(c, d)

  # Construct a `Session` to execute the graph.
  sess = tf.compat.v1.Session()

  # Execute the graph and store the value that `e` represents in `result`.
  result = sess.run(e)
  ```
  """

  # List of Python operators that we allow to override.
  OVERLOADABLE_OPERATORS = {
      # Binary.
      "__add__",
      "__radd__",
      "__sub__",
      "__rsub__",
      "__mul__",
      "__rmul__",
      "__div__",
      "__rdiv__",
      "__truediv__",
      "__rtruediv__",
      "__floordiv__",
      "__rfloordiv__",
      "__mod__",
      "__rmod__",
      "__lt__",
      "__le__",