parameters: (self)
return_statement: return self._shape
docstring: Returns the `TensorShape` that represents the shape of this tensor.
  
      The shape is computed using shape inference functions that are
      registered for each `Operation` type using `tf.RegisterShape`.
      See [`TensorShape`](../../api_docs/python/framework.md#TensorShape) for more
      details of what a shape represents.
  
      The inferred shape of a tensor is used to provide shape
      information without having to launch the graph in a session. This
      can be used for debugging, and providing early error messages. For
      example:
  
      ```python
      c = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
  
      print(c.get_shape())
      ==> TensorShape([Dimension(2), Dimension(3)])
  
      d = tf.constant([[1.0, 0.0], [0.0, 1.0], [1.0, 0.0], [0.0, 1.0]])
  
      print(d.get_shape())
      ==> TensorShape([Dimension(4), Dimension(2)])
  
      # Raises a ValueError, because `c` and `d` do not have compatible
      # inner dimensions.
      e = tf.matmul(c, d)
  
      f = tf.matmul(c, d, transpose_a=True, transpose_b=True)
  
      print(f.get_shape())
      ==> TensorShape([Dimension(3), Dimension(4)])
      ```
  
      In some cases, the inferred shape may have unknown dimensions. If
      the caller has additional information about the values of these
      dimensions, `Tensor.set_shape()` can be used to augment the
      inferred shape.
  
      Returns:
        A `TensorShape` representing the shape of this tensor.
docstring_summary: Returns the `TensorShape` that represents the shape of this tensor.
docstring_tokens: Returns the TensorShape that represents the shape of this tensor .
function: def get_shape(self):
      """Returns the `TensorShape` that represents the shape of this tensor.
  
      The shape is computed using shape inference functions that are
      registered for each `Operation` type using `tf.RegisterShape`.
      See [`TensorShape`](../../api_docs/python/framework.md#TensorShape) for more
      details of what a shape represents.
  
      The inferred shape of a tensor is used to provide shape
      information without having to launch the graph in a session. This
      can be used for debugging, and providing early error messages. For
      example:
  
      ```python
      c = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
  
      print(c.get_shape())
      ==> TensorShape([Dimension(2), Dimension(3)])
  
      d = tf.constant([[1.0, 0.0], [0.0, 1.0], [1.0, 0.0], [0.0, 1.0]])
  
      print(d.get_shape())
      ==> TensorShape([Dimension(4), Dimension(2)])
  
      # Raises a ValueError, because `c` and `d` do not have compatible
      # inner dimensions.
      e = tf.matmul(c, d)
  
      f = tf.matmul(c, d, transpose_a=True, transpose_b=True)
  
      print(f.get_shape())
      ==> TensorShape([Dimension(3), Dimension(4)])
      ```
  
      In some cases, the inferred shape may have unknown dimensions. If
      the caller has additional information about the values of these
      dimensions, `Tensor.set_shape()` can be used to augment the
      inferred shape.
  
      Returns:
        A `TensorShape` representing the shape of this tensor.
      """
      return self._shape
function_tokens: def get_shape ( self ) : return self . _shape