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+Represents the shape of a `Tensor`.
+
+A `TensorShape` represents a possibly-partial shape specification for a
+`Tensor`. It may be one of the following:
+
+* *Fully-known shape:* has a known number of dimensions and a known size
+  for each dimension.
+* *Partially-known shape:* has a known number of dimensions, and an unknown
+  size for one or more dimension.
+* *Unknown shape:* has an unknown number of dimensions, and an unknown
+  size in all dimensions.
+
+If a tensor is produced by an operation of type `"Foo"`, its shape
+may be inferred if there is a registered shape function for
+`"Foo"`. See [`tf.RegisterShape()`](../../api_docs/python/framework.md#RegisterShape)
+for details of shape
+functions and how to register them. Alternatively, the shape may be set
+explicitly using [`Tensor.set_shape()`](../../api_docs/python/framework.md#Tensor.set_shape).
+
+- - -
+
+#### `tf.TensorShape.merge_with(other)` {#TensorShape.merge_with}
+
+Returns a `TensorShape` combining the information in `self` and `other`.
+
+The dimensions in `self` and `other` are merged elementwise,
+according to the rules defined for `Dimension.merge_with()`.
+
+##### Args:
+
+
+*  <b>`other`</b>: Another `TensorShape`.
+
+##### Returns:
+
+  A `TensorShape` containing the combined information of `self` and
+  `other`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `self` and `other` are not compatible.
+
+
+- - -
+
+#### `tf.TensorShape.concatenate(other)` {#TensorShape.concatenate}
+
+Returns the concatenation of the dimension in `self` and `other`.
+
+*N.B.* If either `self` or `other` is completely unknown,
+concatenation will discard information about the other shape. In
+future, we might support concatenation that preserves this
+information for use with slicing.
+
+##### Args:
+
+
+*  <b>`other`</b>: Another `TensorShape`.
+
+##### Returns:
+
+  A `TensorShape` whose dimensions are the concatenation of the
+  dimensions in `self` and `other`.
+
+
+
+- - -
+
+#### `tf.TensorShape.ndims` {#TensorShape.ndims}
+
+Returns the rank of this shape, or None if it is unspecified.
+
+
+- - -
+
+#### `tf.TensorShape.dims` {#TensorShape.dims}
+
+Returns a list of Dimensions, or None if the shape is unspecified.
+
+
+- - -
+
+#### `tf.TensorShape.as_list()` {#TensorShape.as_list}
+
+Returns a list of integers or None for each dimension.
+
+##### Returns:
+
+  A list of integers or None for each dimension.
+
+
+- - -
+
+#### `tf.TensorShape.as_proto()` {#TensorShape.as_proto}
+
+Returns this shape as a `TensorShapeProto`.
+
+
+- - -
+
+#### `tf.TensorShape.is_compatible_with(other)` {#TensorShape.is_compatible_with}
+
+Returns True iff `self` is compatible with `other`.
+
+Two possibly-partially-defined shapes are compatible if there
+exists a fully-defined shape that both shapes can represent. Thus,
+compatibility allows the shape inference code to reason about
+partially-defined shapes. For example:
+
+* TensorShape(None) is compatible with all shapes.
+
+* TensorShape([None, None]) is compatible with all two-dimensional
+  shapes, such as TensorShape([32, 784]), and also TensorShape(None). It is
+  not compatible with, for example, TensorShape([None]) or
+  TensorShape([None, None, None]).
+
+* TensorShape([32, None]) is compatible with all two-dimensional shapes
+  with size 32 in the 0th dimension, and also TensorShape([None, None])
+  and TensorShape(None). It is not compatible with, for example,
+  TensorShape([32]), TensorShape([32, None, 1]) or TensorShape([64, None]).
+
+* TensorShape([32, 784]) is compatible with itself, and also
+  TensorShape([32, None]), TensorShape([None, 784]), TensorShape([None,
+  None]) and TensorShape(None). It is not compatible with, for example,
+  TensorShape([32, 1, 784]) or TensorShape([None]).
+
+The compatibility relation is reflexive and symmetric, but not
+transitive. For example, TensorShape([32, 784]) is compatible with
+TensorShape(None), and TensorShape(None) is compatible with
+TensorShape([4, 4]), but TensorShape([32, 784]) is not compatible with
+TensorShape([4, 4]).
+
+##### Args:
+
+
+*  <b>`other`</b>: Another TensorShape.
+
+##### Returns:
+
+  True iff `self` is compatible with `other`.
+
+
+- - -
+
+#### `tf.TensorShape.is_fully_defined()` {#TensorShape.is_fully_defined}
+
+Returns True iff `self` is fully defined in every dimension.
+
+
+
+- - -
+
+#### `tf.TensorShape.with_rank(rank)` {#TensorShape.with_rank}
+
+Returns a shape based on `self` with the given rank.
+
+This method promotes a completely unknown shape to one with a
+known rank.
+
+##### Args:
+
+
+*  <b>`rank`</b>: An integer.
+
+##### Returns:
+
+  A shape that is at least as specific as `self` with the given rank.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `self` does not represent a shape with the given `rank`.
+
+
+- - -
+
+#### `tf.TensorShape.with_rank_at_least(rank)` {#TensorShape.with_rank_at_least}
+
+Returns a shape based on `self` with at least the given rank.
+
+##### Args:
+
+
+*  <b>`rank`</b>: An integer.
+
+##### Returns:
+
+  A shape that is at least as specific as `self` with at least the given
+  rank.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `self` does not represent a shape with at least the given
+    `rank`.
+
+
+- - -
+
+#### `tf.TensorShape.with_rank_at_most(rank)` {#TensorShape.with_rank_at_most}
+
+Returns a shape based on `self` with at most the given rank.
+
+##### Args:
+
+
+*  <b>`rank`</b>: An integer.
+
+##### Returns:
+
+  A shape that is at least as specific as `self` with at most the given
+  rank.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `self` does not represent a shape with at most the given
+    `rank`.
+
+
+
+- - -
+
+#### `tf.TensorShape.assert_has_rank(rank)` {#TensorShape.assert_has_rank}
+
+Raises an exception if `self` is not compatible with the given `rank`.
+
+##### Args:
+
+
+*  <b>`rank`</b>: An integer.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `self` does not represent a shape with the given `rank`.
+
+
+- - -
+
+#### `tf.TensorShape.assert_same_rank(other)` {#TensorShape.assert_same_rank}
+
+Raises an exception if `self` and `other` do not have compatible ranks.
+
+##### Args:
+
+
+*  <b>`other`</b>: Another `TensorShape`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `self` and `other` do not represent shapes with the
+    same rank.
+
+
+- - -
+
+#### `tf.TensorShape.assert_is_compatible_with(other)` {#TensorShape.assert_is_compatible_with}
+
+Raises exception if `self` and `other` do not represent the same shape.
+
+This method can be used to assert that there exists a shape that both
+`self` and `other` represent.
+
+##### Args:
+
+
+*  <b>`other`</b>: Another TensorShape.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `self` and `other` do not represent the same shape.
+
+
+- - -
+
+#### `tf.TensorShape.assert_is_fully_defined()` {#TensorShape.assert_is_fully_defined}
+
+Raises an exception if `self` is not fully defined in every dimension.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `self` does not have a known value for every dimension.
+
+
+
+#### Other Methods
+- - -
+
+#### `tf.TensorShape.__init__(dims)` {#TensorShape.__init__}
+
+Creates a new TensorShape with the given dimensions.
+
+##### Args:
+
+
+*  <b>`dims`</b>: A list of Dimensions, or None if the shape is unspecified.
+*  <b>`DEPRECATED`</b>: A single integer is treated as a singleton list.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If dims cannot be converted to a list of dimensions.
+
+
+- - -
+
+#### `tf.TensorShape.num_elements()` {#TensorShape.num_elements}
+
+Returns the total number of elements, or none for incomplete shapes.
+
+