# tf.Tensor

## Class Tensor

Defined in tensorflow/python/framework/ops.py.

See the guide: Building Graphs > Core graph data structures

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.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.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:

# 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.Session()

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


## Properties

### device

The name of the device on which this tensor will be produced, or None.

### dtype

The DType of elements in this tensor.

### graph

The Graph that contains this tensor.

### name

The string name of this tensor.

### op

The Operation that produces this tensor as an output.

### shape

Returns the TensorShape that represents the shape of this tensor.

The shape is computed using shape inference functions that are registered in the Op for each Operation. See tf.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:

c = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])

print(c.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.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.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.

### value_index

The index of this tensor in the outputs of its Operation.

## Methods

### __init__

__init__(
op,
value_index,
dtype
)


Creates a new Tensor.

#### Args:

• op: An Operation. Operation that computes this tensor.
• value_index: An int. Index of the operation's endpoint that produces this tensor.
• dtype: A DType. Type of elements stored in this tensor.

#### Raises:

• TypeError: If the op is not an Operation.

### __abs__

__abs__(
x,
name=None
)


Computes the absolute value of a tensor.

Given a tensor x of complex numbers, this operation returns a tensor of type float32 or float64 that is the absolute value of each element in x. All elements in x must be complex numbers of the form $$a + bj$$. The absolute value is computed as $$\sqrt{a^2 + b^2}$$. For example:

x = tf.constant([[-2.25 + 4.75j], [-3.25 + 5.75j]])
tf.abs(x)  # [5.25594902, 6.60492229]


#### Args:

• x: A Tensor or SparseTensor of type float32, float64, int32, int64, complex64 or complex128.
• name: A name for the operation (optional).

#### Returns:

A Tensor or SparseTensor the same size and type as x with absolute values. Note, for complex64 or complex128 input, the returned Tensor will be of type float32 or float64, respectively.

### __add__

__add__(
x,
y
)


Returns x + y element-wise.

NOTE: Add supports broadcasting. AddN does not. More about broadcasting here

#### Args:

• x: A Tensor. Must be one of the following types: bfloat16, half, float32, float64, uint8, int8, int16, int32, int64, complex64, complex128, string.
• y: A Tensor. Must have the same type as x.
• name: A name for the operation (optional).

#### Returns:

A Tensor. Has the same type as x.

### __and__

__and__(
x,
y
)


Returns the truth value of x AND y element-wise.

NOTE: LogicalAnd supports broadcasting. More about broadcasting here

#### Args:

• x: A Tensor of type bool.
• y: A Tensor of type bool.
• name: A name for the operation (optional).

#### Returns:

A Tensor of type bool.

### __bool__

__bool__()


Dummy method to prevent a tensor from being used as a Python bool.

This overload raises a TypeError when the user inadvertently treats a Tensor as a boolean (e.g. in an if statement). For example:

if tf.constant(True):  # Will raise.
# ...

if tf.constant(5) < tf.constant(7):  # Will raise.
# ...


This disallows ambiguities between testing the Python value vs testing the dynamic condition of the Tensor.

#### Raises:

TypeError.

### __div__

__div__(
x,
y
)


Divide two values using Python 2 semantics. Used for Tensor.div.

#### Args:

• x: Tensor numerator of real numeric type.
• y: Tensor denominator of real numeric type.
• name: A name for the operation (optional).

#### Returns:

x / y returns the quotient of x and y.

### __eq__

__eq__(other)


### __floordiv__

__floordiv__(
x,
y
)


Divides x / y elementwise, rounding toward the most negative integer.

The same as tf.div(x,y) for integers, but uses tf.floor(tf.div(x,y)) for floating point arguments so that the result is always an integer (though possibly an integer represented as floating point). This op is generated by x // y floor division in Python 3 and in Python 2.7 with from __future__ import division.

Note that for efficiency, floordiv uses C semantics for negative numbers (unlike Python and Numpy).

x and y must have the same type, and the result will have the same type as well.

#### Args:

• x: Tensor numerator of real numeric type.
• y: Tensor denominator of real numeric type.
• name: A name for the operation (optional).

#### Returns:

x / y rounded down (except possibly towards zero for negative integers).

#### Raises:

• TypeError: If the inputs are complex.

### __ge__

__ge__(
x,
y,
name=None
)


Returns the truth value of (x >= y) element-wise.

NOTE: GreaterEqual supports broadcasting. More about broadcasting here

#### Args:

• x: A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64.
• y: A Tensor. Must have the same type as x.
• name: A name for the operation (optional).

#### Returns:

A Tensor of type bool.

### __getitem__

__getitem__(
tensor,
slice_spec,
var=None
)


This operation extracts the specified region from the tensor. The notation is similar to NumPy with the restriction that currently only support basic indexing. That means that using a non-scalar tensor as input is not currently allowed.

Some useful examples:

# strip leading and trailing 2 elements
foo = tf.constant([1,2,3,4,5,6])
print(foo[2:-2].eval())  # => [3,4]

# skip every row and reverse every column
foo = tf.constant([[1,2,3], [4,5,6], [7,8,9]])
print(foo[::2,::-1].eval())  # => [[3,2,1], [9,8,7]]

# Use scalar tensors as indices on both dimensions
print(foo[tf.constant(0), tf.constant(2)].eval())  # => 3

# Insert another dimension
foo = tf.constant([[1,2,3], [4,5,6], [7,8,9]])
print(foo[tf.newaxis, :, :].eval()) # => [[[1,2,3], [4,5,6], [7,8,9]]]
print(foo[:, tf.newaxis, :].eval()) # => [[[1,2,3]], [[4,5,6]], [[7,8,9]]]
print(foo[:, :, tf.newaxis].eval()) # => [[[1],[2],[3]], [[4],[5],[6]],
[[7],[8],[9]]]

# Ellipses (3 equivalent operations)
foo = tf.constant([[1,2,3], [4,5,6], [7,8,9]])
print(foo[tf.newaxis, :, :].eval())  # => [[[1,2,3], [4,5,6], [7,8,9]]]
print(foo[tf.newaxis, ...].eval())  # => [[[1,2,3], [4,5,6], [7,8,9]]]
print(foo[tf.newaxis].eval())  # => [[[1,2,3], [4,5,6], [7,8,9]]]


Notes: - tf.newaxis is None as in NumPy. - An implicit ellipsis is placed at the end of the slice_spec - NumPy advanced indexing is currently not supported.

#### Args:

• tensor: An ops.Tensor object.
• slice_spec: The arguments to Tensor.getitem.
• var: In the case of variable slice assignment, the Variable object to slice (i.e. tensor is the read-only view of this variable).

#### Returns:

The appropriate slice of "tensor", based on "slice_spec".

#### Raises:

• ValueError: If a slice range is negative size.
• TypeError: If the slice indices aren't int, slice, or Ellipsis.

### __gt__

__gt__(
x,
y,
name=None
)


Returns the truth value of (x > y) element-wise.

NOTE: Greater supports broadcasting. More about broadcasting here

#### Args:

• x: A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64.
• y: A Tensor. Must have the same type as x.
• name: A name for the operation (optional).

#### Returns:

A Tensor of type bool.

### __invert__

__invert__(
x,
name=None
)


Returns the truth value of NOT x element-wise.

#### Args:

• x: A Tensor of type bool.
• name: A name for the operation (optional).

#### Returns:

A Tensor of type bool.

### __iter__

__iter__()


### __le__

__le__(
x,
y,
name=None
)


Returns the truth value of (x <= y) element-wise.

NOTE: LessEqual supports broadcasting. More about broadcasting here

#### Args:

• x: A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64.
• y: A Tensor. Must have the same type as x.
• name: A name for the operation (optional).

#### Returns:

A Tensor of type bool.

### __lt__

__lt__(
x,
y,
name=None
)


Returns the truth value of (x < y) element-wise.

NOTE: Less supports broadcasting. More about broadcasting here

#### Args:

• x: A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64.
• y: A Tensor. Must have the same type as x.
• name: A name for the operation (optional).

#### Returns:

A Tensor of type bool.

### __matmul__

__matmul__(
x,
y
)


Multiplies matrix a by matrix b, producing a * b.

The inputs must, following any transpositions, be tensors of rank >= 2 where the inner 2 dimensions specify valid matrix multiplication arguments, and any further outer dimensions match.

Both matrices must be of the same type. The supported types are: float16, float32, float64, int32, complex64, complex128.

Either matrix can be transposed or adjointed (conjugated and transposed) on the fly by setting one of the corresponding flag to True. These are False by default.

If one or both of the matrices contain a lot of zeros, a more efficient multiplication algorithm can be used by setting the corresponding a_is_sparse or b_is_sparse flag to True. These are False by default. This optimization is only available for plain matrices (rank-2 tensors) with datatypes bfloat16 or float32.

For example:

# 2-D tensor a
# [[1, 2, 3],
#  [4, 5, 6]]
a = tf.constant([1, 2, 3, 4, 5, 6], shape=[2, 3])

# 2-D tensor b
# [[ 7,  8],
#  [ 9, 10],
#  [11, 12]]
b = tf.constant([7, 8, 9, 10, 11, 12], shape=[3, 2])

# a * b
# [[ 58,  64],
#  [139, 154]]
c = tf.matmul(a, b)

# 3-D tensor a
# [[[ 1,  2,  3],
#   [ 4,  5,  6]],
#  [[ 7,  8,  9],
#   [10, 11, 12]]]
a = tf.constant(np.arange(1, 13, dtype=np.int32),
shape=[2, 2, 3])

# 3-D tensor b
# [[[13, 14],
#   [15, 16],
#   [17, 18]],
#  [[19, 20],
#   [21, 22],
#   [23, 24]]]
b = tf.constant(np.arange(13, 25, dtype=np.int32),
shape=[2, 3, 2])

# a * b
# [[[ 94, 100],
#   [229, 244]],
#  [[508, 532],
#   [697, 730]]]
c = tf.matmul(a, b)

# Since python >= 3.5 the @ operator is supported (see PEP 465).
# In TensorFlow, it simply calls the tf.matmul() function, so the
# following lines are equivalent:
d = a @ b @ [[10.], [11.]]
d = tf.matmul(tf.matmul(a, b), [[10.], [11.]])


#### Args:

• a: Tensor of type float16, float32, float64, int32, complex64, complex128 and rank > 1.
• b: Tensor with same type and rank as a.
• transpose_a: If True, a is transposed before multiplication.
• transpose_b: If True, b is transposed before multiplication.
• adjoint_a: If True, a is conjugated and transposed before multiplication.
• adjoint_b: If True, b is conjugated and transposed before multiplication.
• a_is_sparse: If True, a is treated as a sparse matrix.
• b_is_sparse: If True, b is treated as a sparse matrix.
• name: Name for the operation (optional).

#### Returns:

A Tensor of the same type as a and b where each inner-most matrix is the product of the corresponding matrices in a and b, e.g. if all transpose or adjoint attributes are False:

output[..., i, j] = sum_k (a[..., i, k] * b[..., k, j]), for all indices i, j.

• Note: This is matrix product, not element-wise product.

#### Raises:

• ValueError: If transpose_a and adjoint_a, or transpose_b and adjoint_b are both set to True.

### __mod__

__mod__(
x,
y
)


Returns element-wise remainder of division. When x < 0 xor y < 0 is

true, this follows Python semantics in that the result here is consistent with a flooring divide. E.g. floor(x / y) * y + mod(x, y) = x.

NOTE: FloorMod supports broadcasting. More about broadcasting here

#### Args:

• x: A Tensor. Must be one of the following types: int32, int64, bfloat16, half, float32, float64.
• y: A Tensor. Must have the same type as x.
• name: A name for the operation (optional).

#### Returns:

A Tensor. Has the same type as x.

### __mul__

__mul__(
x,
y
)


Dispatches cwise mul for "DenseDense" and "DenseSparse".

### __neg__

__neg__(
x,
name=None
)


Computes numerical negative value element-wise.

I.e., $$y = -x$$.

#### Args:

• x: A Tensor. Must be one of the following types: bfloat16, half, float32, float64, int32, int64, complex64, complex128.
• name: A name for the operation (optional).

#### Returns:

A Tensor. Has the same type as x.

### __nonzero__

__nonzero__()


Dummy method to prevent a tensor from being used as a Python bool.

This is the Python 2.x counterpart to __bool__() above.

#### Raises:

TypeError.

### __or__

__or__(
x,
y
)


Returns the truth value of x OR y element-wise.

NOTE: LogicalOr supports broadcasting. More about broadcasting here

#### Args:

• x: A Tensor of type bool.
• y: A Tensor of type bool.
• name: A name for the operation (optional).

#### Returns:

A Tensor of type bool.

### __pow__

__pow__(
x,
y
)


Computes the power of one value to another.

Given a tensor x and a tensor y, this operation computes $$x^y$$ for corresponding elements in x and y. For example:

x = tf.constant([[2, 2], [3, 3]])
y = tf.constant([[8, 16], [2, 3]])
tf.pow(x, y)  # [[256, 65536], [9, 27]]


#### Args:

• x: A Tensor of type float32, float64, int32, int64, complex64, or complex128.
• y: A Tensor of type float32, float64, int32, int64, complex64, or complex128.
• name: A name for the operation (optional).

#### Returns:

A Tensor.

### __radd__

__radd__(
y,
x
)


Returns x + y element-wise.

NOTE: Add supports broadcasting. AddN does not. More about broadcasting here

#### Args:

• x: A Tensor. Must be one of the following types: bfloat16, half, float32, float64, uint8, int8, int16, int32, int64, complex64, complex128, string.
• y: A Tensor. Must have the same type as x.
• name: A name for the operation (optional).

#### Returns:

A Tensor. Has the same type as x.

### __rand__

__rand__(
y,
x
)


Returns the truth value of x AND y element-wise.

NOTE: LogicalAnd supports broadcasting. More about broadcasting here

#### Args:

• x: A Tensor of type bool.
• y: A Tensor of type bool.
• name: A name for the operation (optional).

#### Returns:

A Tensor of type bool.

### __rdiv__

__rdiv__(
y,
x
)


Divide two values using Python 2 semantics. Used for Tensor.div.

#### Args:

• x: Tensor numerator of real numeric type.
• y: Tensor denominator of real numeric type.
• name: A name for the operation (optional).

#### Returns:

x / y returns the quotient of x and y.

### __rfloordiv__

__rfloordiv__(
y,
x
)


Divides x / y elementwise, rounding toward the most negative integer.

The same as tf.div(x,y) for integers, but uses tf.floor(tf.div(x,y)) for floating point arguments so that the result is always an integer (though possibly an integer represented as floating point). This op is generated by x // y floor division in Python 3 and in Python 2.7 with from __future__ import division.

Note that for efficiency, floordiv uses C semantics for negative numbers (unlike Python and Numpy).

x and y must have the same type, and the result will have the same type as well.

#### Args:

• x: Tensor numerator of real numeric type.
• y: Tensor denominator of real numeric type.
• name: A name for the operation (optional).

#### Returns:

x / y rounded down (except possibly towards zero for negative integers).

#### Raises:

• TypeError: If the inputs are complex.

### __rmatmul__

__rmatmul__(
y,
x
)


Multiplies matrix a by matrix b, producing a * b.

The inputs must, following any transpositions, be tensors of rank >= 2 where the inner 2 dimensions specify valid matrix multiplication arguments, and any further outer dimensions match.

Both matrices must be of the same type. The supported types are: float16, float32, float64, int32, complex64, complex128.

Either matrix can be transposed or adjointed (conjugated and transposed) on the fly by setting one of the corresponding flag to True. These are False by default.

If one or both of the matrices contain a lot of zeros, a more efficient multiplication algorithm can be used by setting the corresponding a_is_sparse or b_is_sparse flag to True. These are False by default. This optimization is only available for plain matrices (rank-2 tensors) with datatypes bfloat16 or float32.

For example:

# 2-D tensor a
# [[1, 2, 3],
#  [4, 5, 6]]
a = tf.constant([1, 2, 3, 4, 5, 6], shape=[2, 3])

# 2-D tensor b
# [[ 7,  8],
#  [ 9, 10],
#  [11, 12]]
b = tf.constant([7, 8, 9, 10, 11, 12], shape=[3, 2])

# a * b
# [[ 58,  64],
#  [139, 154]]
c = tf.matmul(a, b)

# 3-D tensor a
# [[[ 1,  2,  3],
#   [ 4,  5,  6]],
#  [[ 7,  8,  9],
#   [10, 11, 12]]]
a = tf.constant(np.arange(1, 13, dtype=np.int32),
shape=[2, 2, 3])

# 3-D tensor b
# [[[13, 14],
#   [15, 16],
#   [17, 18]],
#  [[19, 20],
#   [21, 22],
#   [23, 24]]]
b = tf.constant(np.arange(13, 25, dtype=np.int32),
shape=[2, 3, 2])

# a * b
# [[[ 94, 100],
#   [229, 244]],
#  [[508, 532],
#   [697, 730]]]
c = tf.matmul(a, b)

# Since python >= 3.5 the @ operator is supported (see PEP 465).
# In TensorFlow, it simply calls the tf.matmul() function, so the
# following lines are equivalent:
d = a @ b @ [[10.], [11.]]
d = tf.matmul(tf.matmul(a, b), [[10.], [11.]])


#### Args:

• a: Tensor of type float16, float32, float64, int32, complex64, complex128 and rank > 1.
• b: Tensor with same type and rank as a.
• transpose_a: If True, a is transposed before multiplication.
• transpose_b: If True, b is transposed before multiplication.
• adjoint_a: If True, a is conjugated and transposed before multiplication.
• adjoint_b: If True, b is conjugated and transposed before multiplication.
• a_is_sparse: If True, a is treated as a sparse matrix.
• b_is_sparse: If True, b is treated as a sparse matrix.
• name: Name for the operation (optional).

#### Returns:

A Tensor of the same type as a and b where each inner-most matrix is the product of the corresponding matrices in a and b, e.g. if all transpose or adjoint attributes are False:

output[..., i, j] = sum_k (a[..., i, k] * b[..., k, j]), for all indices i, j.

• Note: This is matrix product, not element-wise product.

#### Raises:

• ValueError: If transpose_a and adjoint_a, or transpose_b and adjoint_b are both set to True.

### __rmod__

__rmod__(
y,
x
)


Returns element-wise remainder of division. When x < 0 xor y < 0 is

true, this follows Python semantics in that the result here is consistent with a flooring divide. E.g. floor(x / y) * y + mod(x, y) = x.

NOTE: FloorMod supports broadcasting. More about broadcasting here

#### Args:

• x: A Tensor. Must be one of the following types: int32, int64, bfloat16, half, float32, float64.
• y: A Tensor. Must have the same type as x.
• name: A name for the operation (optional).

#### Returns:

A Tensor. Has the same type as x.

### __rmul__

__rmul__(
y,
x
)


Dispatches cwise mul for "DenseDense" and "DenseSparse".

### __ror__

__ror__(
y,
x
)


Returns the truth value of x OR y element-wise.

NOTE: LogicalOr supports broadcasting. More about broadcasting here

#### Args:

• x: A Tensor of type bool.
• y: A Tensor of type bool.
• name: A name for the operation (optional).

#### Returns:

A Tensor of type bool.

### __rpow__

__rpow__(
y,
x
)


Computes the power of one value to another.

Given a tensor x and a tensor y, this operation computes $$x^y$$ for corresponding elements in x and y. For example:

x = tf.constant([[2, 2], [3, 3]])
y = tf.constant([[8, 16], [2, 3]])
tf.pow(x, y)  # [[256, 65536], [9, 27]]


#### Args:

• x: A Tensor of type float32, float64, int32, int64, complex64, or complex128.
• y: A Tensor of type float32, float64, int32, int64, complex64, or complex128.
• name: A name for the operation (optional).

#### Returns:

A Tensor.

### __rsub__

__rsub__(
y,
x
)


Returns x - y element-wise.

NOTE: Subtract supports broadcasting. More about broadcasting here

#### Args:

• x: A Tensor. Must be one of the following types: bfloat16, half, float32, float64, uint8, int8, uint16, int16, int32, int64, complex64, complex128.
• y: A Tensor. Must have the same type as x.
• name: A name for the operation (optional).

#### Returns:

A Tensor. Has the same type as x.

### __rtruediv__

__rtruediv__(
y,
x
)


### __rxor__

__rxor__(
y,
x
)


x ^ y = (x | y) & ~(x & y).

### __sub__

__sub__(
x,
y
)


Returns x - y element-wise.

NOTE: Subtract supports broadcasting. More about broadcasting here

#### Args:

• x: A Tensor. Must be one of the following types: bfloat16, half, float32, float64, uint8, int8, uint16, int16, int32, int64, complex64, complex128.
• y: A Tensor. Must have the same type as x.
• name: A name for the operation (optional).

#### Returns:

A Tensor. Has the same type as x.

### __truediv__

__truediv__(
x,
y
)


### __xor__

__xor__(
x,
y
)


x ^ y = (x | y) & ~(x & y).

### consumers

consumers()


Returns a list of Operations that consume this tensor.

#### Returns:

A list of Operations.

### eval

eval(
feed_dict=None,
session=None
)


Evaluates this tensor in a Session.

Calling this method will execute all preceding operations that produce the inputs needed for the operation that produces this tensor.

N.B. Before invoking Tensor.eval(), its graph must have been launched in a session, and either a default session must be available, or session must be specified explicitly.

#### Args:

• feed_dict: A dictionary that maps Tensor objects to feed values. See tf.Session.run for a description of the valid feed values.
• session: (Optional.) The Session to be used to evaluate this tensor. If none, the default session will be used.

#### Returns:

A numpy array corresponding to the value of this tensor.

### get_shape

get_shape()


Alias of Tensor.shape.

### set_shape

set_shape(shape)


Updates the shape of this tensor.

This method can be called multiple times, and will merge the given shape with the current shape of this tensor. It can be used to provide additional information about the shape of this tensor that cannot be inferred from the graph alone. For example, this can be used to provide additional information about the shapes of images:

_, image_data = tf.TFRecordReader(...).read(...)
image = tf.image.decode_png(image_data, channels=3)

# The height and width dimensions of image are data dependent, and
# cannot be computed without executing the op.
print(image.shape)
==> TensorShape([Dimension(None), Dimension(None), Dimension(3)])

# We know that each image in this dataset is 28 x 28 pixels.
image.set_shape([28, 28, 3])
print(image.shape)
==> TensorShape([Dimension(28), Dimension(28), Dimension(3)])


#### Args:

• shape: A TensorShape representing the shape of this tensor, a TensorShapeProto, a list, a tuple, or None.

#### Raises:

• ValueError: If shape is not compatible with the current shape of this tensor.