mindspore.Tensor

class mindspore.Tensor(input_data=None, dtype=None, shape=None, init=None, internal=False, const_arg=False, device=None)[source]

Tensor is a data structure that stores an n-dimensional array.

Note

If init interface is used to initialize Tensor, the Tensor.init_data API needs to be called to load the actual data to Tensor.

Warning

To convert dtype of a Tensor, it is recommended to use Tensor.astype() rather than Tensor(sourceTensor, dtype=newDtype).

Parameters

input_data (Union[Tensor, float, int, bool, tuple, list, numpy.ndarray]) – The data to be stored. It can be another Tensor, Python number or NumPy ndarray. Default: None .
dtype (mindspore.dtype) – Used to indicate the data type of the output Tensor. The argument should be defined in mindspore.dtype. If it is None , the data type of the output Tensor will be the same as the input_data. Default: None .
shape (Union[tuple, list, int, mindspore.Symbol]) – Used to indicate the shape of the output Tensor. If input_data is available, shape doesn't need to be set. If None or Symbol exists in shape , a tensor of dynamic shape is created, input_data doesn't need to be set; if only integers exist in shape, a tensor of static shape is created, input_data or init must be set. Default: None .
init (Initializer) – The information of init data. init is used for delayed initialization in parallel mode, when using init, dtype and shape must be set. Default: None .
internal (bool) – Whether it is created by the framework. 'True' means that the tensor is created by framework. 'False' means that the tensor is created by user. Default: False .
const_arg (bool) – Whether the tensor is a constant when it is used for the argument of a network. Default: False .
device (str) – This parameter is reserved and does not need to be configured. Default: None .

Outputs:: Tensor.

Note

The default value None of input_data works as a placeholder, it does not mean that we can create a NoneType Tensor. Tensor with shape contains 0 is not fully tested and supported.

Examples

>>> import numpy as np
>>> import mindspore as ms
>>> from mindspore import Tensor
>>> from mindspore.common.initializer import One
>>> # initialize a tensor with numpy.ndarray
>>> t1 = Tensor(np.zeros([1, 2, 3]), ms.float32)
>>> print(t1)
[[[0. 0. 0.]
[0. 0. 0.]]]
>>> print(type(t1))
<class 'mindspore.common.tensor.Tensor'>
>>> print(t1.shape)
(1, 2, 3)
>>> print(t1.dtype)
Float32
>>>
>>> # initialize a tensor with a float scalar
>>> t2 = Tensor(0.1)
>>> print(t2)
0.1
>>> print(type(t2))
<class 'mindspore.common.tensor.Tensor'>
>>> print(t2.shape)
()
>>> print(t2.dtype)
Float32
>>>
>>> # initialize a tensor with a tuple
>>> t3 = Tensor((1, 2))
>>> print(t3)
[1 2]
>>> print(type(t3))
<class 'mindspore.common.tensor.Tensor'>
>>> print(t3.shape)
(2,)
>>> print(t3.dtype)
Int64
...
>>> # initialize a tensor with init
>>> t4 = Tensor(shape = (1, 3), dtype=ms.float32, init=One())
>>> print(t4)
[[1. 1. 1.]]
>>> print(type(t4))
<class 'mindspore.common.tensor.Tensor'>
>>> print(t4.shape)
(1, 3)
>>> print(t4.dtype)
Float32

`mindspore.Tensor.abs`	For details, please refer to `mindspore.ops.abs()`.
`mindspore.Tensor.absolute`	Alias for `mindspore.Tensor.abs()`.
`mindspore.Tensor.acos`	For details, please refer to `mindspore.ops.acos()`.
`mindspore.Tensor.acosh`	For details, please refer to `mindspore.ops.acosh()`.
`mindspore.Tensor.add`	For details, please refer to `mindspore.ops.add()`.
`mindspore.Tensor.addbmm`	For details, please refer to `mindspore.ops.addbmm()`.
`mindspore.Tensor.addcdiv`	For details, please refer to `mindspore.ops.addcdiv()`.
`mindspore.Tensor.addcmul`	For details, please refer to `mindspore.ops.addcmul()`.
`mindspore.Tensor.addmm`	For details, please refer to `mindspore.ops.addmm()`.
`mindspore.Tensor.addmm_`	For details, please refer to `mindspore.ops.addmm()`.
`mindspore.Tensor.addmv`	For details, please refer to `mindspore.ops.addmv()`.
`mindspore.Tensor.addr`	For details, please refer to `mindspore.ops.addr()`.
`mindspore.Tensor.adjoint`	For details, please refer to `mindspore.ops.adjoint()`.
`mindspore.Tensor.all`	For details, please refer to `mindspore.ops.all()`.
`mindspore.Tensor.amax`	For details, please refer to `mindspore.ops.amax()`.
`mindspore.Tensor.amin`	For details, please refer to `mindspore.ops.amin()`.
`mindspore.Tensor.aminmax`	For details, please refer to `mindspore.ops.aminmax()`.
`mindspore.Tensor.any`	For details, please refer to `mindspore.ops.any()`.
`mindspore.Tensor.angle`	For details, please refer to `mindspore.ops.angle()`.
`mindspore.Tensor.approximate_equal`	For details, please refer to `mindspore.ops.approximate_equal()`.
`mindspore.Tensor.arccos`	Alias for `mindspore.Tensor.acos()`.
`mindspore.Tensor.arccosh`	For details, please refer to `mindspore.ops.arccosh()`.
`mindspore.Tensor.arcsin`	For details, please refer to `mindspore.ops.arcsin()`.
`mindspore.Tensor.arcsinh`	Alias for `mindspore.Tensor.asinh()`.
`mindspore.Tensor.arctan`	For details, please refer to `mindspore.ops.arctan()`.
`mindspore.Tensor.arctan2`	For details, please refer to `mindspore.ops.arctan2()`.
`mindspore.Tensor.arctanh`	Alias for `mindspore.Tensor.atanh()`.
`mindspore.Tensor.argmax`	Returns the indices of the maximum value along a specified axis of a Tensor.
`mindspore.Tensor.argmax_with_value`	Returns the maximum value with corresponding index.
`mindspore.Tensor.argmin`	For details, please refer to `mindspore.ops.argmin()`.
`mindspore.Tensor.argmin_with_value`	Returns the minimum value with corresponding index.
`mindspore.Tensor.argsort`	For details, please refer to `mindspore.ops.argsort()`.
`mindspore.Tensor.argwhere`	For details, please refer to `mindspore.ops.argwhere()`.
`mindspore.Tensor.asin`	For details, please refer to `mindspore.ops.asin()`.
`mindspore.Tensor.asinh`	For details, please refer to `mindspore.ops.asinh()`.
`mindspore.Tensor.asnumpy`	Convert tensor to numpy array.
`mindspore.Tensor.assign_value`	Assign another tensor value to this tensor.
`mindspore.Tensor.astype`	Return a copy of the tensor, cast to a specified type.
`mindspore.Tensor.atan`	For details, please refer to `mindspore.ops.atan()`.
`mindspore.Tensor.atan2`	For details, please refer to `mindspore.ops.atan2()`.
`mindspore.Tensor.atanh`	For details, please refer to `mindspore.ops.atanh()`.
`mindspore.Tensor.baddbmm`	For details, please refer to `mindspore.ops.baddbmm()`.
`mindspore.Tensor.bernoulli`	For details, please refer to `mindspore.ops.bernoulli()`.
`mindspore.Tensor.bincount`	For details, please refer to `mindspore.ops.bincount()`.
`mindspore.Tensor.bitwise_and`	For details, please refer to `mindspore.ops.bitwise_and()`.
`mindspore.Tensor.bitwise_left_shift`	For details, please refer to `mindspore.ops.bitwise_left_shift()`.
`mindspore.Tensor.bitwise_or`	For details, please refer to `mindspore.ops.bitwise_or()`.
`mindspore.Tensor.bitwise_right_shift`	For details, please refer to `mindspore.ops.bitwise_right_shift()`.
`mindspore.Tensor.bitwise_xor`	For details, please refer to `mindspore.ops.bitwise_xor()`.
`mindspore.Tensor.bmm`	For details, please refer to `mindspore.ops.bmm()`.
`mindspore.Tensor.bool`	Converts input tensor dtype to bool.
`mindspore.Tensor.broadcast_to`	For details, please refer to `mindspore.ops.broadcast_to()`.
`mindspore.Tensor.byte`	Converts input tensor dtype to uint8.
`mindspore.Tensor.cauchy`	Fills the tensor with numbers drawn from the Cauchy distribution.
`mindspore.Tensor.ceil`	Rounds a tensor up to the closest integer element-wise.
`mindspore.Tensor.cholesky`	For details, please refer to `mindspore.ops.cholesky()`.
`mindspore.Tensor.cholesky_solve`	For details, please refer to `mindspore.ops.cholesky_solve()`.
`mindspore.Tensor.choose`	Construct a tensor from an index tensor and a list of tensors to choose from.
`mindspore.Tensor.chunk`	For details, please refer to `mindspore.ops.chunk()`.
`mindspore.Tensor.clamp`	Clamps tensor values between the specified minimum value and maximum value.
`mindspore.Tensor.clip`	clips tensor values between the specified minimum value and maximum value.
`mindspore.Tensor.col2im`	For details, please refer to `mindspore.ops.col2im()`.
`mindspore.Tensor.conj`	For details, please refer to `mindspore.ops.conj()`.
`mindspore.Tensor.contiguous`	Converts a Tensor into a continuous-memory Tensor that contains the same data as the original Tensor.
`mindspore.Tensor.copy`	Return a copy of the tensor.
`mindspore.Tensor.copysign`	For details, please refer to `mindspore.ops.copysign()`.
`mindspore.Tensor.cos`	Computes cosine of self element-wise.
`mindspore.Tensor.cosh`	For details, please refer to `mindspore.ops.cosh()`.
`mindspore.Tensor.count_nonzero`	For details, please refer to `mindspore.ops.count_nonzero()`.
`mindspore.Tensor.cov`	For details, please refer to `mindspore.ops.cov()`.
`mindspore.Tensor.cross`	For details, please refer to `mindspore.ops.cross()`.
`mindspore.Tensor.cummax`	For details, please refer to `mindspore.ops.cummax()`.
`mindspore.Tensor.cummin`	For details, please refer to `mindspore.ops.cummin()`.
`mindspore.Tensor.cumprod`	For details, please refer to `mindspore.ops.cumprod()`.
`mindspore.Tensor.cumsum`	For details, please refer to `mindspore.ops.cumsum()`.
`mindspore.Tensor.deg2rad`	For details, please refer to `mindspore.ops.deg2rad()`.
`mindspore.Tensor.diag`	For details, please refer to `mindspore.ops.diag()`.
`mindspore.Tensor.diagflat`	For details, please refer to `mindspore.ops.diagflat()`.
`mindspore.Tensor.diagonal`	For details, please refer to `mindspore.ops.diagonal()`.
`mindspore.Tensor.diagonal_scatter`	For details, please refer to `mindspore.ops.diagonal_scatter()`.
`mindspore.Tensor.diff`	For details, please refer to `mindspore.ops.diff()`.
`mindspore.Tensor.digamma`	For details, please refer to `mindspore.ops.digamma()`.
`mindspore.Tensor.div`	Divides the first input tensor by the second input tensor in floating-point type element-wise.
`mindspore.Tensor.divide`	For details, please refer to :func:'mindspore.Tensor.div'
`mindspore.Tensor.dot`	For details, please refer to `mindspore.ops.dot()`.
`mindspore.Tensor.dsplit`	For details, please refer to `mindspore.ops.dsplit()`.
`mindspore.Tensor.dtype`	Return the dtype of the tensor (`mindspore.dtype`).
`mindspore.Tensor.eq`	Computes the equivalence between two tensors element-wise.
`mindspore.Tensor.equal`	For details, please refer to `mindspore.ops.equal()`.
`mindspore.Tensor.erf`	Computes the Gauss error function of input element-wise.
`mindspore.Tensor.erfc`	For details, please refer to `mindspore.ops.erfc()`.
`mindspore.Tensor.erfinv`	For details, please refer to `mindspore.ops.erfinv()`.
`mindspore.Tensor.eigvals`	For details, please refer to `mindspore.ops.eigvals()`.
`mindspore.Tensor.exp`	Returns exponential of a tensor element-wise.
`mindspore.Tensor.expand_as`	Expand the dimension of target tensor to the dimension of input tensor.
`mindspore.Tensor.expand_dims`	For details, please refer to `mindspore.ops.expand_dims()`.
`mindspore.Tensor.expm1`	For details, please refer to `mindspore.ops.expm1()`.
`mindspore.Tensor.fill_diagonal`	Fills the main diagonal of a Tensor with a specified value and returns the result.
`mindspore.Tensor.flatten`	For details, please refer to `mindspore.ops.flatten()`.
`mindspore.Tensor.flip`	For details, please refer to `mindspore.ops.flip()`.
`mindspore.Tensor.fliplr`	For details, please refer to `mindspore.ops.fliplr()`.
`mindspore.Tensor.flipud`	For details, please refer to `mindspore.ops.flipud()`.
`mindspore.Tensor.float`	Converts input tensor dtype to float32.
`mindspore.Tensor.float_power`	For details, please refer to `mindspore.ops.float_power()`.
`mindspore.Tensor.floor`	Rounds a tensor down to the closest integer element-wise.
`mindspore.Tensor.floor_divide`	For details, please refer to `mindspore.ops.floor_divide()`.
`mindspore.Tensor.flush_from_cache`	Flush cache data to host if tensor is cache enable.
`mindspore.Tensor.fold`	For details, please refer to `mindspore.ops.fold()`.
`mindspore.Tensor.fmax`	For details, please refer to `mindspore.ops.fmax()`.
`mindspore.Tensor.fmod`	For details, please refer to `mindspore.ops.fmod()`.
`mindspore.Tensor.frac`	For details, please refer to `mindspore.ops.frac()`.
`mindspore.Tensor.from_numpy`	Convert numpy array to Tensor.
`mindspore.Tensor.gather`	Gather data from a tensor by indices.
`mindspore.Tensor.gather_elements`	For details, please refer to `mindspore.ops.gather_elements()`.
`mindspore.Tensor.gather_nd`	For details, please refer to `mindspore.ops.gather_nd()`.
`mindspore.Tensor.ge`	For details, please refer to `mindspore.ops.ge()`.
`mindspore.Tensor.geqrf`	For details, please refer to `mindspore.ops.geqrf()`.
`mindspore.Tensor.ger`	For details, please refer to `mindspore.ops.ger()`.
`mindspore.Tensor.greater`	Compare the value of the input parameters \(self > other\) element-wise, and the output result is a bool value.
`mindspore.Tensor.greater_equal`	For details, please refer to `mindspore.ops.greater_equal()`.
`mindspore.Tensor.gt`	For details, please refer to :func:'mindspore.Tensor.greater'.
`mindspore.Tensor.H`	Returns a view of a matrix (2-D tensor) conjugated and transposed.
`mindspore.Tensor.half`	Converts input tensor dtype to float16.
`mindspore.Tensor.hardshrink`	For details, please refer to `mindspore.ops.hardshrink()`.
`mindspore.Tensor.has_init`	Whether tensor is initialized.
`mindspore.Tensor.heaviside`	For details, please refer to `mindspore.ops.heaviside()`.
`mindspore.Tensor.histc`	For details, please refer to `mindspore.ops.histc()`.
`mindspore.Tensor.hsplit`	For details, please refer to `mindspore.ops.hsplit()`.
`mindspore.Tensor.hypot`	For details, please refer to `mindspore.ops.hypot()`.
`mindspore.Tensor.i0`	For details, please refer to `mindspore.ops.i0()`.
`mindspore.Tensor.igamma`	For details, please refer to `mindspore.ops.igamma()`.
`mindspore.Tensor.igammac`	For details, please refer to `mindspore.ops.igammac()`.
`mindspore.Tensor.imag`	For details, please refer to `mindspore.ops.imag()`.
`mindspore.Tensor.index_add`	For details, please refer to `mindspore.ops.index_add()`.
`mindspore.Tensor.index_fill`	For details, please refer to `mindspore.ops.index_fill()`.
`mindspore.Tensor.index_put`	Returns a Tensor.
`mindspore.Tensor.index_select`	Generates a new Tensor that accesses the values of self along the specified axis dimension using the indices specified in index.
`mindspore.Tensor.init_data`	Get the tensor format data of this Tensor.
`mindspore.Tensor.inner`	For details, please refer to `mindspore.ops.inner()`.
`mindspore.Tensor.inplace_update`	For details, please refer to `mindspore.ops.inplace_update()`.
`mindspore.Tensor.int`	Converts input tensor dtype to int32.
`mindspore.Tensor.inv`	For details, please refer to `mindspore.ops.inv()`.
`mindspore.Tensor.inverse`	For details, please refer to `mindspore.ops.inverse()`.
`mindspore.Tensor.invert`	For details, please refer to `mindspore.ops.invert()`.
`mindspore.Tensor.isclose`	Returns a tensor of Boolean values indicating whether each element of input is "close" to the corresponding element of other.
`mindspore.Tensor.isfinite`	Determine which elements are finite for each position.
`mindspore.Tensor.is_floating_point`	For details, please refer to `mindspore.ops.is_floating_point()`.
`mindspore.Tensor.isinf`	For details, please refer to `mindspore.ops.isinf()`.
`mindspore.Tensor.isnan`	For details, please refer to `mindspore.ops.isnan()`.
`mindspore.Tensor.isneginf`	For details, please refer to `mindspore.ops.isneginf()`.
`mindspore.Tensor.isposinf`	For details, please refer to `mindspore.ops.isposinf()`.
`mindspore.Tensor.isreal`	For details, please refer to `mindspore.ops.isreal()`.
`mindspore.Tensor.is_signed`	Judge whether the data type of tensor is a signed data type.
`mindspore.Tensor.is_complex`	For details, please refer to `mindspore.ops.is_complex()`.
`mindspore.Tensor.is_contiguous`	Determines whether the memory of tensor is contiguous.
`mindspore.Tensor.item`	Get the item at the specified index of the tensor.
`mindspore.Tensor.itemset`	Insert scalar into a tensor (scalar is cast to tensor's dtype, if possible).
`mindspore.Tensor.itemsize`	Return the length of one tensor element in bytes.
`mindspore.Tensor.lcm`	For details, please refer to `mindspore.ops.lcm()`.
`mindspore.Tensor.ldexp`	For details, please refer to `mindspore.ops.ldexp()`.
`mindspore.Tensor.le`	Computes the boolean value of :math: self <= other element-wise.
`mindspore.Tensor.lerp`	For details, please refer to `mindspore.ops.lerp()`.
`mindspore.Tensor.less`	Computes the boolean value of \(input < other\) element-wise.
`mindspore.Tensor.less_equal`	For more details, please refer to `mindspore.Tensor.le()`.
`mindspore.Tensor.log`	Returns the natural logarithm of a tensor element-wise.
`mindspore.Tensor.log10`	For details, please refer to `mindspore.ops.log10()`.
`mindspore.Tensor.log1p`	For details, please refer to `mindspore.ops.log1p()`.
`mindspore.Tensor.log2`	For details, please refer to `mindspore.ops.log2()`.
`mindspore.Tensor.logaddexp`	For details, please refer to `mindspore.ops.logaddexp()`.
`mindspore.Tensor.logaddexp2`	For details, please refer to `mindspore.ops.logaddexp2()`.
`mindspore.Tensor.logcumsumexp`	For details, please refer to `mindspore.ops.logcumsumexp()`.
`mindspore.Tensor.logdet`	For details, please refer to `mindspore.ops.logdet()`.
`mindspore.Tensor.logical_and`	Computes the "logical AND" of two tensors element-wise.
`mindspore.Tensor.logical_not`	Computes the "logical NOT" of a tensor element-wise.
`mindspore.Tensor.logical_or`	Computes the "logical OR" of two tensors element-wise.
`mindspore.Tensor.logical_xor`	For details, please refer to `mindspore.ops.logical_xor()`.
`mindspore.Tensor.logit`	For details, please refer to `mindspore.ops.logit()`.
`mindspore.Tensor.logsumexp`	For details, please refer to `mindspore.ops.logsumexp()`.
`mindspore.Tensor.log_normal`	Fills the elements of the input tensor with log normal values initialized by given mean and std:
`mindspore.Tensor.long`	Converts input tensor dtype to int64.
`mindspore.Tensor.lt`	For more details, please refer to `mindspore.Tensor.less()`.
`mindspore.Tensor.lu_solve`	For details, please refer to `mindspore.ops.lu_solve()`.
`mindspore.Tensor.masked_fill`	Fills elements of Tensor with value where mask is True.
`mindspore.Tensor.masked_scatter`	Returns a Tensor.
`mindspore.Tensor.masked_select`	Returns a new 1-D Tensor which indexes the x tensor according to the boolean mask.
`mindspore.Tensor.matmul`	Returns the matrix product of two tensors.
`mindspore.Tensor.max`	Calculates the maximum value along with the given axis for the self tensor.
`mindspore.Tensor.maximum`	Computes the maximum of input tensors element-wise.
`mindspore.Tensor.mean`	Reduces all dimension of a tensor by averaging all elements in the dimension, by default.
`mindspore.Tensor.median`	For details, please refer to `mindspore.ops.median()`.
`mindspore.Tensor.mH`	Accessing this property is equivalent to Calling self.adjoint().
`mindspore.Tensor.min`	Calculates the minimum value of the input tensor.
`mindspore.Tensor.minimum`	Computes the minimum of input tensors element-wise.
`mindspore.Tensor.mm`	For details, please refer to `mindspore.ops.mm()`.
`mindspore.Tensor.movedim`	For details, please refer to `mindspore.ops.movedim()`.
`mindspore.Tensor.moveaxis`	For details, please refer to `mindspore.ops.moveaxis()`.
`mindspore.Tensor.move_to`	Copy Tensor to target device synchronously or asynchronously, default synchronously.
`mindspore.Tensor.msort`	For details, please refer to `mindspore.ops.msort()`.
`mindspore.Tensor.mT`	Returns the Tensor that exchanges the last two dimensions.
`mindspore.Tensor.mul`	Multiplies two tensors element-wise.
`mindspore.Tensor.multinomial`	For details, please refer to `mindspore.ops.multinomial()`.
`mindspore.Tensor.multiply`	For details, please refer to `mindspore.ops.multiply()`.
`mindspore.Tensor.mvlgamma`	For details, please refer to `mindspore.ops.mvlgamma()`.
`mindspore.Tensor.nan_to_num`	Replace the NaN, positive infinity and negative infinity values of the self with the specified values in nan, posinf and neginf respectively.
`mindspore.Tensor.nanmean`	For details, please refer to `mindspore.ops.nanmean()`.
`mindspore.Tensor.nanmedian`	For details, please refer to `mindspore.ops.nanmedian()`.
`mindspore.Tensor.nansum`	For details, please refer to `mindspore.ops.nansum()`.
`mindspore.Tensor.narrow`	For details, please refer to `mindspore.ops.narrow()`.
`mindspore.Tensor.nbytes`	Return the total number of bytes taken by the tensor.
`mindspore.Tensor.ndim`	Return the number of tensor dimensions.
`mindspore.Tensor.ndimension`	Alias for `mindspore.Tensor.ndim()`.
`mindspore.Tensor.ne`	Computes the non-equivalence of two tensors element-wise.
`mindspore.Tensor.neg`	Returns a tensor with negative values of the input tensor element-wise.
`mindspore.Tensor.negative`	Returns a tensor with negative values of the self tensor element-wise.
`mindspore.Tensor.nelement`	Alias for `mindspore.Tensor.numel()`.
`mindspore.Tensor.new_ones`	Return a tensor of size filled with ones.
`mindspore.Tensor.new_zeros`	Return a tensor of size filled with zeros.
`mindspore.Tensor.nextafter`	For details, please refer to `mindspore.ops.nextafter()`.
`mindspore.Tensor.numel`	For details, please refer to `mindspore.ops.numel()`.
`mindspore.Tensor.numpy`	Alias for `mindspore.Tensor.asnumpy()`.
`mindspore.Tensor.nonzero`	For details, please refer to `mindspore.ops.nonzero()`.
`mindspore.Tensor.norm`	For details, please refer to `mindspore.ops.norm()`.
`mindspore.Tensor.not_equal`
`mindspore.Tensor.outer`	For details, please refer to `mindspore.ops.outer()`.
`mindspore.Tensor.orgqr`	For details, please refer to `mindspore.ops.orgqr()`.
`mindspore.Tensor.ormqr`	For details, please refer to `mindspore.ops.ormqr()`, Args input2 and input3 correspond to the args tau and other of `mindspore.ops.ormqr()`.
`mindspore.Tensor.permute`	For details, please refer to `mindspore.ops.permute()`.
`mindspore.Tensor.positive`	For details, please refer to `mindspore.ops.positive()`.
`mindspore.Tensor.pow`	Calculates the exponent power of each element in self.
`mindspore.Tensor.prod`	Reduces a dimension of a tensor by multiplying all elements in the dimension, by default.
`mindspore.Tensor.ptp`	The name of the function comes from the acronym for "peak to peak".
`mindspore.Tensor.rad2deg`	For details, please refer to `mindspore.ops.rad2deg()`.
`mindspore.Tensor.random_categorical`	For details, please refer to `mindspore.ops.random_categorical()`.
`mindspore.Tensor.ravel`	Return a contiguous flattened tensor.
`mindspore.Tensor.real`	For details, please refer to `mindspore.ops.real()`.
`mindspore.Tensor.reciprocal`	Returns reciprocal of a tensor element-wise.
`mindspore.Tensor.remainder`	Computes the remainder of self divided by other element-wise.
`mindspore.Tensor.renorm`	For details, please refer to `mindspore.ops.renorm()`.
`mindspore.Tensor.repeat`	Repeat elements of a tensor.
`mindspore.Tensor.repeat_interleave`	Repeat elements of a tensor along an axis, like numpy.repeat.
`mindspore.Tensor.reshape`	Rearranges self Tensor based on the given shape.
`mindspore.Tensor.reshape_as`	Change the shape of the Tensor to the shape of other without changing the data.
`mindspore.Tensor.resize`	Changes shape and size of tensor in-place.
`mindspore.Tensor.reverse`	For details, please refer to `mindspore.ops.flip()`.
`mindspore.Tensor.reverse_sequence`	For details, please refer to `mindspore.ops.reverse_sequence()`.
`mindspore.Tensor.roll`	For details, please refer to `mindspore.ops.roll()`.
`mindspore.Tensor.round`	Returns half to even of a tensor element-wise.
`mindspore.Tensor.rot90`	For details, please refer to `mindspore.ops.rot90()`.
`mindspore.Tensor.rsqrt`	Computes reciprocal of square root of self tensor element-wise.
`mindspore.Tensor.register_hook`	Registers a backward hook for tensor.
`mindspore.Tensor.scatter`	For details, please refer to `mindspore.ops.scatter()`.
`mindspore.Tensor.scatter_add`	For details, please refer to `mindspore.ops.scatter_add()`.
`mindspore.Tensor.scatter_div`	For details, please refer to `mindspore.ops.scatter_div()`.
`mindspore.Tensor.scatter_max`	For details, please refer to `mindspore.ops.scatter_max()`.
`mindspore.Tensor.scatter_min`	For details, please refer to `mindspore.ops.scatter_min()`.
`mindspore.Tensor.scatter_mul`	For details, please refer to `mindspore.ops.scatter_mul()`.
`mindspore.Tensor.scatter_sub`	Creates a new tensor by subtracting the values from the positions in self tensor indicated by indices, with values from updates.
`mindspore.Tensor.searchsorted`	Finds indices where elements should be inserted to maintain order.
`mindspore.Tensor.select`	For details, please refer to `mindspore.ops.select()`.
`mindspore.Tensor.select_scatter`	For details, please refer to `mindspore.ops.select_scatter()`.
`mindspore.Tensor.set_const_arg`	Specify whether the tensor is a constant when it is used for the argument of a network.
`mindspore.Tensor.sgn`	For details, please refer to `mindspore.ops.sgn()`.
`mindspore.Tensor.shape`	For details, please refer to `mindspore.ops.shape()`.
`mindspore.Tensor.short`	Return a copy of the tensor, cast to int16 type, equivalent to self.astype(mstype.int16).
`mindspore.Tensor.sigmoid`	Computes Sigmoid of self element-wise.
`mindspore.Tensor.sign`	For details, please refer to `mindspore.ops.sign()`.
`mindspore.Tensor.signbit`	For details, please refer to `mindspore.ops.signbit()`.
`mindspore.Tensor.sin`	For details, please refer to `mindspore.ops.sin()`.
`mindspore.Tensor.sinc`	For details, please refer to `mindspore.ops.sinc()`.
`mindspore.Tensor.sinh`	For details, please refer to `mindspore.ops.sinh()`.
`mindspore.Tensor.size`	For details, please refer to `mindspore.ops.size()`.
`mindspore.Tensor.slice_scatter`	For details, please refer to `mindspore.ops.slice_scatter()`.
`mindspore.Tensor.slogdet`	For details, please refer to `mindspore.ops.slogdet()`.
`mindspore.Tensor.softmax`	For details, please refer to `mindspore.ops.softmax()`.
`mindspore.Tensor.sort`	Sorts the elements of the self tensor along the given dimension in the specified order.
`mindspore.Tensor.split`	Splits the Tensor into chunks along the given axis.
`mindspore.Tensor.sqrt`	For details, please refer to `mindspore.ops.sqrt()`.
`mindspore.Tensor.square`	For details, please refer to `mindspore.ops.square()`.
`mindspore.Tensor.squeeze`	For details, please refer to `mindspore.ops.squeeze()`.
`mindspore.Tensor.std`	For details, please refer to `mindspore.ops.std()`.
`mindspore.Tensor.storage_offset`	Tensor's offset in the underlying storage in terms of the number of storage elements.
`mindspore.Tensor.stride`	The stride to jump from one element to the next in the input dim.
`mindspore.Tensor.strides`	Return the tuple of bytes to step in each dimension when traversing a tensor.
`mindspore.Tensor.sub`	For details, please refer to `mindspore.ops.sub()`.
`mindspore.Tensor.subtract`	For details, please refer to `mindspore.ops.subtract()`.
`mindspore.Tensor.sum`	Return sum of tensor elements over a given axis.
`mindspore.Tensor.sum_to_size`	Sum self Tensor to the size.
`mindspore.Tensor.svd`	For details, please refer to `mindspore.ops.svd()`.
`mindspore.Tensor.swapaxes`	For details, please refer to `mindspore.ops.swapaxes()`.
`mindspore.Tensor.swapdims`	For details, please refer to `mindspore.ops.swapdims()`.
`mindspore.Tensor.T`	Return the transposed tensor.
`mindspore.Tensor.t`	For details, please refer to `mindspore.ops.t()`.
`mindspore.Tensor.take`	Takes elements from a tensor along an axis.
`mindspore.Tensor.tan`	For details, please refer to `mindspore.ops.tan()`.
`mindspore.Tensor.tanh`	Computes hyperbolic tangent of input element-wise.
`mindspore.Tensor.tensor_split`	For details, please refer to `mindspore.ops.tensor_split()`.
`mindspore.Tensor.tile`	Creates a new tensor by replicating self dims times.
`mindspore.Tensor.to`	Returns a tensor with the new specified data type.
`mindspore.Tensor.to_coo`	Convert a Tensor to COOTensor.
`mindspore.Tensor.to_csr`	Convert a Tensor to CSRTensor.
`mindspore.Tensor.tolist`	Convert a Tensor to List.
`mindspore.Tensor.topk`	For details, please refer to `mindspore.ops.topk()`.
`mindspore.Tensor.trace`	Return the sum along diagonals of the tensor.
`mindspore.Tensor.transpose`	For details, please refer to `mindspore.ops.transpose()`.
`mindspore.Tensor.tril`	transpose(input, diagonal=0) -> Tensor
`mindspore.Tensor.triu`	Returns the upper triangle part of 'self' (elements that contain the diagonal and below), and set the other elements to zeros.
`mindspore.Tensor.true_divide`	Alias for Tensor.div() with \(rounding\_mode=None\).
`mindspore.Tensor.trunc`	Returns a new tensor with the truncated integer values of the elements of the input tensor.
`mindspore.Tensor.type`	Change the dtype of the Tensor to the dtype .
`mindspore.Tensor.type_as`	Change the dtype of the Tensor to the dtype of other.
`mindspore.Tensor.unbind`	For details, please refer to `mindspore.ops.unbind()`.
`mindspore.Tensor.unfold`	For details, please refer to `mindspore.ops.unfold()`.
`mindspore.Tensor.uniform`	Generates random numbers in the half-open interval [from_, to).
`mindspore.Tensor.unique_consecutive`	For details, please refer to `mindspore.ops.unique_consecutive()`.
`mindspore.Tensor.unique_with_pad`	For details, please refer to `mindspore.ops.unique_with_pad()`.
`mindspore.Tensor.unsorted_segment_max`	For details, please refer to `mindspore.ops.unsorted_segment_max()`.
`mindspore.Tensor.unsorted_segment_min`	For details, please refer to `mindspore.ops.unsorted_segment_min()`.
`mindspore.Tensor.unsorted_segment_prod`	For details, please refer to `mindspore.ops.unsorted_segment_prod()`.
`mindspore.Tensor.unsqueeze`	For details, please refer to `mindspore.ops.unsqueeze()`.
`mindspore.Tensor.var`	Compute the variance along the specified axis.
`mindspore.Tensor.view`	Reshape the tensor according to the input shape.
`mindspore.Tensor.view_as`	View self Tensor as the same shape as other .
`mindspore.Tensor.where`	Selects elements from input or other based on condition and returns a tensor.
`mindspore.Tensor.xdivy`	For details, please refer to `mindspore.ops.xdivy()`.
`mindspore.Tensor.xlogy`	For details, please refer to `mindspore.ops.xlogy()`.
`mindspore.Tensor.vsplit`	For details, please refer to `mindspore.ops.vsplit()`.