tensorplay

Tensors

`tensorplay.is_tensor`	Returns True if obj is a TensorPlay tensor.
`tensorplay.is_storage`
`tensorplay.functional.is_complex`	Returns True if the data type of `input` is a complex data type i.e., one of `tensorplay.complex64`, and `tensorplay.complex128`.
`tensorplay.functional.is_conj`	Returns True if the `input` is a conjugated tensor, i.e. its conjugate bit is set to True.
`tensorplay.functional.is_floating_point`	Returns True if the data type of `input` is a floating point data type i.e., one of `tensorplay.float64`, `tensorplay.float32`, `tensorplay.float16`, and `tensorplay.bfloat16`.
`tensorplay.functional.is_inference`	Returns True if `input` is an inference tensor.
`tensorplay.functional.is_neg`
`tensorplay.functional.is_nonzero`	Returns True if the `input` is a single element tensor which is not equal to zero after type conversions.
`tensorplay.functional.is_same_size`
`tensorplay.functional.is_signed`
`tensorplay.set_default_dtype`	Sets the default floating point dtype to `d`.
`tensorplay.get_default_dtype`	Get the current default floating point `tensorplay.dtype`.
`tensorplay.set_default_device`	Sets the default `tensorplay.Tensor` to be allocated on `device`.
`tensorplay.get_default_device`	Gets the default `tensorplay.Tensor` to be allocated on `device`
`tensorplay.functional.numel`	Returns the total number of elements in the `input` tensor.
`tensorplay.set_printoptions`	Set printing options for tensor output.

Creation Ops

`tensorplay.tensor`	Constructs a tensor with no autograd history (also known as a "leaf tensor", see Autograd mechanics) by copying `data`.
`tensorplay.functional.sparse_coo_tensor`	Constructs a sparse tensor in COO(rdinate) format with specified values at the given `indices`.
`tensorplay.functional.sparse_csr_tensor`	Constructs a sparse tensor in CSR (Compressed Sparse Row) with specified values at the given `crow_indices` and `col_indices`.
`tensorplay.functional.sparse_csc_tensor`	Constructs a sparse tensor in CSC (Compressed Sparse Column) with specified values at the given `ccol_indices` and `row_indices`.
`tensorplay.functional.sparse_bsr_tensor`	Constructs a sparse tensor in BSR (Block Compressed Sparse Row)) with specified 2-dimensional blocks at the given `crow_indices` and `col_indices`.
`tensorplay.functional.sparse_bsc_tensor`	Constructs a sparse tensor in BSC (Block Compressed Sparse Column)) with specified 2-dimensional blocks at the given `ccol_indices` and `row_indices`.
`tensorplay.asarray`	Converts `obj` to a tensor.
`tensorplay.as_tensor`	Converts `data` into a tensor, sharing data and preserving autograd history if possible.
`tensorplay.functional.as_strided`	Create a view of an existing tensorplay.Tensor `input` with specified `size`, `stride` and `storage_offset`.
`tensorplay.functional.from_file`	Creates a CPU tensor with a storage backed by a memory-mapped file.
`tensorplay.from_numpy`	Creates a `Tensor` from a `numpy.ndarray`.
`tensorplay.from_dlpack`
`tensorplay.frombuffer`	Creates a 1-dimensional `Tensor` from an object that implements the Python buffer protocol.
`tensorplay.zeros`	Returns a tensor filled with the scalar value 0, with the shape defined by the variable argument `size`.
`tensorplay.functional.zeros_like`	Returns a tensor filled with the scalar value 0, with the same size as `input`.
`tensorplay.ones`	Returns a tensor filled with the scalar value 1, with the shape defined by the variable argument `size`.
`tensorplay.functional.ones_like`	Returns a tensor filled with the scalar value 1, with the same size as `input`.
`tensorplay.functional.arange`	Returns a 1-D tensor of size $\left\lceil \frac{\text{end} - \text{start}}{\text{step}} \right\rceil$ with values from the interval `[start, end)` taken with common difference `step` beginning from start.
`tensorplay.functional.range`	Returns a 1-D tensor of size $\left\lfloor \frac{\text{end} - \text{start}}{\text{step}} \right\rfloor + 1$ with values from `start` to `end` with step `step`.
`tensorplay.functional.linspace`	Creates a one-dimensional tensor of size `steps` whose values are evenly spaced from `start` to `end`, inclusive.
`tensorplay.functional.logspace`	Creates a one-dimensional tensor of size `steps` whose values are evenly spaced from ${{\text{{base}}}}^{{\text{{start}}}}$ to ${{\text{{base}}}}^{{\text{{end}}}}$ , inclusive, on a logarithmic scale with base `base`.
`tensorplay.functional.eye`	Returns a 2-D tensor with ones on the diagonal and zeros elsewhere.
`tensorplay.empty`	empty(size, , out=None, dtype=None, layout=tensorplay.strided, device=None, requires_grad=False, pin_memory=False, memory_format=tensorplay.contiguous_format) -> Tensor
`tensorplay.functional.empty_like`	Returns an uninitialized tensor with the same size as `input`.
`tensorplay.functional.empty_permuted`	Creates an uninitialized, non-overlapping and dense tensor with the specified `size`, with `physical_layout` specifying how the dimensions are physically laid out in memory (each logical dimension is listed from outermost to innermost).
`tensorplay.functional.empty_quantized`
`tensorplay.functional.empty_strided`	Creates a tensor with the specified `size` and `stride` and filled with undefined data.
`tensorplay.full`	Creates a tensor of size `size` filled with `fill_value`.
`tensorplay.functional.full_like`	Returns a tensor with the same size as `input` filled with `fill_value`.
`tensorplay.functional.quantize_per_tensor`	Converts a float tensor to a quantized tensor with given scale and zero point.
`tensorplay.functional.quantize_per_tensor_dynamic`	Converts a float tensor to a quantized tensor with scale and zero_point calculated dynamically based on the input.
`tensorplay.functional.quantize_per_channel`	Converts a float tensor to a per-channel quantized tensor with given scales and zero points.
`tensorplay.functional.dequantize`	Returns an fp32 Tensor by dequantizing a quantized Tensor
`tensorplay.functional.complex`	Constructs a complex tensor with its real part equal to `real` and its imaginary part equal to `imag`.
`tensorplay.functional.polar`	Constructs a complex tensor whose elements are Cartesian coordinates corresponding to the polar coordinates with absolute value `abs` and angle `angle`.
`tensorplay.functional.scalar_tensor`
`tensorplay.functional.heaviside`	Computes the Heaviside step function for each element in `input`.

Indexing, Slicing, Joining, Mutating Ops

`tensorplay.functional.adjoint`	Returns a view of the tensor conjugated and with the last two dimensions transposed.
`tensorplay.functional.alias_copy`	Performs the same operation as `tensorplay.alias()`, but all output tensors are freshly created instead of aliasing the input.
`tensorplay.functional.argwhere`	Returns a tensor containing the indices of all non-zero elements of `input`.
`tensorplay.functional.as_strided_copy`	Performs the same operation as `tensorplay.as_strided()`, but all output tensors are freshly created instead of aliasing the input.
`tensorplay.functional.as_strided_scatter`	Embeds the values of the `src` tensor into `input` along the elements corresponding to the result of calling input.as_strided(size, stride, storage_offset).
`tensorplay.functional.cat`	Concatenates the given sequence of tensors in `tensors` in the given dimension.
`tensorplay.functional.ccol_indices_copy`
`tensorplay.functional.col_indices_copy`	Performs the same operation as `tensorplay.col_indices()`, but all output tensors are freshly created instead of aliasing the input.
`tensorplay.functional.concat`	Alias of `tensorplay.cat()`.
`tensorplay.functional.concatenate`	Alias of `tensorplay.cat()`.
`tensorplay.functional.conj`	Returns a view of `input` with a flipped conjugate bit.
`tensorplay.functional.chunk`	Attempts to split a tensor into the specified number of chunks.
`tensorplay.functional.crow_indices_copy`	Performs the same operation as `tensorplay.crow_indices()`, but all output tensors are freshly created instead of aliasing the input.
`tensorplay.functional.detach`
`tensorplay.functional.detach_copy`	Performs the same operation as `tensorplay.detach()`, but all output tensors are freshly created instead of aliasing the input.
`tensorplay.functional.diagonal_copy`	Performs the same operation as `tensorplay.diagonal()`, but all output tensors are freshly created instead of aliasing the input.
`tensorplay.functional.dsplit`	Splits `input`, a tensor with three or more dimensions, into multiple tensors depthwise according to `indices_or_sections`.
`tensorplay.functional.column_stack`	Creates a new tensor by horizontally stacking the tensors in `tensors`.
`tensorplay.functional.dstack`	Stack tensors in sequence depthwise (along third axis).
`tensorplay.functional.expand_copy`	Performs the same operation as `tensorplay.Tensor.expand()`, but all output tensors are freshly created instead of aliasing the input.
`tensorplay.functional.fill`
`tensorplay.functional.gather`	Gathers values along an axis specified by dim.
`tensorplay.functional.hsplit`	Splits `input`, a tensor with one or more dimensions, into multiple tensors horizontally according to `indices_or_sections`.
`tensorplay.functional.hstack`	Stack tensors in sequence horizontally (column wise).
`tensorplay.functional.index_add`	See `index_add_()` for function description.
`tensorplay.functional.index_copy`	See `index_add_()` for function description.
`tensorplay.functional.index_put_`
`tensorplay.functional.index_reduce`	See `index_reduce_()` for function description.
`tensorplay.functional.index_select`	Returns a new tensor which indexes the `input` tensor along dimension `dim` using the entries in `index` which is a LongTensor.
`tensorplay.functional.indices_copy`	Performs the same operation as `tensorplay.indices()`, but all output tensors are freshly created instead of aliasing the input.
`tensorplay.functional.masked_fill`
`tensorplay.functional.masked_select`	Returns a new 1-D tensor which indexes the `input` tensor according to the boolean mask `mask` which is a BoolTensor.
`tensorplay.functional.movedim`	Moves the dimension(s) of `input` at the position(s) in `source` to the position(s) in `destination`.
`tensorplay.functional.moveaxis`	Alias for `tensorplay.movedim()`.
`tensorplay.functional.narrow`	Returns a new tensor that is a narrowed version of `input` tensor.
`tensorplay.functional.narrow_copy`	Same as `Tensor.narrow()` except this returns a copy rather than shared storage.
`tensorplay.functional.nonzero`
`tensorplay.functional.nonzero_static`
`tensorplay.functional.permute`	Returns a view of the original tensor `input` with its dimensions permuted.
`tensorplay.functional.permute_copy`	Performs the same operation as `tensorplay.permute()`, but all output tensors are freshly created instead of aliasing the input.
`tensorplay.functional.put`
`tensorplay.functional.reshape`	Returns a tensor with the same data and number of elements as `input`, but with the specified shape.
`tensorplay.functional.row_indices_copy`
`tensorplay.functional.row_stack`	Alias of `tensorplay.vstack()`.
`tensorplay.functional.select`	Slices the `input` tensor along the selected dimension at the given index.
`tensorplay.functional.select_copy`	Performs the same operation as `tensorplay.select()`, but all output tensors are freshly created instead of aliasing the input.
`tensorplay.functional.scatter`	Out-of-place version of `tensorplay.Tensor.scatter_()`
`tensorplay.functional.diagonal_scatter`	Embeds the values of the `src` tensor into `input` along the diagonal elements of `input`, with respect to `dim1` and `dim2`.
`tensorplay.functional.select_scatter`	Embeds the values of the `src` tensor into `input` at the given index.
`tensorplay.functional.slice_copy`	Performs the same operation as `tensorplay.slice()`, but all output tensors are freshly created instead of aliasing the input.
`tensorplay.functional.slice_inverse`
`tensorplay.functional.slice_scatter`	Embeds the values of the `src` tensor into `input` at the given dimension.
`tensorplay.functional.scatter_add`	Out-of-place version of `tensorplay.Tensor.scatter_add_()`
`tensorplay.functional.scatter_reduce`	Out-of-place version of `tensorplay.Tensor.scatter_reduce_()`
`tensorplay.functional.segment_reduce`	Perform a segment reduction operation on the input tensor along the specified axis.
`tensorplay.functional.split`
`tensorplay.functional.split_copy`	Performs the same operation as `tensorplay.split()`, but all output tensors are freshly created instead of aliasing the input.
`tensorplay.functional.split_with_sizes_copy`	Performs the same operation as `tensorplay.split_with_sizes()`, but all output tensors are freshly created instead of aliasing the input.
`tensorplay.functional.squeeze`	Returns a tensor with all specified dimensions of `input` of size 1 removed.
`tensorplay.functional.squeeze_copy`	Performs the same operation as `tensorplay.squeeze()`, but all output tensors are freshly created instead of aliasing the input.
`tensorplay.functional.stack`	Concatenates a sequence of tensors along a new dimension.
`tensorplay.functional.swapaxes`	Alias for `tensorplay.transpose()`.
`tensorplay.functional.swapdims`	Alias for `tensorplay.transpose()`.
`tensorplay.functional.t`	Expects `input` to be <= 2-D tensor and transposes dimensions 0 and 1.
`tensorplay.functional.t_copy`	Performs the same operation as `tensorplay.t()`, but all output tensors are freshly created instead of aliasing the input.
`tensorplay.functional.take`	Returns a new tensor with the elements of `input` at the given indices.
`tensorplay.functional.take_along_dim`	Selects values from `input` at the 1-dimensional indices from `indices` along the given `dim`.
`tensorplay.functional.tensor_split`	Splits a tensor into multiple sub-tensors, all of which are views of `input`, along dimension `dim` according to the indices or number of sections specified by `indices_or_sections`.
`tensorplay.functional.tile`	Constructs a tensor by repeating the elements of `input`.
`tensorplay.functional.transpose`	Returns a tensor that is a transposed version of `input`.
`tensorplay.functional.transpose_copy`	Performs the same operation as `tensorplay.transpose()`, but all output tensors are freshly created instead of aliasing the input.
`tensorplay.functional.unbind`	Removes a tensor dimension.
`tensorplay.functional.unbind_copy`	Performs the same operation as `tensorplay.unbind()`, but all output tensors are freshly created instead of aliasing the input.
`tensorplay.functional.unfold_copy`	Performs the same operation as `tensorplay.unfold()`, but all output tensors are freshly created instead of aliasing the input.
`tensorplay._shape_funcs.unravel_index`	Converts flat indices into coordinate tuples (one LongTensor per dim).
`tensorplay.functional.unsqueeze`	Returns a new tensor with a dimension of size one inserted at the specified position.
`tensorplay.functional.unsqueeze_copy`	Performs the same operation as `tensorplay.unsqueeze()`, but all output tensors are freshly created instead of aliasing the input.
`tensorplay.functional.values_copy`	Performs the same operation as `tensorplay.values()`, but all output tensors are freshly created instead of aliasing the input.
`tensorplay.functional.view_as_complex_copy`	Performs the same operation as `tensorplay.view_as_complex()`, but all output tensors are freshly created instead of aliasing the input.
`tensorplay.functional.view_as_real_copy`	Performs the same operation as `tensorplay.view_as_real()`, but all output tensors are freshly created instead of aliasing the input.
`tensorplay.functional.view_copy`	Performs the same operation as `tensorplay.view()`, but all output tensors are freshly created instead of aliasing the input.
`tensorplay.functional.vsplit`	Splits `input`, a tensor with two or more dimensions, into multiple tensors vertically according to `indices_or_sections`.
`tensorplay.functional.vstack`	Stack tensors in sequence vertically (row wise).
`tensorplay.functional.where`	Return a tensor of elements selected from either `input` or `other`, depending on `condition`.

Generators

tensorplay.Generator

Creates and returns a generator object that manages the state of the algorithm which produces pseudo random numbers.

Random sampling

`tensorplay.seed`
`tensorplay.manual_seed`
`tensorplay.initial_seed`
`tensorplay.get_rng_state`
`tensorplay.set_rng_state`
`tensorplay.functional.bernoulli`	Draws binary random numbers (0 or 1) from a Bernoulli distribution.
`tensorplay.functional.multinomial`	Returns a tensor where each row contains `num_samples` indices sampled from the multinomial (a stricter definition would be multivariate, refer to `tensorplay.distributions.multinomial.Multinomial` for more details) probability distribution located in the corresponding row of tensor `input`.
`tensorplay.functional.normal`	Returns a tensor of random numbers drawn from separate normal distributions whose mean and standard deviation are given.
`tensorplay.functional.poisson`	Returns a tensor of the same size as `input` with each element sampled from a Poisson distribution with rate parameter given by the corresponding element in `input` i.e.,
`tensorplay.rand`	rand(size, , generator=None, out=None, dtype=None, layout=tensorplay.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor
`tensorplay.functional.rand_like`	Returns a tensor with the same size as `input` that is filled with random numbers from a uniform distribution on the interval $[0, 1)$ .
`tensorplay.functional.randint`	Returns a tensor filled with random integers generated uniformly between `low` (inclusive) and `high` (exclusive).
`tensorplay.functional.randint_like`	Returns a tensor with the same shape as Tensor `input` filled with random integers generated uniformly between `low` (inclusive) and `high` (exclusive).
`tensorplay.randn`	randn(size, , generator=None, out=None, dtype=None, layout=tensorplay.strided, device=None, requires_grad=False, pin_memory=False) -> Tensor
`tensorplay.functional.randn_like`	Returns a tensor with the same size as `input` that is filled with random numbers from a normal distribution with mean 0 and variance 1.
`tensorplay.functional.randperm`	Returns a random permutation of integers from `0` to `n - 1`.

Serialization

`tensorplay.serialization.api.save`
`tensorplay.serialization.api.load`
`tensorplay.serialization.api.default_restore_location`

Parallelism

`tensorplay.get_num_threads`	Returns the number of threads used for parallelizing CPU operations
`tensorplay.set_num_threads`	Sets the number of threads used for intraop parallelism on CPU.

Locally disabling gradient computation

`tensorplay.autograd.grad_mode.no_grad`	Context-manager that disables gradient calculation.
`tensorplay.autograd.grad_mode.enable_grad`	Context-manager that enables gradient calculation.
`tensorplay.autograd.grad_mode.set_grad_enabled`	Context-manager that sets gradient calculation on or off.
`tensorplay.autograd.grad_mode.is_grad_enabled`	Returns True if grad mode is currently enabled.
`tensorplay.autograd.grad_mode.inference_mode`	Context manager that enables or disables inference mode.

Pointwise Ops

`tensorplay.functional.abs`	Computes the absolute value of each element in `input`.
`tensorplay.functional.abs_`
`tensorplay.functional.absolute`	Alias for `tensorplay.abs()`
`tensorplay.functional.acos`	Computes the inverse cosine of each element in `input`.
`tensorplay.functional.acos_`
`tensorplay.functional.arccos`	Alias for `tensorplay.acos()`.
`tensorplay.functional.arccos_`
`tensorplay.functional.acosh`	Returns a new tensor with the inverse hyperbolic cosine of the elements of `input`.
`tensorplay.functional.acosh_`
`tensorplay.functional.arccosh`	Alias for `tensorplay.acosh()`.
`tensorplay.functional.arccosh_`
`tensorplay.functional.add`	Adds `other`, scaled by `alpha`, to `input`.
`tensorplay.functional.addcdiv`	Performs the element-wise division of `tensor1` by `tensor2`, multiplies the result by the scalar `value` and adds it to `input`.
`tensorplay.functional.addcmul`	Performs the element-wise multiplication of `tensor1` by `tensor2`, multiplies the result by the scalar `value` and adds it to `input`.
`tensorplay.functional.angle`	Computes the element-wise angle (in radians) of the given `input` tensor.
`tensorplay.functional.asin`	Returns a new tensor with the arcsine of the elements of `input`.
`tensorplay.functional.asin_`
`tensorplay.functional.arcsin`	Alias for `tensorplay.asin()`.
`tensorplay.functional.arcsin_`
`tensorplay.functional.asinh`	Returns a new tensor with the inverse hyperbolic sine of the elements of `input`.
`tensorplay.functional.asinh_`
`tensorplay.functional.arcsinh`	Alias for `tensorplay.asinh()`.
`tensorplay.functional.arcsinh_`
`tensorplay.functional.atan`	Returns a new tensor with the arctangent of the elements of `input`.
`tensorplay.functional.atan_`
`tensorplay.functional.arctan`	Alias for `tensorplay.atan()`.
`tensorplay.functional.arctan_`
`tensorplay.functional.atanh`	Returns a new tensor with the inverse hyperbolic tangent of the elements of `input`.
`tensorplay.functional.atanh_`
`tensorplay.functional.arctanh`	Alias for `tensorplay.atanh()`.
`tensorplay.functional.arctanh_`
`tensorplay.functional.atan2`	Element-wise arctangent of $\text{input}_{i} / \text{other}_{i}$ with consideration of the quadrant.
`tensorplay.functional.arctan2`	Alias for `tensorplay.atan2()`.
`tensorplay.functional.bitwise_not`	Computes the bitwise NOT of the given input tensor.
`tensorplay.functional.bitwise_and`	Computes the bitwise AND of `input` and `other`.
`tensorplay.functional.bitwise_or`	Computes the bitwise OR of `input` and `other`.
`tensorplay.functional.bitwise_xor`	Computes the bitwise XOR of `input` and `other`.
`tensorplay.functional.bitwise_left_shift`	Computes the left arithmetic shift of `input` by `other` bits.
`tensorplay.functional.bitwise_right_shift`	Computes the right arithmetic shift of `input` by `other` bits.
`tensorplay.functional.ceil`	Returns a new tensor with the ceil of the elements of `input`, the smallest integer greater than or equal to each element.
`tensorplay.functional.ceil_`
`tensorplay.functional.clamp`	Clamps all elements in `input` into the range [ `min`, `max` ].
`tensorplay.functional.clamp_`
`tensorplay.functional.clamp_max_`
`tensorplay.functional.clamp_min_`
`tensorplay.functional.clip`	Alias for `tensorplay.clamp()`.
`tensorplay.functional.clip_`
`tensorplay.functional.conj_physical`	Computes the element-wise conjugate of the given `input` tensor.
`tensorplay.functional.conj_physical_`
`tensorplay.functional.copysign`	Create a new floating-point tensor with the magnitude of `input` and the sign of `other`, elementwise.
`tensorplay.functional.cos`	Returns a new tensor with the cosine of the elements of `input`.
`tensorplay.functional.cos_`
`tensorplay.functional.cosh`	Returns a new tensor with the hyperbolic cosine of the elements of `input`.
`tensorplay.functional.cosh_`
`tensorplay.functional.deg2rad`	Returns a new tensor with each of the elements of `input` converted from angles in degrees to radians.
`tensorplay.functional.deg2rad_`
`tensorplay.functional.div`	Divides each element of the input `input` by the corresponding element of `other`.
`tensorplay.functional.divide`	Alias for `tensorplay.div()`.
`tensorplay.functional.digamma`	Alias for `tensorplay.special.digamma()`.
`tensorplay.functional.erf`	Alias for `tensorplay.special.erf()`.
`tensorplay.functional.erf_`
`tensorplay.functional.erfc`	Alias for `tensorplay.special.erfc()`.
`tensorplay.functional.erfc_`
`tensorplay.functional.erfinv`	Alias for `tensorplay.special.erfinv()`.
`tensorplay.functional.exp`	Returns a new tensor with the exponential of the elements of the input tensor `input`.
`tensorplay.functional.exp_`
`tensorplay.functional.exp2`	Alias for `tensorplay.special.exp2()`.
`tensorplay.functional.exp2_`
`tensorplay.functional.expm1`	Alias for `tensorplay.special.expm1()`.
`tensorplay.functional.expm1_`
`tensorplay.functional.fake_quantize_per_channel_affine`	Returns a new tensor with the data in `input` fake quantized per channel using `scale`, `zero_point`, `quant_min` and `quant_max`, across the channel specified by `axis`.
`tensorplay.functional.fake_quantize_per_tensor_affine`	Returns a new tensor with the data in `input` fake quantized using `scale`, `zero_point`, `quant_min` and `quant_max`.
`tensorplay.functional.fill_`
`tensorplay.functional.fix`	Alias for `tensorplay.trunc()`
`tensorplay.functional.fix_`
`tensorplay.functional.float_power`	Raises `input` to the power of `exponent`, elementwise, in double precision.
`tensorplay.functional.floor`	Returns a new tensor with the floor of the elements of `input`, the largest integer less than or equal to each element.
`tensorplay.functional.floor_`
`tensorplay.functional.floor_divide`
`tensorplay.functional.fmod`	Applies C++'s std::fmod entrywise.
`tensorplay.functional.frac`	Computes the fractional portion of each element in `input`.
`tensorplay.functional.frac_`
`tensorplay.functional.frexp`	Decomposes `input` into mantissa and exponent tensors such that $\text{input} = \text{mantissa} \times 2^{\text{exponent}}$ .
`tensorplay.gradient`	Estimates the gradient of a function $g : \mathbb{R}^n \rightarrow \mathbb{R}$ in one or more dimensions using the second-order accurate central differences method and either first or second order estimates at the boundaries.
`tensorplay.functional.imag`	Returns a new tensor containing imaginary values of the `self` tensor.
`tensorplay.functional.ldexp`	Multiplies `input` by 2 ** `other`.
`tensorplay.functional.ldexp_`
`tensorplay.functional.lerp`	Does a linear interpolation of two tensors `start` (given by `input`) and `end` based on a scalar or tensor `weight` and returns the resulting `out` tensor.
`tensorplay.functional.lgamma`	Computes the natural logarithm of the absolute value of the gamma function on `input`.
`tensorplay.functional.log`	Returns a new tensor with the natural logarithm of the elements of `input`.
`tensorplay.functional.log_`
`tensorplay.functional.log10`	Returns a new tensor with the logarithm to the base 10 of the elements of `input`.
`tensorplay.functional.log10_`
`tensorplay.functional.log1p`	Returns a new tensor with the natural logarithm of (1 + `input`).
`tensorplay.functional.log1p_`
`tensorplay.functional.log2`	Returns a new tensor with the logarithm to the base 2 of the elements of `input`.
`tensorplay.functional.log2_`
`tensorplay.functional.logaddexp`	Logarithm of the sum of exponentiations of the inputs.
`tensorplay.functional.logaddexp2`	Logarithm of the sum of exponentiations of the inputs in base-2.
`tensorplay.functional.logical_and`	Computes the element-wise logical AND of the given input tensors.
`tensorplay.functional.logical_not`	Computes the element-wise logical NOT of the given input tensor.
`tensorplay.functional.logical_or`	Computes the element-wise logical OR of the given input tensors.
`tensorplay.functional.logical_xor`	Computes the element-wise logical XOR of the given input tensors.
`tensorplay.functional.logit`	Alias for `tensorplay.special.logit()`.
`tensorplay.functional.logit_`
`tensorplay.functional.hypot`	Given the legs of a right triangle, return its hypotenuse.
`tensorplay.functional.i0`	Alias for `tensorplay.special.i0()`.
`tensorplay.functional.i0_`
`tensorplay.functional.igamma`	Alias for `tensorplay.special.gammainc()`.
`tensorplay.functional.igammac`	Alias for `tensorplay.special.gammaincc()`.
`tensorplay.functional.mul`	Multiplies `input` by `other`.
`tensorplay.functional.multiply`	Alias for `tensorplay.mul()`.
`tensorplay.functional.mvlgamma`	Alias for `tensorplay.special.multigammaln()`.
`tensorplay.functional.nan_to_num`	Replaces `NaN`, positive infinity, and negative infinity values in `input` with the values specified by `nan`, `posinf`, and `neginf`, respectively.
`tensorplay.functional.nan_to_num_`
`tensorplay.functional.neg`	Returns a new tensor with the negative of the elements of `input`.
`tensorplay.functional.neg_`
`tensorplay.functional.negative`	Alias for `tensorplay.neg()`
`tensorplay.functional.negative_`
`tensorplay.functional.nextafter`	Return the next floating-point value after `input` towards `other`, elementwise.
`tensorplay.functional.polygamma`	Alias for `tensorplay.special.polygamma()`.
`tensorplay.functional.positive`	Returns `input`.
`tensorplay.functional.pow`	Takes the power of each element in `input` with `exponent` and returns a tensor with the result.
`tensorplay.functional.quantized_batch_norm`	Applies batch normalization on a 4D (NCHW) quantized tensor.
`tensorplay.functional.quantized_max_pool1d`	Applies a 1D max pooling over an input quantized tensor composed of several input planes.
`tensorplay.functional.quantized_max_pool2d`	Applies a 2D max pooling over an input quantized tensor composed of several input planes.
`tensorplay.functional.rad2deg`	Returns a new tensor with each of the elements of `input` converted from angles in radians to degrees.
`tensorplay.functional.rad2deg_`
`tensorplay.functional.real`	Returns a new tensor containing real values of the `self` tensor.
`tensorplay.functional.reciprocal`	Returns a new tensor with the reciprocal of the elements of `input`
`tensorplay.functional.reciprocal_`
`tensorplay.functional.remainder`	Computes Python's modulus operation entrywise.
`tensorplay.functional.round`	Rounds elements of `input` to the nearest integer.
`tensorplay.functional.round_`
`tensorplay.functional.rsqrt`	Returns a new tensor with the reciprocal of the square-root of each of the elements of `input`.
`tensorplay.functional.rsqrt_`
`tensorplay.functional.sigmoid`	Alias for `tensorplay.special.expit()`.
`tensorplay.functional.sigmoid_`
`tensorplay.functional.sign`	Returns a new tensor with the signs of the elements of `input`.
`tensorplay.functional.sgn`	This function is an extension of tensorplay.sign() to complex tensors.
`tensorplay.functional.signbit`	Tests if each element of `input` has its sign bit set or not.
`tensorplay.functional.sin`	Returns a new tensor with the sine of the elements of `input`.
`tensorplay.functional.sin_`
`tensorplay.functional.sinc`	Alias for `tensorplay.special.sinc()`.
`tensorplay.functional.sinc_`
`tensorplay.functional.sinh`	Returns a new tensor with the hyperbolic sine of the elements of `input`.
`tensorplay.functional.sinh_`
`tensorplay.functional.softmax`	Alias for `tensorplay.nn.functional.softmax()`.
`tensorplay.functional.sqrt`	Returns a new tensor with the square-root of the elements of `input`.
`tensorplay.functional.sqrt_`
`tensorplay.functional.square`	Returns a new tensor with the square of the elements of `input`.
`tensorplay.functional.square_`
`tensorplay.functional.sub`	Subtracts `other`, scaled by `alpha`, from `input`.
`tensorplay.functional.subtract`	Alias for `tensorplay.sub()`.
`tensorplay.functional.tan`	Returns a new tensor with the tangent of the elements of `input`.
`tensorplay.functional.tan_`
`tensorplay.functional.tanh`	Returns a new tensor with the hyperbolic tangent of the elements of `input`.
`tensorplay.functional.tanh_`
`tensorplay.functional.true_divide`	Alias for `tensorplay.div()` with `rounding_mode=None`.
`tensorplay.functional.trunc`	Returns a new tensor with the truncated integer values of the elements of `input`.
`tensorplay.functional.trunc_`
`tensorplay.functional.xlogy`	Alias for `tensorplay.special.xlogy()`.
`tensorplay.functional.xlogy_`
`tensorplay.functional.zero_`

Reduction Ops

`tensorplay.functional.argmax`	Returns the indices of the maximum value of all elements in the `input` tensor.
`tensorplay.functional.argmin`	Returns the indices of the minimum value(s) of the flattened tensor or along a dimension
`tensorplay.functional.amax`	Returns the maximum value of each slice of the `input` tensor in the given dimension(s) `dim`.
`tensorplay.functional.amin`	Returns the minimum value of each slice of the `input` tensor in the given dimension(s) `dim`.
`tensorplay.functional.aminmax`
`tensorplay.functional.all`	Tests if all elements in `input` evaluate to True.
`tensorplay.functional.any`	Tests if any element in `input` evaluates to True.
`tensorplay.max`	Returns the maximum value of all elements in the `input` tensor.
`tensorplay.min`	Returns the minimum value of all elements in the `input` tensor.
`tensorplay.functional.dist`	Returns the p-norm of (`input` - `other`)
`tensorplay.functional.logsumexp`	Returns the log of summed exponentials of each row of the `input` tensor in the given dimension `dim`.
`tensorplay.functional.mean`
`tensorplay.functional.nanmean`	Computes the mean of all non-NaN elements along the specified dimensions.
`tensorplay.functional.median`
`tensorplay.functional.nanmedian`
`tensorplay.functional.mode`
`tensorplay.functional.norm`
`tensorplay.functional.norm_except_dim`
`tensorplay.functional.nuclear_norm`
`tensorplay.functional.nansum`	Returns the sum of all elements, treating Not a Numbers (NaNs) as zero.
`tensorplay.functional.prod`	Returns the product of all elements in the `input` tensor.
`tensorplay._composite_funcs.quantile`	Computes the q-th quantiles of each row of the `input` tensor along the dimension `dim`.
`tensorplay._composite_funcs.nanquantile`	This is a variant of `tensorplay.quantile()` that "ignores" `NaN` values, computing the quantiles `q` as if `NaN` values in `input` did not exist.
`tensorplay.functional.std`	Calculates the standard deviation over the dimensions specified by `dim`.
`tensorplay.functional.std_mean`	Calculates the standard deviation and mean over the dimensions specified by `dim`.
`tensorplay.functional.sum`	Returns the sum of all elements in the `input` tensor.
`tensorplay.functional.unique`
`tensorplay.functional.unique_consecutive`
`tensorplay.functional.var`	Calculates the variance over the dimensions specified by `dim`.
`tensorplay.functional.var_mean`	Calculates the variance and mean over the dimensions specified by `dim`.
`tensorplay.functional.count_nonzero`	Counts the number of non-zero values in the tensor `input` along the given `dim`.
`tensorplay.functional.hash_tensor`	Returns a hash of all elements in the `input` tensor.

Comparison Ops

`tensorplay.functional.allclose`	This function checks if `input` and `other` satisfy the condition:
`tensorplay.functional.argsort`	Returns the indices that sort a tensor along a given dimension in ascending order by value.
`tensorplay.functional.eq`	Computes element-wise equality
`tensorplay.functional.equal`	`True` if two tensors have the same size and elements, `False` otherwise.
`tensorplay.functional.ge`	Computes $\text{input} \geq \text{other}$ element-wise.
`tensorplay.functional.greater_equal`	Alias for `tensorplay.ge()`.
`tensorplay.functional.gt`	Computes $\text{input} > \text{other}$ element-wise.
`tensorplay.functional.greater`	Alias for `tensorplay.gt()`.
`tensorplay.functional.isclose`	Returns a new tensor with boolean elements representing if each element of `input` is "close" to the corresponding element of `other`.
`tensorplay.functional.isfinite`	Returns a new tensor with boolean elements representing if each element is finite or not.
`tensorplay.functional.isin`	Tests if each element of `elements` is in `test_elements`.
`tensorplay.functional.isinf`	Tests if each element of `input` is infinite (positive or negative infinity) or not.
`tensorplay.functional.isposinf`	Tests if each element of `input` is positive infinity or not.
`tensorplay.functional.isneginf`	Tests if each element of `input` is negative infinity or not.
`tensorplay.functional.isnan`	Returns a new tensor with boolean elements representing if each element of `input` is NaN or not.
`tensorplay.functional.isreal`	Returns a new tensor with boolean elements representing if each element of `input` is real-valued or not.
`tensorplay.functional.kthvalue`
`tensorplay.functional.le`	Computes $\text{input} \leq \text{other}$ element-wise.
`tensorplay.functional.less_equal`	Alias for `tensorplay.le()`.
`tensorplay.functional.lt`	Computes $\text{input} < \text{other}$ element-wise.
`tensorplay.functional.less`	Alias for `tensorplay.lt()`.
`tensorplay.functional.maximum`	Computes the element-wise maximum of `input` and `other`.
`tensorplay.functional.minimum`	Computes the element-wise minimum of `input` and `other`.
`tensorplay.functional.fmax`	Computes the element-wise maximum of `input` and `other`.
`tensorplay.functional.fmin`	Computes the element-wise minimum of `input` and `other`.
`tensorplay.functional.ne`	Computes $\text{input} \neq \text{other}$ element-wise.
`tensorplay.functional.not_equal`	Alias for `tensorplay.ne()`.
`tensorplay.functional.sort`
`tensorplay.functional.topk`
`tensorplay.functional.msort`	Sorts the elements of the `input` tensor along its first dimension in ascending order by value.

Spectral Ops

`tensorplay.functional.stft`
`tensorplay.functional.istft`
`tensorplay.functional.bartlett_window`	Bartlett window function.
`tensorplay.functional.blackman_window`	Blackman window function.
`tensorplay.functional.hamming_window`	Hamming window function.
`tensorplay.functional.hann_window`	Hann window function.
`tensorplay.functional.kaiser_window`	Computes the Kaiser window with window length `window_length` and shape parameter `beta`.

Other Operations

`tensorplay.functional.adaptive_avg_pool1d`
`tensorplay.functional.adaptive_max_pool1d`
`tensorplay.functional.affine_grid_generator`
`tensorplay.functional.alpha_dropout`
`tensorplay.functional.alpha_dropout_`
`tensorplay.functional.as_strided_`
`tensorplay.functional.atleast_1d`
`tensorplay.functional.atleast_2d`
`tensorplay.functional.atleast_3d`
`tensorplay.functional.avg_pool1d`
`tensorplay.functional.batch_norm_backward_elemt`
`tensorplay.functional.batch_norm_backward_reduce`
`tensorplay.functional.batch_norm_elemt`
`tensorplay.functional.batch_norm_gather_stats`
`tensorplay.functional.batch_norm_gather_stats_with_counts`
`tensorplay.functional.batch_norm_stats`
`tensorplay.functional.batch_norm_update_stats`
`tensorplay.functional.bilinear`
`tensorplay.functional.bincount`	Count the frequency of each value in an array of non-negative ints.
`tensorplay.functional.binomial`
`tensorplay._shape_funcs.block_diag`	Builds a block diagonal matrix from the given blocks.
`tensorplay._shape_funcs.broadcast_tensors`	Broadcasts the given tensors to a common shape.
`tensorplay.functional.broadcast_to`	Broadcasts `input` to the shape `shape`.
`tensorplay._shape_funcs.broadcast_shapes`	Returns the broadcast shape of the given shapes (right-aligned).
`tensorplay.functional.bucketize`	Returns the indices of the buckets to which each value in the `input` belongs, where the boundaries of the buckets are set by `boundaries`.
`tensorplay.functional.cartesian_prod`
`tensorplay.functional.cdist`
`tensorplay.functional.celu_`
`tensorplay.functional.channel_shuffle`
`tensorplay.functional.choose_qparams_optimized`
`tensorplay.functional.clone`	Returns a copy of `input`.
`tensorplay.functional.combinations`	Compute combinations of length $r$ of the given tensor.
`tensorplay.functional.conv1d`
`tensorplay.functional.conv3d`
`tensorplay.functional.conv_tbc`
`tensorplay.functional.conv_transpose1d`
`tensorplay.functional.conv_transpose2d`
`tensorplay.functional.conv_transpose3d`
`tensorplay.functional.convolution`
`tensorplay.functional.corrcoef`	Estimates the Pearson product-moment correlation coefficient matrix of the variables given by the `input` matrix, where rows are the variables and columns are the observations.
`tensorplay.functional.cosine_embedding_loss`
`tensorplay.functional.cosine_similarity`
`tensorplay.functional.cov`	Estimates the covariance matrix of the variables given by the `input` matrix, where rows are the variables and columns are the observations.
`tensorplay.functional.cross`	Returns the cross product of vectors in dimension `dim` of `input` and `other`.
`tensorplay.functional.ctc_loss`
`tensorplay.functional.cudnn_affine_grid_generator`
`tensorplay.functional.cudnn_batch_norm`
`tensorplay.functional.cudnn_convolution`
`tensorplay.functional.cudnn_convolution_add_relu`
`tensorplay.functional.cudnn_convolution_relu`
`tensorplay.functional.cudnn_convolution_transpose`
`tensorplay.functional.cudnn_grid_sampler`
`tensorplay.functional.cudnn_is_acceptable`
`tensorplay.functional.cummax`
`tensorplay.functional.cummin`
`tensorplay.functional.cumprod`	Returns the cumulative product of elements of `input` in the dimension `dim`.
`tensorplay.functional.cumsum`	Returns the cumulative sum of elements of `input` in the dimension `dim`.
`tensorplay.functional.detach_`
`tensorplay.functional.diag`
`tensorplay.functional.diag_embed`	Creates a tensor whose diagonals of certain 2D planes (specified by `dim1` and `dim2`) are filled by `input`.
`tensorplay.functional.diagflat`
`tensorplay.functional.diagonal`	Returns a partial view of `input` with the its diagonal elements with respect to `dim1` and `dim2` appended as a dimension at the end of the shape.
`tensorplay.functional.diff`	Computes the n-th forward difference along the given dimension.
`tensorplay.functional.dropout_`
`tensorplay._einsum.einsum`	Sums the product of the elements of the input `operands` along dimensions specified using a notation based on the Einstein summation convention, e.g. `tp.einsum("ij,jk->ik", A, B)` computes a matrix multiplication.
`tensorplay.functional.embedding`
`tensorplay.functional.embedding_renorm_`
`tensorplay.functional.fbgemm_linear_fp16_weight`
`tensorplay.functional.fbgemm_linear_fp16_weight_fp32_activation`
`tensorplay.functional.fbgemm_linear_int8_weight`
`tensorplay.functional.fbgemm_linear_int8_weight_fp32_activation`
`tensorplay.functional.fbgemm_linear_quantize_weight`
`tensorplay.functional.fbgemm_pack_gemm_matrix_fp16`
`tensorplay.functional.fbgemm_pack_quantized_matrix`
`tensorplay.functional.feature_alpha_dropout`
`tensorplay.functional.feature_alpha_dropout_`
`tensorplay.functional.feature_dropout`
`tensorplay.functional.feature_dropout_`
`tensorplay.functional.flatten`	Flattens `input` by reshaping it into a one-dimensional tensor.
`tensorplay.functional.flip`	Reverse the order of an n-D tensor along given axis in dims.
`tensorplay.functional.fliplr`	Flip tensor in the left/right direction, returning a new tensor.
`tensorplay.functional.flipud`	Flip tensor in the up/down direction, returning a new tensor.
`tensorplay.functional.fused_moving_avg_obs_fake_quant`
`tensorplay.functional.gcd`	Computes the element-wise greatest common divisor (GCD) of `input` and `other`.
`tensorplay.functional.gcd_`
`tensorplay.functional.grid_sampler_2d`
`tensorplay.functional.grid_sampler_3d`
`tensorplay.functional.group_norm`
`tensorplay.functional.gru`
`tensorplay.functional.gru_cell`
`tensorplay.functional.hardshrink`
`tensorplay.functional.hinge_embedding_loss`
`tensorplay.functional.histc`	Computes the histogram of a tensor.
`tensorplay._composite_funcs.histogram`	Computes a histogram of the values in a tensor.
`tensorplay.functional.histogramdd`	Computes a multi-dimensional histogram of the values in a tensor.
`tensorplay.functional.instance_norm`
`tensorplay.functional.int_repr`
`tensorplay.functional.kl_div`
`tensorplay.functional.kron`	Computes the Kronecker product, denoted by $\otimes$ , of `input` and `other`.
`tensorplay.functional.lcm`	Computes the element-wise least common multiple (LCM) of `input` and `other`.
`tensorplay.functional.lcm_`
`tensorplay.functional.logcumsumexp`	Returns the logarithm of the cumulative summation of the exponentiation of elements of `input` in the dimension `dim`.
`tensorplay.functional.lstm`
`tensorplay.functional.lstm_cell`
`tensorplay.functional.margin_ranking_loss`
`tensorplay.functional.max_pool1d`
`tensorplay.functional.max_pool3d`
`tensorplay.functional.meshgrid`
`tensorplay.functional.miopen_batch_norm`
`tensorplay.functional.miopen_convolution`
`tensorplay.functional.miopen_convolution_add_relu`
`tensorplay.functional.miopen_convolution_relu`
`tensorplay.functional.miopen_convolution_transpose`
`tensorplay.functional.miopen_ctc_loss`
`tensorplay.functional.miopen_depthwise_convolution`
`tensorplay.functional.miopen_rnn`
`tensorplay.functional.mkldnn_adaptive_avg_pool2d`
`tensorplay.functional.mkldnn_convolution`
`tensorplay.functional.mkldnn_linear_backward_weights`
`tensorplay.functional.mkldnn_max_pool2d`
`tensorplay.functional.mkldnn_max_pool3d`
`tensorplay.functional.mkldnn_rnn_layer`
`tensorplay.functional.native_batch_norm`
`tensorplay.functional.native_channel_shuffle`
`tensorplay.functional.native_group_norm`
`tensorplay.functional.native_layer_norm`
`tensorplay.functional.native_norm`
`tensorplay.functional.pairwise_distance`
`tensorplay.functional.pdist`
`tensorplay.functional.pixel_unshuffle`
`tensorplay.functional.poisson_nll_loss`
`tensorplay.functional.prelu`
`tensorplay.functional.q_per_channel_axis`
`tensorplay.functional.q_per_channel_scales`
`tensorplay.functional.q_per_channel_zero_points`
`tensorplay.functional.q_scale`
`tensorplay.functional.q_zero_point`
`tensorplay.functional.quantized_gru_cell`
`tensorplay.functional.quantized_lstm_cell`
`tensorplay.functional.quantized_max_pool3d`
`tensorplay.functional.quantized_rnn_relu_cell`
`tensorplay.functional.quantized_rnn_tanh_cell`
`tensorplay.functional.ravel`	Return a contiguous flattened tensor.
`tensorplay.functional.relu_`
`tensorplay.functional.renorm`	Returns a tensor where each sub-tensor of `input` along dimension `dim` is normalized such that the p-norm of the sub-tensor is lower than the value `maxnorm`
`tensorplay.functional.repeat_interleave`	Repeat elements of a tensor.
`tensorplay.functional.resize_as_`
`tensorplay.functional.resize_as_sparse_`
`tensorplay.functional.rms_norm`
`tensorplay.functional.rnn_relu`
`tensorplay.functional.rnn_relu_cell`
`tensorplay.functional.rnn_tanh`
`tensorplay.functional.rnn_tanh_cell`
`tensorplay.functional.roll`	Roll the tensor `input` along the given dimension(s).
`tensorplay.functional.rot90`	Rotate an n-D tensor by 90 degrees in the plane specified by dims axis.
`tensorplay.functional.rrelu`
`tensorplay.functional.rrelu_`
`tensorplay.functional.rsub`
`tensorplay.functional.searchsorted`	Find the indices from the innermost dimension of `sorted_sequence` such that, if the corresponding values in `values` were inserted before the indices, when sorted, the order of the corresponding innermost dimension within `sorted_sequence` would be preserved.
`tensorplay.functional.selu`
`tensorplay.functional.selu_`
`tensorplay._shape_funcs.tensordot`	Contracts `input` and `other` over the given dimensions.
`tensorplay.functional.threshold`
`tensorplay.functional.threshold_`
`tensorplay.functional.trace`	Returns the sum of the elements of the diagonal of the input 2-D matrix.
`tensorplay.functional.tril`	Returns the lower triangular part of the matrix (2-D tensor) or batch of matrices `input`, the other elements of the result tensor `out` are set to 0.
`tensorplay.functional.tril_indices`	Returns the indices of the lower triangular part of a `row`-by- `col` matrix in a 2-by-N Tensor, where the first row contains row coordinates of all indices and the second row contains column coordinates.
`tensorplay.functional.triu`	Returns the upper triangular part of a matrix (2-D tensor) or batch of matrices `input`, the other elements of the result tensor `out` are set to 0.
`tensorplay.functional.triu_indices`	Returns the indices of the upper triangular part of a `row` by `col` matrix in a 2-by-N Tensor, where the first row contains row coordinates of all indices and the second row contains column coordinates.
`tensorplay.functional.triplet_margin_loss`
`tensorplay.functional.unflatten`	Expands a dimension of the input tensor over multiple dimensions.
`tensorplay.functional.vander`	Generates a Vandermonde matrix.
`tensorplay.functional.view_as_real`	Returns a view of `input` as a real tensor.
`tensorplay.functional.view_as_complex`	Returns a view of `input` as a complex tensor.
`tensorplay.functional.resolve_conj`	Returns a new tensor with materialized conjugation if `input`'s conjugate bit is set to True, else returns `input`.
`tensorplay.functional.resolve_neg`	Returns a new tensor with materialized negation if `input`'s negative bit is set to True, else returns `input`.

BLAS and LAPACK Operations

`tensorplay.functional.addbmm`	Performs a batch matrix-matrix product of matrices stored in `batch1` and `batch2`, with a reduced add step (all matrix multiplications get accumulated along the first dimension).
`tensorplay.functional.addmm`	Performs a matrix multiplication of the matrices `mat1` and `mat2`.
`tensorplay.functional.addmv`	Performs a matrix-vector product of the matrix `mat` and the vector `vec`.
`tensorplay.functional.addmv_`
`tensorplay.functional.addr`	Performs the outer-product of vectors `vec1` and `vec2` and adds it to the matrix `input`.
`tensorplay.functional.baddbmm`	Performs a batch matrix-matrix product of matrices in `batch1` and `batch2`.
`tensorplay.functional.bmm`	Performs a batch matrix-matrix product of matrices stored in `input` and `mat2`.
`tensorplay.functional.chain_matmul`
`tensorplay.functional.cholesky_inverse`	Computes the inverse of a complex Hermitian or real symmetric positive-definite matrix given its Cholesky decomposition.
`tensorplay.functional.cholesky_solve`	Computes the solution of a system of linear equations with complex Hermitian or real symmetric positive-definite lhs given its Cholesky decomposition.
`tensorplay.functional.dot`	Computes the dot product of two 1D tensors.
`tensorplay.functional.geqrf`	This is a low-level function for calling LAPACK's geqrf directly.
`tensorplay.functional.ger`	Alias of `tensorplay.outer()`.
`tensorplay.functional.inner`	Computes the dot product for 1D tensors.
`tensorplay.functional.inverse`	Alias for `tensorplay.linalg.inv()`
`tensorplay.functional.det`	Alias for `tensorplay.linalg.det()`
`tensorplay.functional.logdet`	Calculates log determinant of a square matrix or batches of square matrices.
`tensorplay.functional.slogdet`	Alias for `tensorplay.linalg.slogdet()`
`tensorplay.functional.lu_solve`	Returns the LU solve of the linear system $Ax = b$ using the partially pivoted LU factorization of A from `lu_factor()`.
`tensorplay.functional.lu_unpack`	Unpacks the LU decomposition returned by `lu_factor()` into the P, L, U matrices.
`tensorplay.functional.matmul`	Matrix product of two tensors.
`tensorplay.functional.matrix_power`	Alias for `tensorplay.linalg.matrix_power()`
`tensorplay.functional.matrix_exp`	Alias for `tensorplay.linalg.matrix_exp()`.
`tensorplay.functional.mm`	Performs a matrix multiplication of the matrices `input` and `mat2`.
`tensorplay.functional.mv`	Performs a matrix-vector product of the matrix `input` and the vector `vec`.
`tensorplay.functional.orgqr`	Alias for `tensorplay.linalg.householder_product()`.
`tensorplay.functional.ormqr`	Computes the matrix-matrix multiplication of a product of Householder matrices with a general matrix.
`tensorplay.functional.outer`	Outer product of `input` and `vec2`.
`tensorplay.functional.pinverse`	Alias for `tensorplay.linalg.pinv()`
`tensorplay.functional.svd`	Computes the singular value decomposition of either a matrix or batch of matrices `input`.
`tensorplay.functional.trapz`	Alias for `tensorplay.trapezoid()`.
`tensorplay.functional.trapezoid`	Computes the trapezoidal rule along `dim`.
`tensorplay.functional.cumulative_trapezoid`	Cumulatively computes the trapezoidal rule along `dim`.
`tensorplay.functional.triangular_solve`	Solves a system of equations with a square upper or lower triangular invertible matrix $A$ and multiple right-hand sides $b$ .
`tensorplay.functional.vdot`	Computes the dot product of two 1D vectors along a dimension.

Utilities

`tensorplay.autocast_decrement_nesting`
`tensorplay.autocast_increment_nesting`
`tensorplay.clear_autocast_cache`
`tensorplay.amp.autocast_mode.get_autocast_cpu_dtype`	Return the dtype to be used for CPU autocasting.
`tensorplay.get_autocast_dtype`
`tensorplay.amp.autocast_mode.get_autocast_gpu_dtype`	Return the dtype to be used for CUDA autocasting.
`tensorplay.functional.get_device`
`tensorplay.is_autocast_cache_enabled`
`tensorplay.is_autocast_enabled`
`tensorplay.functional.is_distributed`
`tensorplay.functional.is_vulkan_available`
`tensorplay.functional.result_type`	Returns the `tensorplay.dtype` that would result from performing an arithmetic operation on the provided input tensors.
`tensorplay.functional.can_cast`	described in the type promotion documentation.
`tensorplay.functional.promote_types`	Returns the `tensorplay.dtype` with the smallest size and scalar kind that is not smaller nor of lower kind than either type1 or type2.
`tensorplay.set_autocast_cache_enabled`
`tensorplay.set_autocast_dtype`
`tensorplay.set_autocast_enabled`
`tensorplay.use_deterministic_algorithms`	Sets whether TensorPlay operations must use "deterministic" algorithms.
`tensorplay.are_deterministic_algorithms_enabled`	Returns True if the global deterministic flag is turned on.
`tensorplay.is_deterministic_algorithms_warn_only_enabled`	Returns True if the global deterministic flag is set to warn only.
`tensorplay.set_deterministic_debug_mode`	Sets the debug mode for deterministic operations.
`tensorplay.get_deterministic_debug_mode`	Returns the current value of the debug mode for deterministic operations.
`tensorplay.set_float32_matmul_precision`	Sets the internal precision of float32 matrix multiplications.
`tensorplay.get_float32_matmul_precision`	Returns the current value of float32 matrix multiplication precision.
`tensorplay.typename`	String representation of the type of an object.

Symbolic Numbers

`tensorplay.SymInt`
`tensorplay.SymFloat`
`tensorplay.SymBool`
`tensorplay.functional.sym_constrain_range`
`tensorplay.functional.sym_constrain_range_for_size`
`tensorplay.sym_float`
`tensorplay.sym_int`
`tensorplay.sym_max`
`tensorplay.sym_min`
`tensorplay.sym_not`
`tensorplay.sym_ite`
`tensorplay.sym_sqrt`
`tensorplay.sym_sum`

Optimizations

tensorplay._stax.api.compile

Compile a callable through the TensorPlay compiler frontend.

Operator Tags

tensorplay.serialization.StorageType

TensorPlay-specific additions

`DType`	Members:
`Device`
`DeviceMismatchError`
`DeviceType`	Members:
`DispatchKey`	Dispatch keys of the runtime, in dispatch-priority order: autocast keys outrank autograd keys, which outrank backend keys.
`FileCheck`
`Future`
`GradScaler`	An instance `scaler` of `GradScaler`.
`Layout`	`tensor.layout` returns one of these values; dense tensors report STRIDED.
`MemoryFormat`
`Node`
`QScheme`	Quantization scheme attached to a quantized tensor's quantizer.
`Scalar`
`Size`	Sequence of tensor dimension sizes (tuple subclass).
`Tensor`
`UntypedStorage`
`absolute_`
`adaptive_avg_pool2d`
`adaptive_avg_pool3d`
`adaptive_max_pool2d`
`adaptive_max_pool2d_with_indices`
`adaptive_max_pool3d`
`add_`
`add_relu`
`addbmm_`
`addcdiv_`
`addcmul_`
`addmm_`
`addr_`
`airy_ai`
`alias`
`arctan2_`
`atan2_`
`autocast`	Instances of `autocast` serve as context managers or decorators that allow regions of your script to run in mixed precision.
`avg_pool2d`
`avg_pool3d`
`baddbmm_`
`batch_norm`
`bernoulli_`
`bessel_j0`
`bessel_j1`
`bessel_y0`
`bessel_y1`
`binary_cross_entropy`
`binary_cross_entropy_with_logits`
`bitwise_and_`
`bitwise_left_shift_`
`bitwise_not_`
`bitwise_or_`
`bitwise_right_shift_`
`bitwise_xor_`
`cauchy_`
`ccol_indices`
`celu`
`chebyshev_polynomial_t`
`chebyshev_polynomial_u`
`chebyshev_polynomial_v`
`chebyshev_polynomial_w`
`cholesky`	Computes the Cholesky decomposition of a symmetric positive-definite matrix $A$ or for batches of symmetric positive-definite matrices.
`circular_pad_nd`
`clamp_max`
`clamp_min`
`coalesce`
`col2im`
`col_indices`
`constant_pad_nd`
`contiguous`
`conv1d_grad_bias`
`conv1d_grad_input`
`conv1d_grad_weight`
`conv2d`
`conv2d_grad_bias`
`conv2d_grad_input`
`conv2d_grad_weight`
`conv2d_relu`
`conv3d_grad_bias`
`conv3d_grad_input`
`conv3d_grad_weight`
`conv_depthwise3d`
`conv_transpose1d_grad_bias`
`conv_transpose1d_grad_input`
`conv_transpose1d_grad_weight`
`conv_transpose2d_grad_bias`
`conv_transpose2d_grad_input`
`conv_transpose2d_grad_weight`
`conv_transpose3d_grad_bias`
`conv_transpose3d_grad_input`
`conv_transpose3d_grad_weight`
`convolution_backward_overrideable`
`convolution_overrideable`
`copy`
`copy_`
`copy_sparse_to_sparse_`
`copysign_`
`cross_entropy_loss`
`crow_indices`
`cumprod_`
`cumsum_`
`custom_bwd`	Create a helper decorator for backward methods of custom autograd functions.
`custom_fwd`	Create a helper decorator for `forward` methods of custom autograd functions.
`data`
`dense_dim`
`digamma_`
`dim`
`div_`
`divide_`
`dropout`
`elu`
`elu_`
`embedding_bag`
`entr`
`eq_`
`erfcx`
`erfinv_`
`expand`
`expand_as`
`exponential_`
`fake_quantize_per_channel_affine_cachemask`
`fake_quantize_per_tensor_affine_cachemask`
`fft_fft`
`fft_fft2`
`fft_fftfreq`
`fft_fftn`
`fft_fftshift`
`fft_hfft`
`fft_hfft2`
`fft_hfftn`
`fft_ifft`
`fft_ifft2`
`fft_ifftn`
`fft_ifftshift`
`fft_ihfft`
`fft_ihfft2`
`fft_ihfftn`
`fft_irfft`
`fft_irfft2`
`fft_irfftn`
`fft_rfft`
`fft_rfft2`
`fft_rfftfreq`
`fft_rfftn`
`fill_diagonal_`
`finfo`
`flatten_dense_tensors`
`float_power_`
`floor_divide_`
`fmod_`
`fork_rng`	Forks the RNG state: code inside the context gets a pristine RNG.
`forward_add`
`forward_cos`
`forward_div`
`forward_exp`
`forward_log`
`forward_mm`
`forward_mul`
`forward_neg`
`forward_pow`
`forward_relu`
`forward_sigmoid`
`forward_sin`
`forward_sqrt`
`forward_sub`
`forward_tanh`
`fractional_max_pool2d`
`fractional_max_pool3d`
`frobenius_norm`
`fused_mul_add`
`fused_rope`
`fused_swiglu`
`gammainc`
`gammaincc`
`ge_`
`gelu`
`gelu_`
`geometric_`
`get_parallel_info`	Returns a detailed string describing parallelization settings
`get_thread_num`	Returns the current thread number (starting from 0) in the current parallel region, or 0 in the sequential region
`glu`
`glu_backward_jvp`
`glu_jvp`
`greater_`
`greater_equal_`
`grid_sampler`
`grouped_mm`
`gt_`
`hardsigmoid`
`hardsigmoid_`
`hardswish`
`hardswish_`
`hardtanh`
`hardtanh_`
`heaviside_`
`hermite_polynomial_h`
`hermite_polynomial_he`
`hspmm`	Performs a matrix multiplication of a sparse COO matrix `mat1` and a strided matrix `mat2`.
`huber_loss`
`hypot_`
`i0e`
`i1`
`i1e`
`igamma_`
`igammac_`
`iinfo`
`im2col`
`in_parallel_region`	Checks whether the code runs in a parallel region
`index`
`index_add_`
`index_copy_`
`index_fill`
`index_fill_`
`index_put`
`index_reduce_`
`indices`
`inner_backward_other`
`inner_backward_self`
`inspect_checkpoint`
`is_autocast_available`	Return a bool indicating if autocast is available on `device_type`.
`is_coalesced`
`is_contiguous`
`is_leaf`
`is_pinned`
`is_quantized`
`is_set_to`
`item`
`l1_loss`
`laguerre_polynomial_l`
`layer_norm`
`le_`
`leaky_relu`
`leaky_relu_`
`legendre_polynomial_p`
`lerp_`
`less_`
`less_equal_`
`lgamma_`
`lift`
`lift_fresh`
`lift_fresh_copy`
`linear`
`log_ndtr`
`log_normal_`
`log_sigmoid`
`log_sigmoid_forward`
`log_softmax`
`logical_and_`
`logical_not_`
`logical_or_`
`logical_xor_`
`lt_`
`mH`
`mT`
`masked_fill_`
`masked_scatter`
`masked_scatter_`
`matmul_backward_other`
`matmul_backward_self`
`matrix_H`
`max_pool1d_with_indices`
`max_pool2d`
`max_pool2d_with_indices`
`max_pool3d_with_indices`
`max_unpool2d`
`max_unpool3d`
`mish`
`mish_`
`mkldnn_linear`
`mkldnn_linear_backward_input`
`mkldnn_reorder_conv2d_weight`
`mkldnn_reorder_conv3d_weight`
`modified_bessel_i0`
`modified_bessel_i1`
`modified_bessel_k0`
`modified_bessel_k1`
`mse_loss`
`mul_`
`multi_margin_loss`
`multilabel_margin_loss`
`multilabel_margin_loss_forward`
`multilabel_soft_margin_loss`
`multiply_`
`mvlgamma_`
`native_alpha_dropout`
`native_dropout`
`native_feature_dropout`
`ndtr`
`ndtri`
`ne_`
`nested_to_padded_tensor`
`new_empty`
`new_empty_strided`
`new_full`
`new_ones`
`new_zeros`
`nextafter_`
`nll_loss`
`nll_loss2d`
`nll_loss2d_forward`
`nll_loss_forward`
`nll_loss_nd`
`nonzero_numpy`
`normal_`
`normal_functional`
`not_equal_`
`numpy_T`
`one_hot`
`output_nr`
`pad`
`pad_sequence`
`pin_memory`
`pixel_shuffle`
`polygamma_`
`pow_`
`put_`
`qscheme`
`quantized_add`
`quantized_clamp`
`quantized_conv2d`
`quantized_conv2d_prepack`
`quantized_conv2d_run`
`quantized_conv2d_unpack`
`quantized_div`
`quantized_linear`
`quantized_mul`
`quantized_sub`
`random_`
`reflection_pad1d`
`reflection_pad2d`
`reflection_pad3d`
`reflection_pad_nd`
`relu`
`relu6`
`relu6_`
`remainder_`
`renorm_`
`repeat`
`replication_pad1d`
`replication_pad2d`
`replication_pad3d`
`replication_pad_nd`
`requires_grad_`
`reshape_as`
`resize_`
`retain_grad`
`retains_grad`
`rotary_embedding`
`row_indices`
`rrelu_with_noise`
`rrelu_with_noise_`
`sample`
`scaled_dot_product_attention`
`scaled_modified_bessel_k0`
`scaled_modified_bessel_k1`
`scatter_`
`scatter_add_`
`scatter_reduce_`
`set_`
`set_data`
`sgn_`
`shifted_chebyshev_polynomial_t`
`shifted_chebyshev_polynomial_u`
`shifted_chebyshev_polynomial_v`
`shifted_chebyshev_polynomial_w`
`sign_`
`silu`
`silu_`
`silu_and_mul`
`silu_mul`
`size`
`slice`
`slow_conv3d`
`slow_conv3d_forward`
`slow_conv_dilated2d`
`slow_conv_dilated3d`
`slow_conv_transpose2d`
`slow_conv_transpose3d`
`smm`	Performs a matrix multiplication of the sparse matrix `input` with the dense matrix `mat`.
`smooth_l1_loss`
`soft_margin_loss`
`softplus`
`softshrink`
`sparse_add`
`sparse_compressed_tensor`	Constructs a sparse tensor in Compressed Sparse format - CSR, CSC, BSR, or BSC - with specified values at the given `compressed_indices` and `plain_indices`.
`sparse_dim`
`sparse_mask`
`sparse_mm`
`sparse_mul`
`sparse_resize_`
`sparse_resize_and_clear_`
`sparse_sampled_addmm`
`sparse_sum`
`spdiags`
`special_airy_ai`
`special_bessel_j0`
`special_bessel_j1`
`special_bessel_y0`
`special_bessel_y1`
`special_chebyshev_polynomial_t`
`special_chebyshev_polynomial_u`
`special_chebyshev_polynomial_v`
`special_chebyshev_polynomial_w`
`special_digamma`
`special_entr`
`special_erf`
`special_erfc`
`special_erfcx`
`special_erfinv`
`special_exp2`
`special_expit`
`special_expm1`
`special_gammainc`
`special_gammaincc`
`special_gammaln`
`special_hermite_polynomial_h`
`special_hermite_polynomial_he`
`special_i0`
`special_i0e`
`special_i1`
`special_i1e`
`special_laguerre_polynomial_l`
`special_legendre_polynomial_p`
`special_log1p`
`special_log_ndtr`
`special_log_softmax`
`special_logit`
`special_logsumexp`
`special_modified_bessel_i0`
`special_modified_bessel_i1`
`special_modified_bessel_k0`
`special_modified_bessel_k1`
`special_multigammaln`
`special_ndtr`
`special_ndtri`
`special_polygamma`
`special_psi`
`special_round`
`special_scaled_modified_bessel_k0`
`special_scaled_modified_bessel_k1`
`special_shifted_chebyshev_polynomial_t`
`special_shifted_chebyshev_polynomial_u`
`special_shifted_chebyshev_polynomial_v`
`special_shifted_chebyshev_polynomial_w`
`special_sinc`
`special_softmax`
`special_spherical_bessel_j0`
`special_xlog1py`
`special_xlogy`
`special_zeta`
`spherical_bessel_j0`
`split_with_sizes`
`squeeze_`
`sspaddmm`	Matrix multiplies a sparse tensor `mat1` with a dense tensor `mat2`, then adds the sparse tensor `input` to the result.
`storage_offset`
`stride`
`sub_`
`subtract_`
`sum_to_size`
`swapaxes_`
`swapdims_`
`sym_is_contiguous`
`sym_numel`
`sym_size`
`sym_storage_offset`
`sym_stride`
`t_`
`thnn_conv2d`
`to`
`to_dense`
`to_mkldnn`
`to_padded_tensor`
`to_sparse`
`to_sparse_bsc`
`to_sparse_bsr`
`to_sparse_csc`
`to_sparse_csr`
`tp_cosine_embedding_loss`
`tp_hinge_embedding_loss`
`tp_kl_div`
`tp_l1_loss`
`tp_margin_ranking_loss`
`tp_poisson_nll_loss`
`tp_soft_margin_loss`
`transpose_`
`tril_`
`triu_`
`true_divide_`
`type_as`
`unflatten_dense_tensors`
`unfold`
`uniform_`
`unique_dim`
`unique_dim_consecutive`
`unsafe_chunk`	Works like `tensorplay.chunk()` but without enforcing the autograd restrictions on inplace modification of the outputs.
`unsafe_split`	Works like `tensorplay.split()` but without enforcing the autograd restrictions on inplace modification of the outputs.
`unsafe_split_with_sizes`
`unsqueeze_`
`upsample_bicubic2d`
`upsample_bilinear2d`
`upsample_linear1d`
`upsample_nearest1d`
`upsample_nearest2d`
`upsample_nearest3d`
`upsample_trilinear3d`
`values`
`view`
`view_as`
`xlog1py`
`xlog1py_`
`zeta`

tensorplay

Tensors

Creation Ops

Indexing, Slicing, Joining, Mutating Ops

Generators

Random sampling

Serialization

Parallelism

Locally disabling gradient computation

Pointwise Ops

Reduction Ops

Comparison Ops

Spectral Ops

Other Operations

BLAS and LAPACK Operations

Utilities

Symbolic Numbers

Optimizations

Operator Tags

TensorPlay-specific additions

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