# tensorplay

Source: https://www.tensorplay.cn/docs/tensorplay.html

## 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} |
| 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 |  |