This operator is used to add two tensors.


Cast Operator casts the given input tensor into the dtype given in the parameter.


Two tensors are concatenetaed on a specific dimension or axis using this operator.


This operator is used to divide a tensor from another.

Expand Dims

Expand Dims Operator expands the dimensions of the input tensor at the given axis.


This is basically a copy operator. The input tensor is copied as N output tensors.


Performs Matrix Multiplication Operation.


This operator is used for multiplication of two tensors.


Reduce Operator reduces the input tensor based on the given action one among MEAN, MIN, MAX, SUM, ARGMAX at a given axis.


This operator performs the reshape on a given tensor to the new shape specified.


This operation extracts a slice of size from a tensor input starting from the specified begin location.


This operator splits a tensor into sub tensors on a specified axis. This operation is inverse of Concatenation.


Squeeze Operator removes all the dimensions of size 1 from the shape of a tensor. If axis is provided the operation is performed only on the given axis.


This operator is used to subtract a tensor from another.


Tile Operator creates a new tensor by replicating input multiples times. The output tensor's i'th dimension has input.dims(i) * multiples[i] elements, and the values of input are replicated multiples[i] times along the 'i'th dimension. For example, tiling [a b c d] by [2] produces [a b c d a b c d].

Timing Signal

This Operator adds a bunch of sinusoids of different frequencies.Each channel of the input Tensor is incremented by a sinusoid of a different frequency and phase.


Transpose Operation's output dimension i will correspond to the input dimension perm[i]. If perm is not given, it is set to (n-1...0), where n is the rank of the input tensor. Hence by default, this operation performs a regular matrix transpose on 2-D input Tensors. If conjugate is True and a.dtype is either complex64 or complex128 then the values of a are conjugated and transposed.


Unpacks the given dimension of a rank-R tensor into rank-(R-1) tensors.