 # # Operators

This operator is used to add two tensors.

## # Cast

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

## # Concatenate

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

## # Divide

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.

## # Fork

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

## # Matmul

Performs Matrix Multiplication Operation.

## # Multiply

This operator is used for multiplication of two tensors.

## # Reduce

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

## # Reshape

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

## # Slice

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

## # Split

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

## # Squeeze

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.

## # Subtract

This operator is used to subtract a tensor from another.

## # Tile

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  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

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.

## # Unstack

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