# Operators

# Add

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 [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

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.