# Assume Tensors and DataFrames are builtin to the system (i.e. they produce code)

# based on backend selections).

# A Tensor is a container of a Type. Zero-dimensional tensors exist

# and are analogous to elements of the Type.

# You must index the 0-d array to produce the element.

# Elements of the Tensor must be explictly extracted using the syntax a[()]

# where a evaluates to a 0-d Tensor. This always copies the element into a new

# object.

# A Tensor has attributes: shape, ndim, dtype, T, kind

# A Tensor kind is a meta-type which is one of the Types:

# General > Gamma > Strided > C-contiguous > F-contiguous

# General > Chunked

# Based on the kind of Tensor we have private fields that are present or not

# GeneralMeta:

index_map : a function of ndim arguments and a state Object that produces

a 0-d tensor.

# General:

index_map : (a function of ndim integer arguments that produces a 0-d tensor)

# Gamma adds:

gamma_map : (a function of ndim integer arguments that produces an integer)

datapointer : a 1-d array of bytes

# Strided adds:

strides : a Tuple of integers indicating the number of bytes to jump to get

# C-Contiguous:

no additional information, the other attributes functions are computed as needed.

# F-Contiguous:

no additional information, the other attributes are computed as needed.

Also builtin to the system are "generalized universal functions"

These operate on