Reshape with explicit memory layout and transpose with explicit permutation

[christopherzimmerman]

Currently, reshaping respects existing memory layout, so two tensors with different contiguity can often differ after reshaping:

let
  a = toSeq(1..12).toTensor().reshape(3, 2, 2).asContiguous(rowMajor, force=true)
  b = toSeq(1..12).toTensor().reshape(3, 2, 2).asContiguous(colMajor, force=true)

echo a == b
echo a.reshape(2, 6)
echo b.reshape(2, 6)

# true
# Tensor[system.int] of shape [2, 6]" on backend "Cpu"
# |1	2	3	4	5	6|
# |7	8	9	10	11	12|
#
# Tensor[system.int] of shape [2, 6]" on backend "Cpu"
# |1	9	7	2	10	8|
# |5	3	11	6	4	12|

This PR adds another reshape method which allows reshaping to be done w.r.t. a provided memory layout.

echo a.reshape(2, 6, colMajor) == b.reshape(2, 6, colMajor)
# true

It also adds the ability to transpose a tensor using a provided permutation of axes, rather than just simply reversing axes and strides.

let
  a = toSeq(1..12).toTensor().reshape(3, 2, 2)

echo a.transpose()
echo a.transpose(@[2, 0, 1])

# Tensor[system.int] of shape [2, 2, 3]" on backend "Cpu"
# | | 	1	5	9 | 	2	6	10|
# | | 	3	7	11 | 	4	8	12|
# 
# Tensor[system.int] of shape [2, 3, 2]" on backend "Cpu"
# | | 	1	3 | 	2	4|
# | | 	5	7 | 	6	8|
# | | 	9	11 | 	10	12|

[mratsim]

Sorry was off coding for lng while, thanks for your PR

[Clonkk]

@mratsim This seem like a useful addition. Should it be merged ?