torch_tensor_from_blob Subroutine

  subroutine torch_tensor_from_blob(tensor, data, ndims, tensor_shape, layout, dtype, &
                                    device_type, device_index, &
                                    requires_grad)
    use, intrinsic :: iso_c_binding, only : c_bool, c_int, c_int64_t, c_ptr
    type(torch_tensor), intent(out) :: tensor     !! Returned tensor
    type(c_ptr), intent(in)         :: data       !! Pointer to data
    integer(c_int), intent(in)      :: ndims      !! Number of dimensions of the tensor
    integer(c_int64_t), intent(in)  :: tensor_shape(*)  !! Shape of the tensor
    integer(c_int), intent(in)      :: layout(*)  !! Layout for strides for accessing data
    integer(c_int), intent(in)      :: dtype      !! Data type of the tensor
    integer(c_int), intent(in)      :: device_type  !! Device type the tensor will live on (`torch_kCPU` or `torch_kCUDA`)
    integer(c_int), optional, intent(in) :: device_index    !! device index to use for `torch_kCUDA` case
    logical(c_bool), optional, intent(in) :: requires_grad  !! Whether gradients need to be computed for the created tensor

    integer(c_int)                  :: i                    !! loop index
    integer(c_int64_t)              :: strides(ndims)       !! Strides for accessing data
    integer(c_int)                  :: device_index_value   !! device index used
    logical(c_bool)                 :: requires_grad_value  !! Whether gradients need to be computed for the created tensor

    if (.not. present(requires_grad)) then
      requires_grad_value = logical(.false., c_bool)
    else
      requires_grad_value = requires_grad
    end if

    strides(layout(1)) = 1
    do i = 2, ndims
      strides(layout(i)) = strides(layout(i - 1)) * tensor_shape(layout(i - 1))
    end do

    ! Process optional arguments
    if (present(device_index)) then
      device_index_value = device_index
    else if (device_type == torch_kCPU) then
      device_index_value = -1
    else
      device_index_value = 0
    endif

    tensor%p = torch_from_blob_c(data, ndims, tensor_shape, strides, dtype,    &
                                 device_type, device_index_value,              &
                                 requires_grad_value)
  end subroutine torch_tensor_from_blob