torch_tensor_zeros Subroutine

  subroutine torch_tensor_zeros(tensor, ndims, tensor_shape, dtype, &
                                device_type, device_index, requires_grad)
    use, intrinsic :: iso_c_binding, only : c_bool, c_int, c_int64_t
    type(torch_tensor), intent(out) :: tensor     !! Returned tensor
    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)      :: 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)                  :: device_index_value  !! device index used
    logical(c_bool)                 :: requires_grad_value  !! Whether gradients need to be computed for the created tensor

    interface
      function torch_zeros_c(ndims, tensor_shape, dtype, device_type, device_index, requires_grad) result(tensor) &
          bind(c, name = 'torch_zeros')
        use, intrinsic :: iso_c_binding, only : c_bool, c_int, c_int64_t, c_ptr
        integer(c_int), value, intent(in) :: ndims
        integer(c_int64_t), intent(in)    :: tensor_shape(*)
        integer(c_int), value, intent(in) :: dtype
        integer(c_int), value, intent(in) :: device_type
        integer(c_int), value, intent(in) :: device_index
        logical(c_bool), value, intent(in) :: requires_grad
        type(c_ptr)                       :: tensor
      end function torch_zeros_c
    end interface

    ! 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

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

    tensor%p = torch_zeros_c(ndims, tensor_shape, dtype, device_type,          &
                             device_index_value, requires_grad_value)
  end subroutine torch_tensor_zeros