code: def full(
      shape: Union[ivy.NativeShape, Sequence[int]],
      fill_value: Union[int, float, bool],
      *,
      dtype: Optional[Union[ivy.Dtype, torch.dtype]] = None,
      device: torch.device = None,
      out: Optional[torch.Tensor] = None,
  ) -> Tensor:
      dtype = ivy.default_dtype(
          dtype=dtype, item=fill_value, as_native=True
      )
      if isinstance(shape, int):
          shape = (shape,)
      return torch.full(
          shape,
          fill_value,
          dtype=dtype,
          device=device,
          out=out,
      )
  
imports: import copy
  from numbers import Number
  from typing import Union, List, Optional, Sequence, Tuple
  import numpy
  import torch
  from torch import Tensor
  import ivy
  from ivy.func_wrapper import with_unsupported_dtypes, with_unsupported_device_and_dtypes
  from ivy.functional.ivy.creation import _asarray_to_native_arrays_and_back, _asarray_infer_device, _asarray_infer_dtype, _asarray_handle_nestable, NestedSequence, SupportsBufferProtocol, _asarray_inputs_to_native_shapes, _remove_np_bfloat16
  from .None import backend_version
label_desc: Use image node_id 10 for calling a global function with example usage: full(shape, fill_value) and returns: torch