code: def eye(
      n_rows: int,
      n_cols: Optional[int] = None,
      /,
      *,
      k: int = 0,
      batch_shape: Optional[Union[int, Sequence[int]]] = None,
      dtype: torch.dtype,
      device: torch.device = None,
      out: Optional[torch.Tensor] = None,
  ) -> torch.Tensor:
      if n_cols is None:
          n_cols = n_rows
      if batch_shape is None:
          batch_shape = []
      i = torch.eye(n_rows, n_cols, dtype=dtype, device=device)
      reshape_dims = [1] * len(batch_shape) + [n_rows, n_cols]
      tile_dims = list(batch_shape) + [1, 1]
      return_mat = torch.reshape(i, reshape_dims).repeat(tile_dims)
  
      # k=index of the diagonal. A positive value refers to an upper diagonal,
      # a negative value to a lower diagonal, and 0 to the main diagonal.
      # Default: ``0``.
      # value of k ranges from -n_rows < k < n_cols
  
      if k == 0:  # refers to the main diagonal
          ret = return_mat
  
      # when k is negative
      elif -n_rows < k < 0:
          mat = torch.concat(
              [
                  torch.zeros(
                      [-k, n_cols], dtype=dtype, device=device, out=out
                  ),
                  i[: n_rows + k],
              ],
              0,
          )
          ret = torch.reshape(mat, reshape_dims).repeat(tile_dims)
  
      # when k is positive
      elif 0 < k < n_cols:
          mat = torch.concat(
              [
                  torch.zeros([n_rows, k], dtype=dtype, device=device),
                  i[:, : n_cols - k],
              ],
              1,
          )
          ret = torch.reshape(mat, reshape_dims).repeat(tile_dims)
          if out is not None:
              return ivy.inplace_update(out, ret)
      else:
          ret = torch.zeros(
              batch_shape + [n_rows, n_cols],
              dtype=dtype,
              device=device,
              out=out,
          )
      return ret
  
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 7 for calling a global function with example usage: eye() and returns: ret, ivy