code: def diagflat(
      x: torch.Tensor,
      /,
      *,
      offset: int = 0,
      padding_value: float = 0,
      align: str = "RIGHT_LEFT",
      num_rows: int = -1,
      num_cols: int = -1,
      out: Optional[torch.Tensor] = None,
  ):
      if len(x.shape) > 1:
          x = torch.flatten(x)
      # if len(x.shape) == 1 and offset == 0 and num_rows <= 1 and num_cols <= 1:
      if (
          math.prod(x.shape) == 1
          and offset == 0
          and num_rows <= 1
          and num_cols <= 1
      ):
          return x
  
      # This is used as part of Tensorflow's shape calculation
      # See their source code to see what they're doing with it
      lower_diag_index = offset
      upper_diag_index = lower_diag_index
  
      x_shape = x.shape
      x_rank = len(x_shape)
  
      num_diags = upper_diag_index - lower_diag_index + 1
      max_diag_len = x_shape[x_rank - 1]
  
      min_num_rows = max_diag_len - min(upper_diag_index, 0)
      min_num_cols = max_diag_len + max(lower_diag_index, 0)
  
      if num_rows == -1 and num_cols == -1:
          num_rows = max(min_num_rows, min_num_cols)
          num_cols = num_rows
      elif num_rows == -1:
          num_rows = min_num_rows
      elif num_cols == -1:
          num_cols = min_num_cols
  
      output_shape = list(x_shape)
      if num_diags == 1:
          output_shape[x_rank - 1] = num_rows
          output_shape.append(num_cols)
      else:
          output_shape[x_rank - 2] = num_rows
          output_shape[x_rank - 1] = num_cols
  
      output_array = torch.full(
          tuple(output_shape), padding_value, dtype=x.dtype
      )
      output_array = output_array.to(x.dtype)
  
      diag_len = max(min(num_rows, num_cols) - abs(offset) + 1, 1)
  
      if len(x) < diag_len:
          x = torch.tensor(
              list(x) + [padding_value] * max((diag_len - len(x), 0)),
              dtype=x.dtype,
          )
  
      temp = x - torch.full(x.shape, padding_value).type(x.dtype)
      diagonal_to_add = torch.diag(temp, diagonal=offset).type(
          x.dtype
      )  # diag does not support float16
  
      diagonal_to_add = diagonal_to_add[
          tuple(slice(0, n) for n in output_array.shape)
      ]
      diagonal_to_add = diagonal_to_add.to(x.dtype)
  
      output_array += torch.nn.functional.pad(
          diagonal_to_add,
          (
              0,
              max(
                  [output_array.shape[1] - diagonal_to_add.shape[1], 0]
              ),
              0,
              max(
                  [output_array.shape[0] - diagonal_to_add.shape[0], 0]
              ),
          ),
          "constant",
          0,
      ).type(x.dtype)
      ret = output_array.type(x.dtype)
  
      if ivy.exists(out):
          ivy.inplace_update(out, ret)
  
      return ret
  
file_path: ivy/ivy/functional/backends/torch/experimental/linear_algebra.py
imports: import math
  import torch
  from typing import Optional, Tuple, Sequence, Union
  import ivy
  from ivy.func_wrapper import with_unsupported_dtypes
  from ivy.utils.exceptions import IvyNotImplementedException
  from ..None import backend_version
  from ivy.functional.ivy.experimental.linear_algebra import _check_valid_dimension_size
label_desc: Use image node_id 1 for calling a global function with example usage: diagflat() and returns: ret, x