class_docstr: Base class for anchor generators for the task of object detection.
docstr: Returns anchors for the feature map
  
  Args:
      fm_height (int): Height of the feature map
      fm_width (int): Width of the feature map
      fm_output_stride (int): Output stride of the feature map
      device (Optional, str): Device (cpu or cuda). Defaults to cpu
  
  Returns:
      Tensor or Tuple of Tensors
code: def forward(
      self,
      fm_height: int,
      fm_width: int,
      fm_output_stride: int,
      device: Optional[str] = "cpu",
      *args,
      **kwargs,
  ) -> Union[Tensor, Tuple[Tensor, ...]]:
      """
      Returns anchors for the feature map
  
      Args:
          fm_height (int): Height of the feature map
          fm_width (int): Width of the feature map
          fm_output_stride (int): Output stride of the feature map
          device (Optional, str): Device (cpu or cuda). Defaults to cpu
  
      Returns:
          Tensor or Tuple of Tensors
      """
      return self._get_anchors(
          fm_height=fm_height,
          fm_width=fm_width,
          fm_output_stride=fm_output_stride,
          device=device,
          *args,
          **kwargs,
      )
  
file_path: ml-cvnets/cvnets/anchor_generator/base_anchor_generator.py
imports: import argparse
  from typing import Optional, Tuple, Union
  import torch
  from torch import Tensor
label_desc: Use image node_id 6 for calling the BaseAnchorGenerator obj's underlying member method code with example usage: obj.forward(fm_height, fm_width, fm_output_stride, device) and returns: self