code: def forward(
      ctx,
      input,
      offset,
      mask,
      weight,
      bias=None,
      stride=1,
      padding=0,
      dilation=1,
      groups=1,
      deform_groups=1,
  ):
      if input is not None and input.dim() != 4:
          raise ValueError(
              f"Expected 4D tensor as input, got {input.dim()}D tensor \
                    instead."
          )
      ctx.stride = _pair(stride)
      ctx.padding = _pair(padding)
      ctx.dilation = _pair(dilation)
      ctx.groups = groups
      ctx.deform_groups = deform_groups
      ctx.with_bias = bias is not None
      if not ctx.with_bias:
          bias = input.new_empty(0)  # fake tensor
      # When pytorch version >= 1.6.0, amp is adopted for fp16 mode;
      # amp won't cast the type of model (float32), but "offset" is cast
      # to float16 by nn.Conv2d automatically, leading to the type
      # mismatch with input (when it is float32) or weight.
      # The flag for whether to use fp16 or amp is the type of "offset",
      # we cast weight and input to temporarily support fp16 and amp
      # whatever the pytorch version is.
      input = input.type_as(offset)
      weight = weight.type_as(input)
      ctx.save_for_backward(input, offset, mask, weight, bias)
      output = input.new_empty(
          ModulatedDeformConv2dFunction._output_size(ctx, input, weight)
      )
      ctx._bufs = [input.new_empty(0), input.new_empty(0)]
      ext_module.modulated_deform_conv_forward(
          input,
          weight,
          bias,
          ctx._bufs[0],
          offset,
          mask,
          output,
          ctx._bufs[1],
          kernel_h=weight.size(2),
          kernel_w=weight.size(3),
          stride_h=ctx.stride[0],
          stride_w=ctx.stride[1],
          pad_h=ctx.padding[0],
          pad_w=ctx.padding[1],
          dilation_h=ctx.dilation[0],
          dilation_w=ctx.dilation[1],
          group=ctx.groups,
          deformable_group=ctx.deform_groups,
          with_bias=ctx.with_bias,
      )
      return output
  
imports: import math
  import torch
  import torch.nn
  from torch.autograd import Function
  from torch.autograd.function import once_differentiable
  from torch.nn.modules.utils import _pair, _single
  from annotator.uniformer.mmcv.utils import deprecated_api_warning
  from ..cnn import CONV_LAYERS
  from ..utils import ext_loader, print_log
label_desc: Use image node_id 2 for calling the ModulatedDeformConv2dFunction obj's underlying member method code with example usage: obj.forward(ctx, input, offset, mask, weight, bias, stride, padding, dilation, groups, deform_groups) and returns: output