code: def forward(ctx, kwarg_names, conv_fn, *expanded_args_and_kwargs):
      expanded_args, expanded_kwargs = conv_args_and_kwargs(
          kwarg_names, expanded_args_and_kwargs
      )
      orig_input = expanded_args[0]
      was_same_padding = expanded_kwargs["padding"] == "same"
  
      if isinstance(expanded_kwargs["padding"], str):
          # if padding is a string, we'll do the necessary padding (slowly) using F.pad
          kernel_size = expanded_args[1].shape[2:]
          padding, dilation = (
              expanded_kwargs["padding"],
              expanded_kwargs["dilation"],
          )
          input = conv_input_for_string_padding(
              conv_fn, padding, expanded_args[0], dilation, kernel_size
          )
          expanded_args = (input, expanded_args[1])
          # since we've already done the padding, don't need any more
          expanded_kwargs["padding"] = 0
  
      output = forward_helper(conv_fn, expanded_args, expanded_kwargs)
      input, weight = expanded_args
      batched_dim_size = conv_picker(conv_fn, 3, 4, 5)
      if input.dim() != batched_dim_size:
          raise RuntimeError(
              f"Expanded Weights only support convolution with batched input, got {conv_fn} with an"
              f"unbatched input of dim {input.dim()}, expected input of dim {batched_dim_size}"
          )
  
      ctx.conv_fn = conv_fn
  
      ctx.batch_size = orig_input.shape[0]
      ctx.input_required_grad = orig_input.requires_grad
      ctx.orig_input_shape = orig_input.shape
      ctx.was_same_padding = was_same_padding
      ctx.stride, ctx.padding = (
          expanded_kwargs["stride"],
          expanded_kwargs["padding"],
      )
      ctx.dilation, ctx.groups = (
          expanded_kwargs["dilation"],
          expanded_kwargs["groups"],
      )
  
      if isinstance(weight, ExpandedWeight):
          ctx.input = input
      ctx.weight = weight
      ctx.bias = expanded_kwargs["bias"]
  
      return output
  
file_path: pytorch/torch/nn/utils/_expanded_weights/conv_expanded_weights.py
imports: import torch
  import torch.nn.functional
  from .conv_utils import conv_backward, conv_args_and_kwargs, conv_picker, conv_input_for_string_padding
  from .expanded_weights_impl import ExpandedWeight, implements_per_sample_grads
  from .expanded_weights_utils import forward_helper
label_desc: Use image node_id 1 for calling the ConvPerSampleGrad obj's underlying member method code with example usage: obj.forward(ctx, kwarg_names, conv_fn) and returns: output