code: def torch_nn_conv2d(self, input):
      # the output shape is calculated using the formula stated
      # at https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html#torch.nn.Conv2d
      h_in, w_in = input.shape[-2:]
      c_out = self.out_channels
      h_out = math.floor(
          (
              h_in
              + 2 * self.padding[0]
              - self.dilation[0] * (self.kernel_size[0] - 1)
              - 1
          )
          / self.stride[0]
          + 1
      )
      w_out = math.floor(
          (
              w_in
              + 2 * self.padding[1]
              - self.dilation[1] * (self.kernel_size[1] - 1)
              - 1
          )
          / self.stride[1]
          + 1
      )
      result_shape = input.shape[:-3] + (
          c_out,
          h_out,
          w_out,
      )
      return torch.empty(result_shape, device="meta")
  
file_path: ColossalAI/colossalai/fx/tracer/meta_patch/patched_module/convolution.py
imports: import math
  import torch
  from ...registry import meta_patched_module
label_desc: Use image node_id 2 for calling a global function with example usage: torch_nn_conv2d(input) and returns: torch