code: def torch_nn_functional_conv2d(input, weight, **kwargs):
      stride, padding, dilation, _ = _extract_kwargs(kwargs)
  
      stride = _pair(stride)
      padding = _pair(padding)
      dilation = _pair(dilation)
  
      kernel_size = weight.shape[2:]
      h_in, w_in = input.shape[-2:]
      c_out = weight.shape[0]
      h_out = math.floor(
          (
              h_in
              + 2 * padding[0]
              - dilation[0] * (kernel_size[0] - 1)
              - 1
          )
          / stride[0]
          + 1
      )
      w_out = math.floor(
          (
              w_in
              + 2 * padding[1]
              - dilation[1] * (kernel_size[1] - 1)
              - 1
          )
          / 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_function/convolution.py
imports: import collections
  import math
  from itertools import repeat
  import torch
  from ...registry import meta_patched_function
label_desc: Use image node_id 4 for calling a global function with example usage: torch_nn_functional_conv2d(input, weight) and returns: torch