code: def torch_nn_functional_conv1d(input, weight, **kwargs):
      stride, padding, dilation, _ = _extract_kwargs(kwargs)
  
      stride = _single(stride)
      padding = _single(padding)
      dilation = _single(dilation)
  
      kernel_size = weight.shape[2:]
      l_in = input.shape[-1]
      c_out = weight.shape[0]
      l_out = math.floor(
          (
              l_in
              + 2 * padding[0]
              - dilation[0] * (kernel_size[0] - 1)
              - 1
          )
          / stride[0]
          + 1
      )
      result_shape = input.shape[:-2] + (
          c_out,
          l_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 3 for calling a global function with example usage: torch_nn_functional_conv1d(input, weight) and returns: torch