code: def meta_convolution_backward(
      grad_output_,
      input_,
      weight_,
      bias_sizes_opt,
      stride,
      padding,
      dilation,
      transposed,
      output_padding,
      groups,
      output_mask,
  ):
      # High level logic taken from slow_conv3d_backward_cpu which should
      # be representative of all convolution_backward impls
      backend_grad_input = None
      backend_grad_weight = None
      backend_grad_bias = None
  
      if output_mask[0]:
          backend_grad_input = grad_output_.new_empty(input_.size())
      if output_mask[1]:
          backend_grad_weight = grad_output_.new_empty(weight_.size())
      if output_mask[2]:
          backend_grad_bias = grad_output_.new_empty(bias_sizes_opt)
  
      return (
          backend_grad_input,
          backend_grad_weight,
          backend_grad_bias,
      )
  
imports: import torch
  from torch._meta_registrations import register_meta
  from torch._prims_common import check
label_desc: Use image node_id 1 for calling a global function with example usage: meta_convolution_backward(grad_output_, input_, weight_, bias_sizes_opt, stride, padding, dilation, transposed, output_padding, groups, output_mask) and returns: backend_grad_input,  backend_grad_weight,  backend_grad_bias