code: def exclusive_cumprod(tensor, dim: int, eps: float = 1e-10):
      """
      Implementing exclusive cumprod.
      There is cumprod in pytorch, however there is no exclusive mode.
      cumprod(x) = [x1, x1x2, x2x3x4, ..., prod_{i=1}^n x_i]
      exclusive means
      cumprod(x) = [1, x1, x1x2, x1x2x3, ..., prod_{i=1}^{n-1} x_i]
      """
      tensor_size = list(tensor.size())
      tensor_size[dim] = 1
      return_tensor = safe_cumprod(
          torch.cat(
              [torch.ones(tensor_size).type_as(tensor), tensor], dim=dim
          ),
          dim=dim,
          eps=eps,
      )
  
      if dim == 0:
          return return_tensor[:-1]
      elif dim == 1:
          return return_tensor[:, :-1]
      elif dim == 2:
          return return_tensor[:, :, :-1]
      else:
          raise RuntimeError(
              "Cumprod on dimension 3 and more is not implemented"
          )
  
imports: import torch
label_desc: Use image node_id 2 for calling a global function with example usage: exclusive_cumprod(tensor, dim, eps) and returns: return_tensor, return_tensor, return_tensor