code: def replicate_pads(mod):
      """Traverses the Relay graph to replicate nn.pad operators if thay have
      multiple qnn.conv2d consumers. That making remove the situation when
      e.g. pad+conv2d corresponds qnn_conv2d_pattern, but can not be grouped
      because several conv2d use the same pad operation.
  
      Parameters
      ----------
      tvm.ir.IRModule
          The IRModule that gets generated from a relay frontend.
  
      Returns
      -------
      tvm.ir.IRModule
          The IRModule without nn.pad operators with multiple consumers.
      """
      replicator = PadsWithMultipleConsumersReplicator()
      for global_var, func in mod.functions.items():
          func = replicator.visit(func)
          mod.update_func(global_var, func)
      return mod
  
imports: from collections import defaultdict
  from typing import List, Callable
  from ethosu.vela import api
  import tvm
  from tvm import relay
  from tvm.relay.backend.contrib.ethosu.tir.compiler import LowerToTIR
  from tvm.relay.backend.contrib.ethosu.tir.scheduler import copy_constants
  from tvm.contrib.ethosu.cascader import cascade, EthosuDeviceConfig, CascaderOptions, MemoryRegion, extract_memory_info
  from tvm.relay.backend.contrib.ethosu.legalize import LegalizeEthosU
  from tvm.relay.backend.contrib.ethosu import tir_to_cs_translator, util, vela_api
  from tvm.relay.expr_functor import ExprMutator, ExprVisitor, Call
  from tvm.relay import expr
  from tvm.relay.backend.contrib.ethosu.op import op_attrs
  from tvm.relay.backend.contrib.ethosu import op
  from .None import _ffi_api
label_desc: Use image node_id 1 for calling a global function with example usage: replicate_pads(mod) and returns: mod