code: def _conv(
      x,
      weight,
      bias=None,
      stride=1,
      padding=0,
      dilation=1,
      groups=1,
      data_format="NLC",
  ):
      x = _channel_first_input(x, data_format)
  
      if padding == "same":
          dilation = 1
      return convolution_functions._conv(
          x,
          weight,
          bias=bias,
          stride=stride,
          padding=padding,
          dilation=dilation,
          groups=groups,
      )
  
file_path: ivy/ivy/functional/frontends/paddle/nn/functional/conv.py
imports: import ivy
  from ivy.func_wrapper import with_supported_dtypes
  from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
  from ivy.functional.frontends.torch.nn.functional import convolution_functions
label_desc: Use image node_id 2 for calling a global function with example usage: _conv(x, weight, bias, stride, padding, dilation, groups, data_format) and returns: convolution_functions