code: def _channel_first_input(x, data_format):
      ndims = len(x.shape)
      dims = ndims - 2
      if 1 >= dims >= 3:
          raise ivy.utils.exceptions.IvyError(
              f"invalid for input with {dims} dims"
          )
      # channel first input
      if data_format not in ["NCL", "NCHW", "NCDHW"]:
          if data_format in ["NLC", "NHWC", "NDHWC"]:
              x = ivy.permute_dims(
                  x, axes=(0, ndims - 1, *range(1, ndims - 1))
              )
          else:
              raise ivy.utils.exceptions.IvyError(
                  "data_format should be " + "'NCL' or 'NLC' "
                  if dims == 1
                  else (
                      "'NCHW' or 'NHWC' "
                      if dims == 2
                      else "'NCDHW' or 'NDHWC' "
                      + f"but got data_format '{data_format}'"
                  )
              )
      return x
  
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 1 for calling a global function with example usage: _channel_first_input(x, data_format) and returns: x