docstr: Perform forward pass of the BatchNorm layer.
  
  Parameters
  ----------
  inputs
      Inputs to process of shape N,C,*.
  
  Returns
  -------
  ret
      The outputs following the batch normalization operation.
docstr_tok: ["Perform","forward","pass","of","the","BatchNorm","layer",".","Parameters","--","--","--","--","--","inputs","Inputs","to","process","of","shape","N",",","C",",","*",".","Returns","--","--","--","-","ret","The","outputs","following","the","batch","normalization","operation","."]
code: def _forward(self, inputs):
      """Perform forward pass of the BatchNorm layer.
  
      Parameters
      ----------
      inputs
          Inputs to process of shape N,C,*.
  
      Returns
      -------
      ret
          The outputs following the batch normalization operation.
      """
      normalized, running_mean, running_var = ivy.batch_norm(
          inputs,
          self.v.running_mean,
          self.v.running_var,
          eps=self._epsilon,
          momentum=self._momentum,
          data_format=self.data_format,
          training=self.training,
          scale=self.v.w if self._affine else None,
          offset=self.v.b if self._affine else None,
      )
      if self._track_running_stats and self.training:
          self.v.running_mean = running_mean
          self.v.running_var = running_var
  
      return normalized
  
file_path: ivy/ivy/stateful/norms.py
imports: import ivy
  from ivy.stateful.module import Module
  from ivy.stateful.initializers import Zeros, Ones
label_desc: Use image node_id 3 for calling the BatchNorm2D obj's underlying member method code with example usage: obj._forward(inputs) and returns: normalized