code: def forward_grad_np_var(g, ans, x, axis=None, ddof=0, keepdims=False):
      if axis is None:
          num_reps = anp.size(g)
      elif isinstance(axis, int):
          num_reps = anp.shape(g)[axis]
      elif isinstance(axis, tuple):
          num_reps = anp.prod(anp.array(np.shape(g))[list(axis)])
  
      x_minus_mean = anp.conj(x - anp.mean(x, axis=axis, keepdims=True))
      return (
          2.0
          * anp.sum(
              anp.real(g * x_minus_mean), axis=axis, keepdims=keepdims
          )
          / (num_reps - ddof)
      )
  
file_path: autograd/autograd/numpy/numpy_jvps.py
imports: from .None import numpy_wrapper
  from .numpy_vjps import untake, balanced_eq, match_complex, replace_zero, dot_adjoint_0, dot_adjoint_1, tensordot_adjoint_0, tensordot_adjoint_1, nograd_functions
  from autograd.extend import defjvp, defjvp_argnum, def_linear, vspace, JVPNode, register_notrace
  from ..util import func
  from .numpy_boxes import ArrayBox
label_desc: Use image node_id 1 for calling a global function with example usage: forward_grad_np_var(g, ans, x, axis, ddof, keepdims) and returns: unknown