code: def var(
      x: JaxArray,
      /,
      *,
      axis: Optional[Union[int, Sequence[int]]] = None,
      correction: Union[int, float] = 0.0,
      keepdims: bool = False,
      out: Optional[JaxArray] = None,
  ) -> JaxArray:
      if axis is None:
          axis = tuple(range(len(x.shape)))
      axis = (axis,) if isinstance(axis, int) else tuple(axis)
      if isinstance(correction, int):
          ret = jnp.var(
              x, axis=axis, ddof=correction, keepdims=keepdims, out=out
          )
          return ivy.astype(ret, x.dtype, copy=False)
      if x.size == 0:
          return jnp.asarray(float("nan"))
      size = 1
      for a in axis:
          size *= x.shape[a]
      if size == correction:
          size += 0.0001  # to avoid division by zero in return
      return ivy.astype(
          jnp.multiply(
              jnp.var(x, axis=axis, keepdims=keepdims, out=out),
              size / jnp.abs(size - correction),
          ),
          x.dtype,
          copy=False,
      )
  
file_path: ivy/ivy/functional/backends/jax/statistical.py
imports: import jax.numpy
  from typing import Union, Optional, Sequence
  import ivy
  from ivy.func_wrapper import with_unsupported_dtypes
  from ivy.functional.backends.jax import JaxArray
  from .None import backend_version
label_desc: Use image node_id 8 for calling a global function with example usage: var() and returns: ivy, ivy, jnp