parameters: (x, copy=True, nan=0.0, posinf=None, neginf=None, **kwargs)
return_statement: return _mx_nd_np.nan_to_num(x, copy=copy, nan=nan, posinf=posinf, neginf=neginf)
docstring: Replace NaN with zero and infinity with large finite numbers (default
      behaviour) or with the numbers defined by the user using the `nan`,
      `posinf` and/or `neginf` keywords.
  
      If `x` is inexact, NaN is replaced by zero or by the user defined value in
      `nan` keyword, infinity is replaced by the largest finite floating point
      values representable by ``x.dtype`` or by the user defined value in
      `posinf` keyword and -infinity is replaced by the most negative finite
      floating point values representable by ``x.dtype`` or by the user defined
      value in `neginf` keyword.
  
      For complex dtypes, the above is applied to each of the real and
      imaginary components of `x` separately.
  
      If `x` is not inexact, then no replacements are made.
  
      Parameters
      ----------
      x : scalar
          ndarray
          Input data.
      copy : bool, optional
          Whether to create a copy of `x` (True) or to replace values
          in-place (False). The in-place operation only occurs if
          casting to an array does not require a copy.
          Default is True.
          Gluon does not support copy = False.
      nan : int, float, optional
          Value to be used to fill NaN values. If no value is passed
          then NaN values will be replaced with 0.0.
      posinf : int, float, optional
          Value to be used to fill positive infinity values. If no value is
          passed then positive infinity values will be replaced with a very
          large number.
      neginf : int, float, optional
          Value to be used to fill negative infinity values. If no value is
          passed then negative infinity values will be replaced with a very
          small (or negative) number.
  
          .. versionadded:: 1.13
  
      Returns
      -------
      out : ndarray
          `x`, with the non-finite values replaced. If `copy` is False, this may
          be `x` itself.
  
      Notes
      -----
      NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
      (IEEE 754). This means that Not a Number is not equivalent to infinity.
  
      Examples
      --------
      >>> np.nan_to_num(np.inf)
      1.7976931348623157e+308
      >>> np.nan_to_num(-np.inf)
      -1.7976931348623157e+308
      >>> np.nan_to_num(np.nan)
      0.0
      >>> x = np.array([np.inf, -np.inf, np.nan, -128, 128])
      >>> np.nan_to_num(x)
      array([ 3.4028235e+38, -3.4028235e+38,  0.0000000e+00, -1.2800000e+02,
              1.2800000e+02])
      >>> np.nan_to_num(x, nan=-9999, posinf=33333333, neginf=33333333)
      array([ 3.3333332e+07,  3.3333332e+07, -9.9990000e+03, -1.2800000e+02,
              1.2800000e+02])
      >>> y = np.array([[-1, 0, 1],[9999,234,-14222]],dtype="float64")/0
      array([[-inf,  nan,  inf],
          [ inf,  inf, -inf]], dtype=float64)
      >>> np.nan_to_num(y)
      array([[-1.79769313e+308,  0.00000000e+000,  1.79769313e+308],
          [ 1.79769313e+308,  1.79769313e+308, -1.79769313e+308]], dtype=float64)
      >>> np.nan_to_num(y, nan=111111, posinf=222222)
      array([[-1.79769313e+308,  1.11111000e+005,  2.22222000e+005],
          [ 2.22222000e+005,  2.22222000e+005, -1.79769313e+308]], dtype=float64)
      >>> y
      array([[-inf,  nan,  inf],
         [ inf,  inf, -inf]], dtype=float64)
      >>> np.nan_to_num(y, copy=False, nan=111111, posinf=222222)
      array([[-1.79769313e+308,  1.11111000e+005,  2.22222000e+005],
         [ 2.22222000e+005,  2.22222000e+005, -1.79769313e+308]], dtype=float64)
      >>> y
      array([[-1.79769313e+308,  1.11111000e+005,  2.22222000e+005],
         [ 2.22222000e+005,  2.22222000e+005, -1.79769313e+308]], dtype=float64)
docstring_summary: Replace NaN with zero and infinity with large finite numbers (default
      behaviour) or with the numbers defined by the user using the `nan`,
      `posinf` and/or `neginf` keywords.
docstring_tokens: Replace NaN with zero and infinity with large finite numbers ( default behaviour ) or with the numbers defined by the user using the nan posinf and / or neginf keywords .
function: def nan_to_num(x, copy=True, nan=0.0, posinf=None, neginf=None, **kwargs):
      """
      Replace NaN with zero and infinity with large finite numbers (default
      behaviour) or with the numbers defined by the user using the `nan`,
      `posinf` and/or `neginf` keywords.
  
      If `x` is inexact, NaN is replaced by zero or by the user defined value in
      `nan` keyword, infinity is replaced by the largest finite floating point
      values representable by ``x.dtype`` or by the user defined value in
      `posinf` keyword and -infinity is replaced by the most negative finite
      floating point values representable by ``x.dtype`` or by the user defined
      value in `neginf` keyword.
  
      For complex dtypes, the above is applied to each of the real and
      imaginary components of `x` separately.
  
      If `x` is not inexact, then no replacements are made.
  
      Parameters
      ----------
      x : scalar
          ndarray
          Input data.
      copy : bool, optional
          Whether to create a copy of `x` (True) or to replace values
          in-place (False). The in-place operation only occurs if
          casting to an array does not require a copy.
          Default is True.
          Gluon does not support copy = False.
      nan : int, float, optional
          Value to be used to fill NaN values. If no value is passed
          then NaN values will be replaced with 0.0.
      posinf : int, float, optional
          Value to be used to fill positive infinity values. If no value is
          passed then positive infinity values will be replaced with a very
          large number.
      neginf : int, float, optional
          Value to be used to fill negative infinity values. If no value is
          passed then negative infinity values will be replaced with a very
          small (or negative) number.
  
          .. versionadded:: 1.13
  
      Returns
      -------
      out : ndarray
          `x`, with the non-finite values replaced. If `copy` is False, this may
          be `x` itself.
  
      Notes
      -----
      NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
      (IEEE 754). This means that Not a Number is not equivalent to infinity.
  
      Examples
      --------
      >>> np.nan_to_num(np.inf)
      1.7976931348623157e+308
      >>> np.nan_to_num(-np.inf)
      -1.7976931348623157e+308
      >>> np.nan_to_num(np.nan)
      0.0
      >>> x = np.array([np.inf, -np.inf, np.nan, -128, 128])
      >>> np.nan_to_num(x)
      array([ 3.4028235e+38, -3.4028235e+38,  0.0000000e+00, -1.2800000e+02,
              1.2800000e+02])
      >>> np.nan_to_num(x, nan=-9999, posinf=33333333, neginf=33333333)
      array([ 3.3333332e+07,  3.3333332e+07, -9.9990000e+03, -1.2800000e+02,
              1.2800000e+02])
      >>> y = np.array([[-1, 0, 1],[9999,234,-14222]],dtype="float64")/0
      array([[-inf,  nan,  inf],
          [ inf,  inf, -inf]], dtype=float64)
      >>> np.nan_to_num(y)
      array([[-1.79769313e+308,  0.00000000e+000,  1.79769313e+308],
          [ 1.79769313e+308,  1.79769313e+308, -1.79769313e+308]], dtype=float64)
      >>> np.nan_to_num(y, nan=111111, posinf=222222)
      array([[-1.79769313e+308,  1.11111000e+005,  2.22222000e+005],
          [ 2.22222000e+005,  2.22222000e+005, -1.79769313e+308]], dtype=float64)
      >>> y
      array([[-inf,  nan,  inf],
         [ inf,  inf, -inf]], dtype=float64)
      >>> np.nan_to_num(y, copy=False, nan=111111, posinf=222222)
      array([[-1.79769313e+308,  1.11111000e+005,  2.22222000e+005],
         [ 2.22222000e+005,  2.22222000e+005, -1.79769313e+308]], dtype=float64)
      >>> y
      array([[-1.79769313e+308,  1.11111000e+005,  2.22222000e+005],
         [ 2.22222000e+005,  2.22222000e+005, -1.79769313e+308]], dtype=float64)
      """
      return _mx_nd_np.nan_to_num(x, copy=copy, nan=nan, posinf=posinf, neginf=neginf)
function_tokens: def nan_to_num ( x , copy = True , nan = 0.0 , posinf = None , neginf = None , * * kwargs ) : return _mx_nd_np . nan_to_num ( x , copy = copy , nan = nan , posinf = posinf , neginf = neginf )