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# torch.float_power

torch.float_power(input, exponent, *, out=None) → Tensor

  Raises `input` to the power of `exponent`, elementwise, in double precision. If neither input is complex returns a `torch.float64` tensor, and if one or more inputs is complex returns a `torch.complex128` tensor.

  Note

  This function always computes in double precision, unlike `torch.pow()`, which implements more typical type promotion. This is useful when the computation needs to be performed in a wider or more precise dtype, or the results of the computation may contain fractional values not representable in the input dtypes, like when an integer base is raised to a negative integer exponent.

  Parameters:

    - input (Tensor or Number) – the base value(s)

    - exponent (Tensor or Number) – the exponent value(s)

  Keyword Arguments:

    out (Tensor, optional) – the output tensor.

  Example:

      >>> a = torch.randint(10, (4,))
      >>> a
      tensor([6, 4, 7, 1])
      >>> torch.float_power(a, 2)
      tensor([36., 16., 49.,  1.], dtype=torch.float64)

      >>> a = torch.arange(1, 5)
      >>> a
      tensor([ 1,  2,  3,  4])
      >>> exp = torch.tensor([2, -3, 4, -5])
      >>> exp
      tensor([ 2, -3,  4, -5])
      >>> torch.float_power(a, exp)
      tensor([1.0000e+00, 1.2500e-01, 8.1000e+01, 9.7656e-04], dtype=torch.float64)

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