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# torch.pow

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

  Takes the power of each element in `input` with `exponent` and returns a tensor with the result.

  `exponent` can be either a single `float` number or a Tensor with the same number of elements as `input`.

  When `exponent` is a scalar value, the operation applied is:

  out i=x i exponent\text{out}_i = x_i ^ \text{exponent} out i​=x i exponent​

  When `exponent` is a tensor, the operation applied is:

  out i=x i exponent i\text{out}_i = x_i ^ {\text{exponent}_i} out i​=x i exponent i​​

  When `exponent` is a tensor, the shapes of `input` and `exponent` must be broadcastable.

  Parameters:

    - input (Tensor) – the input tensor.

    - exponent (float or tensor) – the exponent value

  Keyword Arguments:

    out (Tensor, optional) – the output tensor.

  Example:

      >>> a = torch.randn(4)
      >>> a
      tensor([ 0.4331,  1.2475,  0.6834, -0.2791])
      >>> torch.pow(a, 2)
      tensor([ 0.1875,  1.5561,  0.4670,  0.0779])
      >>> exp = torch.arange(1., 5.)

      >>> a = torch.arange(1., 5.)
      >>> a
      tensor([ 1.,  2.,  3.,  4.])
      >>> exp
      tensor([ 1.,  2.,  3.,  4.])
      >>> torch.pow(a, exp)
      tensor([   1.,    4.,   27.,  256.])

  torch.pow(self, exponent, *, out=None) → Tensor

  `self` is a scalar `float` value, and `exponent` is a tensor. The returned tensor `out` is of the same shape as `exponent`

  The operation applied is:

  out i=self exponent i\text{out}_i = \text{self} ^ {\text{exponent}_i} out i​=self exponent i​
  Parameters:

    - self (float) – the scalar base value for the power operation

    - exponent (Tensor) – the exponent tensor

  Keyword Arguments:

    out (Tensor, optional) – the output tensor.

  Example:

      >>> exp = torch.arange(1., 5.)
      >>> base = 2
      >>> torch.pow(base, exp)
      tensor([  2.,   4.,   8.,  16.])

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