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# torch.prod

torch.prod(input, *, dtype=None) → Tensor

  Returns the product of all elements in the `input` tensor.

  Parameters:

    input (Tensor) – the input tensor.

  Keyword Arguments:

    dtype (`torch.dtype`, optional) – the desired data type of returned tensor. If specified, the input tensor is casted to `dtype` before the operation is performed. This is useful for preventing data type overflows. Default: None.

  Example:

      >>> a = torch.randn(1, 3)
      >>> a
      tensor([[-0.8020,  0.5428, -1.5854]])
      >>> torch.prod(a)
      tensor(0.6902)

  torch.prod(input, dim, keepdim=False, *, dtype=None) → Tensor

  Returns the product of each row of the `input` tensor in the given dimension `dim`.

  If `keepdim` is `True`, the output tensor is of the same size as `input` except in the dimension `dim` where it is of size 1. Otherwise, `dim` is squeezed (see `torch.squeeze()`), resulting in the output tensor having 1 fewer dimension than `input`.

  Parameters:

    - input (Tensor) – the input tensor.

    - dim (int) – the dimension to reduce.

    - keepdim (bool) – whether the output tensor has `dim` retained or not.

  Keyword Arguments:

    dtype (`torch.dtype`, optional) – the desired data type of returned tensor. If specified, the input tensor is casted to `dtype` before the operation is performed. This is useful for preventing data type overflows. Default: None.

  Example:

      >>> a = torch.randn(4, 2)
      >>> a
      tensor([[ 0.5261, -0.3837],
              [ 1.1857, -0.2498],
              [-1.1646,  0.0705],
              [ 1.1131, -1.0629]])
      >>> torch.prod(a, 1)
      tensor([-0.2018, -0.2962, -0.0821, -1.1831])

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