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# torch.inner

torch.inner(input, other, *, out=None) → Tensor

  Computes the dot product for 1D tensors. For higher dimensions, sums the product of elements from `input` and `other` along their last dimension.

  Note

  If either `input` or `other` is a scalar, the result is equivalent to torch.mul(input, other).

  If both `input` and `other` are non-scalars, the size of their last dimension must match and the result is equivalent to torch.tensordot(input, other, dims=([-1], [-1]))

  Parameters:

    - input (Tensor) – First input tensor

    - other (Tensor) – Second input tensor

  Keyword Arguments:

    out (Tensor, optional) – Optional output tensor to write result into. The output shape is input.shape[:-1] + other.shape[:-1].

  Example:

      # Dot product
      >>> torch.inner(torch.tensor([1, 2, 3]), torch.tensor([0, 2, 1]))
      tensor(7)

      # Multidimensional input tensors
      >>> a = torch.randn(2, 3)
      >>> a
      tensor([[0.8173, 1.0874, 1.1784],
              [0.3279, 0.1234, 2.7894]])
      >>> b = torch.randn(2, 4, 3)
      >>> b
      tensor([[[-0.4682, -0.7159,  0.1506],
              [ 0.4034, -0.3657,  1.0387],
              [ 0.9892, -0.6684,  0.1774],
              [ 0.9482,  1.3261,  0.3917]],

              [[ 0.4537,  0.7493,  1.1724],
              [ 0.2291,  0.5749, -0.2267],
              [-0.7920,  0.3607, -0.3701],
              [ 1.3666, -0.5850, -1.7242]]])
      >>> torch.inner(a, b)
      tensor([[[-0.9837,  1.1560,  0.2907,  2.6785],
              [ 2.5671,  0.5452, -0.6912, -1.5509]],

              [[ 0.1782,  2.9843,  0.7366,  1.5672],
              [ 3.5115, -0.4864, -1.2476, -4.4337]]])

      # Scalar input
      >>> torch.inner(a, torch.tensor(2))
      tensor([[1.6347, 2.1748, 2.3567],
              [0.6558, 0.2469, 5.5787]])

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