4 examples of 'tensorflow reduce_sum' in Python

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def reduce_sum(input_tensor, axis=None, keep_dims=False, name=None, reduction_indices=None):
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    tf.reduce_sum(input_tensor, axis, keep_dims, name, reduction_indices)

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def sum(self, x: BKTensor, *args: Any, **kwargs: Any) -> BKTensor:
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    return self.tf.reduce_sum(x, *args, **kwargs)

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def sum(x, axis=None, keepdims=False):
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    """Sum of the values in a tensor, alongside the specified axis.
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    """
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    return T.sum(x, axis=axis, keepdims=keepdims)

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@ops.RegisterGradient("Sum")
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def _SumGrad(op, grad):
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  """Gradient for Sum."""
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  # Fast path for when reducing to a scalar and ndims is known: adds only
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  # Reshape and Tile ops (and possibly a Shape).
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  if (op.inputs[0].get_shape().ndims is not None and
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      op.inputs[1].op.type == "Const"):
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    rank = op.inputs[0].get_shape().ndims
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    axes = tensor_util.MakeNdarray(op.inputs[1].op.get_attr("value"))
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    if np.array_equal(axes, np.arange(rank)):  # Reduce all dims.
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      grad = array_ops.reshape(grad, [1] * rank)
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      # If shape is not fully defined (but rank is), we use Shape.
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      if op.inputs[0].get_shape().is_fully_defined():
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        input_shape = op.inputs[0].get_shape().as_list()
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      else:
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        input_shape = array_ops.shape(op.inputs[0])
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      return [array_ops.tile(grad, input_shape), None]
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  input_shape = array_ops.shape(op.inputs[0])
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  output_shape_kept_dims = math_ops.reduced_shape(input_shape, op.inputs[1])
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  tile_scaling = _safe_shape_div(input_shape, output_shape_kept_dims)
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  grad = array_ops.reshape(grad, output_shape_kept_dims)
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  return [array_ops.tile(grad, tile_scaling), None]