def sigmoid(x):
    """Sigmoid activation function.

    It is defined as: `sigmoid(x) = 1 / (1 + exp(-x))`.

    For small values (<-5),
    `sigmoid` returns a value close to zero, and for large values (>5)
    the result of the function gets close to 1.

    Sigmoid is equivalent to a 2-element softmax, where the second element is
    assumed to be zero. The sigmoid function always returns a value between
    0 and 1.

    Args:
        x: Input tensor.
    """
    output = ops.sigmoid(x)
    # Cache the logits to use for crossentropy loss.
    try:
        output._keras_logits = x
    except AttributeError:
        # We're dealing with a C-type.
        pass
    return output
Sigmoid activation function.

    It is defined as: `sigmoid(x) = 1 / (1 + exp(-x))`.

    For small values (<-5),
    `sigmoid` returns a value close to zero, and for large values (>5)
    the result of the function gets close to 1.

    Sigmoid is equivalent to a 2-element softmax, where the second element is
    assumed to be zero. The sigmoid function always returns a value between
    0 and 1.

    Args:
        x: Input tensor.
    
keras/src/activations/activations.py