def __init__(self,<EOL>event_shape=(),<EOL>convert_to_tensor_fn=tfd.Distribution.sample,<EOL>sample_dtype=None,<EOL>validate_args=False,<EOL>**kwargs):
convert_to_tensor_fn = _get_convert_to_tensor_fn(convert_to_tensor_fn)<EOL>kwargs.pop('<STR_LIT>', None)<EOL>super(IndependentBernoulli, self).__init__(<EOL>lambda t: IndependentBernoulli.new(  <EOL>t, event_shape, sample_dtype, validate_args),<EOL>convert_to_tensor_fn,<EOL>**kwargs)<EOL>self._event_shape = event_shape<EOL>self._convert_to_tensor_fn = convert_to_tensor_fn<EOL>self._sample_dtype = sample_dtype<EOL>self._validate_args = validate_args<EOL>
Initialize the `IndependentBernoulli` layer.

        Args:
          event_shape: integer vector `Tensor` representing the shape of single
            draw from this distribution.
          convert_to_tensor_fn: Python `callable` that takes a `tfd.Distribution`
            instance and returns a `tf.Tensor`-like object. For examples, see
            `class` docstring.
            Default value: `tfd.Distribution.sample`.
          sample_dtype: `dtype` of samples produced by this distribution.
            Default value: `None` (i.e., previous layer's `dtype`).
          validate_args: Python `bool`, default `False`. When `True` distribution
            parameters are checked for validity despite possibly degrading runtime
            performance. When `False` invalid inputs may silently render incorrect
            outputs.
            Default value: `False`.
          **kwargs: Additional keyword arguments passed to `tf.keras.Layer`.