def __init__(self,<EOL>total_count,<EOL>logits=None,<EOL>probs=None,<EOL>validate_args=False,<EOL>allow_nan_stats=True,<EOL>name="<STR_LIT>"):
parameters = dict(locals())<EOL>with tf.name_scope(name) as name:<EOL><INDENT>dtype = dtype_util.common_dtype([total_count, logits, probs], tf.float32)<EOL>self._total_count = tf.convert_to_tensor(<EOL>value=total_count, name="<STR_LIT>", dtype=dtype)<EOL>if validate_args:<EOL><INDENT>self._total_count = (<EOL>distribution_util.embed_check_nonnegative_integer_form(<EOL>self._total_count))<EOL><DEDENT>self._logits, self._probs = distribution_util.get_logits_and_probs(<EOL>logits=logits,<EOL>probs=probs,<EOL>multidimensional=True,<EOL>validate_args=validate_args,<EOL>name=name,<EOL>dtype=dtype)<EOL>self._mean_val = self._total_count[..., tf.newaxis] * self._probs<EOL><DEDENT>super(Multinomial, self).__init__(<EOL>dtype=dtype,<EOL>reparameterization_type=reparameterization.NOT_REPARAMETERIZED,<EOL>validate_args=validate_args,<EOL>allow_nan_stats=allow_nan_stats,<EOL>parameters=parameters,<EOL>graph_parents=[self._total_count, self._logits, self._probs],<EOL>name=name)<EOL>
Initialize a batch of Multinomial distributions.

        Args:
          total_count: Non-negative floating point tensor with shape broadcastable
            to `[N1,..., Nm]` with `m >= 0`. Defines this as a batch of
            `N1 x ... x Nm` different Multinomial distributions. Its components
            should be equal to integer values.
          logits: Floating point tensor representing unnormalized log-probabilities
            of a positive event with shape broadcastable to
            `[N1,..., Nm, K]` `m >= 0`, and the same dtype as `total_count`. Defines
            this as a batch of `N1 x ... x Nm` different `K` class Multinomial
            distributions. Only one of `logits` or `probs` should be passed in.
          probs: Positive floating point tensor with shape broadcastable to
            `[N1,..., Nm, K]` `m >= 0` and same dtype as `total_count`. Defines
            this as a batch of `N1 x ... x Nm` different `K` class Multinomial
            distributions. `probs`'s components in the last portion of its shape
            should sum to `1`. Only one of `logits` or `probs` should be passed in.
          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.
          allow_nan_stats: Python `bool`, default `True`. When `True`, statistics
            (e.g., mean, mode, variance) use the value "`NaN`" to indicate the
            result is undefined. When `False`, an exception is raised if one or
            more of the statistic's batch members are undefined.
          name: Python `str` name prefixed to Ops created by this class.