class_docstr: Neural Autoregressive Distribution Estimator [1].
  
  [1]: https://arxiv.org/abs/1605.02226
  
  Args:
    num_dims: The number of binary dimensions for each observation.
    num_hidden: The number of hidden units in the NADE.
    internal_bias: Whether the model should maintain its own bias varaibles.
        Otherwise, external values must be passed to `log_prob` and `sample`.
class_docstr_tok: ["Neural","Autoregressive","Distribution","Estimator","[","1","]",".","[","1","]",":","https",":","\/\/arxiv.org\/abs\/1605.02226","Args",":","num_dims",":","The","number","of","binary","dimensions","for","each","observation",".","num_hidden",":","The","number","of","hidden","units","in","the","NADE",".","internal_bias",":","Whether","the","model","should","maintain","its","own","bias","varaibles",".","Otherwise",",","external","values","must","be","passed","to","`","log_prob","`","and","`","sample","`","."]
docstr: The number of input/output dimensions of the NADE.
docstr_tok: ["The","number","of","input\/output","dimensions","of","the","NADE","."]
code: def num_dims(self):
      """The number of input/output dimensions of the NADE."""
      return self._num_dims
  
file_path: magenta/magenta/common/nade.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import tensorflow.compat.v1
  import tensorflow_probability
label_desc: Use image node_id 3 for calling the Nade obj's underlying member method code with example usage: obj.num_dims() and returns: self