parameters: (uniform=True, seed=None, dtype=dtypes.float32)
return_statement: return variance_scaling_initializer(factor=1.0, mode='FAN_AVG',
                                        uniform=uniform, seed=seed, dtype=dtype)
docstring: Returns an initializer performing "Xavier" initialization for weights.
  
    This function implements the weight initialization from:
  
    Xavier Glorot and Yoshua Bengio (2010):
             Understanding the difficulty of training deep feedforward neural
             networks. International conference on artificial intelligence and
             statistics.
  
    This initializer is designed to keep the scale of the gradients roughly the
    same in all layers. In uniform distribution this ends up being the range:
    `x = sqrt(6. / (in + out)); [-x, x]` and for normal distribution a standard
    deviation of `sqrt(3. / (in + out))` is used.
  
    Args:
      uniform: Whether to use uniform or normal distributed random initialization.
      seed: A Python integer. Used to create random seeds. See
        [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
        for behavior.
      dtype: The data type. Only floating point types are supported.
  
    Returns:
      An initializer for a weight matrix.
docstring_summary: Returns an initializer performing "Xavier" initialization for weights.
docstring_tokens: Returns an initializer performing Xavier initialization for weights .
function: def xavier_initializer(uniform=True, seed=None, dtype=dtypes.float32):
    """Returns an initializer performing "Xavier" initialization for weights.
  
    This function implements the weight initialization from:
  
    Xavier Glorot and Yoshua Bengio (2010):
             Understanding the difficulty of training deep feedforward neural
             networks. International conference on artificial intelligence and
             statistics.
  
    This initializer is designed to keep the scale of the gradients roughly the
    same in all layers. In uniform distribution this ends up being the range:
    `x = sqrt(6. / (in + out)); [-x, x]` and for normal distribution a standard
    deviation of `sqrt(3. / (in + out))` is used.
  
    Args:
      uniform: Whether to use uniform or normal distributed random initialization.
      seed: A Python integer. Used to create random seeds. See
        [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
        for behavior.
      dtype: The data type. Only floating point types are supported.
  
    Returns:
      An initializer for a weight matrix.
    """
    return variance_scaling_initializer(factor=1.0, mode='FAN_AVG',
                                        uniform=uniform, seed=seed, dtype=dtype)
function_tokens: def xavier_initializer ( uniform = True , seed = None , dtype = dtypes . float32 ) : return variance_scaling_initializer ( factor = 1.0 , mode = 'FAN_AVG' , uniform = uniform , seed = seed , dtype = dtype )