class_docstr: This is the configuration class to store the configuration of an [`AutoformerModel`]. It is used to instantiate an
  Autoformer model according to the specified arguments, defining the model architecture. Instantiating a
  configuration with the defaults will yield a similar configuration to that of the Autoformer
  [huggingface/autoformer-tourism-monthly](https://huggingface.co/huggingface/autoformer-tourism-monthly)
  architecture.
  
  Configuration objects inherit from [`PretrainedConfig`] can be used to control the model outputs. Read the
  documentation from [`PretrainedConfig`] for more information.
  
  Args:
      prediction_length (`int`):
          The prediction length for the decoder. In other words, the prediction horizon of the model.
      context_length (`int`, *optional*, defaults to `prediction_length`):
          The context length for the encoder. If unset, the context length will be the same as the
          `prediction_length`.
      distribution_output (`string`, *optional*, defaults to `"student_t"`):
          The distribution emission head for the model. Could be either "student_t", "normal" or "negative_binomial".
      loss (`string`, *optional*, defaults to `"nll"`):
          The loss function for the model corresponding to the `distribution_output` head. For parametric
          distributions it is the negative log likelihood (nll) - which currently is the only supported one.
      input_size (`int`, *optional*, defaults to 1):
          The size of the target variable which by default is 1 for univariate targets. Would be > 1 in case of
          multivariate targets.
      lags_sequence (`list[int]`, *optional*, defaults to `[1, 2, 3, 4, 5, 6, 7]`):
          The lags of the input time series as covariates often dictated by the frequency. Default is `[1, 2, 3, 4,
          5, 6, 7]`.
      scaling (`bool`, *optional* defaults to `True`):
          Whether to scale the input targets.
      num_time_features (`int`, *optional*, defaults to 0):
          The number of time features in the input time series.
      num_dynamic_real_features (`int`, *optional*, defaults to 0):
          The number of dynamic real valued features.
      num_static_categorical_features (`int`, *optional*, defaults to 0):
          The number of static categorical features.
      num_static_real_features (`int`, *optional*, defaults to 0):
          The number of static real valued features.
      cardinality (`list[int]`, *optional*):
          The cardinality (number of different values) for each of the static categorical features. Should be a list
          of integers, having the same length as `num_static_categorical_features`. Cannot be `None` if
          `num_static_categorical_features` is > 0.
      embedding_dimension (`list[int]`, *optional*):
          The dimension of the embedding for each of the static categorical features. Should be a list of integers,
          having the same length as `num_static_categorical_features`. Cannot be `None` if
          `num_static_categorical_features` is > 0.
      d_model (`int`, *optional*, defaults to 64):
          Dimensionality of the transformer layers.
      encoder_layers (`int`, *optional*, defaults to 2):
          Number of encoder layers.
      decoder_layers (`int`, *optional*, defaults to 2):
          Number of decoder layers.
      encoder_attention_heads (`int`, *optional*, defaults to 2):
          Number of attention heads for each attention layer in the Transformer encoder.
      decoder_attention_heads (`int`, *optional*, defaults to 2):
          Number of attention heads for each attention layer in the Transformer decoder.
      encoder_ffn_dim (`int`, *optional*, defaults to 32):
          Dimension of the "intermediate" (often named feed-forward) layer in encoder.
      decoder_ffn_dim (`int`, *optional*, defaults to 32):
          Dimension of the "intermediate" (often named feed-forward) layer in decoder.
      activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
          The non-linear activation function (function or string) in the encoder and decoder. If string, `"gelu"` and
          `"relu"` are supported.
      dropout (`float`, *optional*, defaults to 0.1):
          The dropout probability for all fully connected layers in the encoder, and decoder.
      encoder_layerdrop (`float`, *optional*, defaults to 0.1):
          The dropout probability for the attention and fully connected layers for each encoder layer.
      decoder_layerdrop (`float`, *optional*, defaults to 0.1):
          The dropout probability for the attention and fully connected layers for each decoder layer.
      attention_dropout (`float`, *optional*, defaults to 0.1):
          The dropout probability for the attention probabilities.
      activation_dropout (`float`, *optional*, defaults to 0.1):
          The dropout probability used between the two layers of the feed-forward networks.
      num_parallel_samples (`int`, *optional*, defaults to 100):
          The number of samples to generate in parallel for each time step of inference.
      init_std (`float`, *optional*, defaults to 0.02):
          The standard deviation of the truncated normal weight initialization distribution.
      use_cache (`bool`, *optional*, defaults to `True`):
          Whether to use the past key/values attentions (if applicable to the model) to speed up decoding.
      label_length (`int`, *optional*, defaults to 10):
          Start token length of the Autoformer decoder, which is used for direct multi-step prediction (i.e.
          non-autoregressive generation).
      moving_average (`int`, defaults to 25):
          The window size of the moving average. In practice, it's the kernel size in AvgPool1d of the Decomposition
          Layer.
      autocorrelation_factor (`int`, defaults to 3):
          "Attention" (i.e. AutoCorrelation mechanism) factor which is used to find top k autocorrelations delays.
          It's recommended in the paper to set it to a number between 1 and 5.
  
  
      Example:
  
  ```python
  >>> from transformers import AutoformerConfig, AutoformerModel
  
  >>> # Initializing a default Autoformer configuration
  >>> configuration = AutoformerConfig()
  
  >>> # Randomly initializing a model (with random weights) from the configuration
  >>> model = AutoformerModel(configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  ```
code: def _number_of_features(self) -> int:
      return (
          sum(self.embedding_dimension)
          + self.num_dynamic_real_features
          + self.num_time_features
          + self.num_static_real_features
          + self.input_size
          * 2  # the log1p(abs(loc)) and log(scale) features
      )
  
imports: from typing import List, Optional
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
label_desc: Use image node_id 2 for calling the AutoformerConfig obj's underlying member method code with example usage: obj._number_of_features() and returns: unknown