class_docstr: This is the configuration class to store the configuration of a [`PatchTSMixerModel`]. It is used to instantiate a
  PatchTSMixer 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 PatchTSMixer
  [ibm/patchtsmixer-etth1-pretrain](https://huggingface.co/ibm/patchtsmixer-etth1-pretrain) architecture.
  
  Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
  documentation from [`PretrainedConfig`] for more information.
  
  Args:
      context_length (`int`, *optional*, defaults to 32):
          The context/history length for the input sequence.
      patch_len (`int`, *optional*, defaults to 8):
          The patch length for the input sequence.
      num_input_channels (`int`, *optional*, defaults to 1):
          Number of input variates. For Univariate, set it to 1.
      patch_stride (`int`, *optional*, defaults to 8):
          Determines the overlap between two consecutive patches. Set it to patch_length (or greater), if we want
          non-overlapping patches.
      num_parallel_samples (`int`, *optional*, defaults to 100):
          The number of samples to generate in parallel for probabilistic forecast.
      d_model (`int`, *optional*, defaults to 8):
          Hidden dimension of the model. Recommended to set it as a multiple of patch_length (i.e. 2-5X of
          patch_len). Larger value indicates more complex model.
      expansion_factor (`int`, *optional*, defaults to 2):
          Expansion factor to use inside MLP. Recommended range is 2-5. Larger value indicates more complex model.
      num_layers (`int`, *optional*, defaults to 3):
          Number of layers to use. Recommended range is 3-15. Larger value indicates more complex model.
      dropout (`float`, *optional*, defaults to 0.2):
          The dropout probability the `PatchTSMixer` backbone. Recommended range is 0.2-0.7
      mode (`str`, *optional*, defaults to `"common_channel"`):
          Mixer Mode. Determines how to process the channels. Allowed values: "common_channel", "mix_channel". In
          "common_channel" mode, we follow Channel-independent modelling with no explicit channel-mixing. Channel
          mixing happens in an implicit manner via shared weights across channels. (preferred first approach) In
          "mix_channel" mode, we follow explicit channel-mixing in addition to patch and feature mixer. (preferred
          approach when channel correlations are very important to model)
      gated_attn (`bool`, *optional*, defaults to `True`):
          Enable Gated Attention.
      norm_mlp (`str`, *optional*, defaults to `"LayerNorm"`):
          Normalization layer (BatchNorm or LayerNorm).
      self_attn (`bool`, *optional*, defaults to `False`):
          Enable Tiny self attention across patches. This can be enabled when the output of Vanilla PatchTSMixer with
          gated attention is not satisfactory. Enabling this leads to explicit pair-wise attention and modelling
          across patches.
      self_attn_heads (`int`, *optional*, defaults to 1):
          Number of self-attention heads. Works only when `self_attn` is set to `True`.
      use_positional_encoding (`bool`, *optional*, defaults to `False`):
          Enable the use of positional embedding for the tiny self-attention layers. Works only when `self_attn` is
          set to `True`.
      positional_encoding_type (`str`, *optional*, defaults to `"sincos"`):
          Positional encodings. Options `"random"` and `"sincos"` are supported. Works only when
          `use_positional_encoding` is set to `True`
      scaling (`string` or `bool`, *optional*, defaults to `"std"`):
          Whether to scale the input targets via "mean" scaler, "std" scaler or no scaler if `None`. If `True`, the
          scaler is set to "mean".
      loss (`string`, *optional*, defaults to `"mse"`):
          The loss function for the model corresponding to the `distribution_output` head. For parametric
          distributions it is the negative log likelihood ("nll") and for point estimates it is the mean squared
          error "mse".
      init_std (`float`, *optional*, defaults to 0.02):
          The standard deviation of the truncated normal weight initialization distribution.
      post_init (`bool`, *optional*, defaults to `False`):
          Whether to use custom weight initialization from `transformers` library, or the default initialization in
          `PyTorch`. Setting it to `False` performs `PyTorch` weight initialization.
      norm_eps (`float`, *optional*, defaults to 1e-05):
          A value added to the denominator for numerical stability of normalization.
      mask_type (`str`, *optional*, defaults to `"random"`):
          Type of masking to use for Masked Pretraining mode. Allowed values are "random", "forecast". In Random
          masking, points are masked randomly. In Forecast masking, points are masked towards the end.
      random_mask_ratio (`float`, *optional*, defaults to 0.5):
          Masking ratio to use when `mask_type` is `random`. Higher value indicates more masking.
      num_forecast_mask_patches (`int` or `list`, *optional*, defaults to `[2]`):
          Number of patches to be masked at the end of each batch sample. If it is an integer, all the samples in the
          batch will have the same number of masked patches. If it is a list, samples in the batch will be randomly
          masked by numbers defined in the list. This argument is only used for forecast pretraining.
      mask_value (`float`, *optional*, defaults to `0.0`):
          Mask value to use.
      masked_loss (`bool`, *optional*, defaults to `True`):
          Whether to compute pretraining loss only at the masked portions, or on the entire output.
      channel_consistent_masking (`bool`, *optional*, defaults to `True`):
          When true, masking will be same across all channels of a timeseries. Otherwise, masking positions will vary
          across channels.
      unmasked_channel_indices (`list`, *optional*):
          Channels that are not masked during pretraining.
      head_dropout (`float`, *optional*, defaults to 0.2):
          The dropout probability the `PatchTSMixer` head.
      distribution_output (`string`, *optional*, defaults to `"student_t"`):
          The distribution emission head for the model when loss is "nll". Could be either "student_t", "normal" or
          "negative_binomial".
      prediction_length (`int`, *optional*, defaults to 16):
          Number of time steps to forecast for a forecasting task. Also known as the Forecast Horizon.
      prediction_channel_indices (`list`, *optional*):
          List of channel indices to forecast. If None, forecast all channels. Target data is expected to have all
          channels and we explicitly filter the channels in prediction and target before loss computation.
      num_targets (`int`, *optional*, defaults to 3):
          Number of targets (dimensionality of the regressed variable) for a regression task.
      output_range (`list`, *optional*):
          Output range to restrict for the regression task. Defaults to None.
      head_aggregation (`str`, *optional*, defaults to `"max_pool"`):
          Aggregation mode to enable for classification or regression task. Allowed values are `None`, "use_last",
          "max_pool", "avg_pool".
  
  Example:
  
  ```python
  >>> from transformers import PatchTSMixerConfig, PatchTSMixerModel
  
  >>> # Initializing a default PatchTSMixer configuration
  >>> configuration = PatchTSMixerConfig()
  
  >>> # Randomly initializing a model (with random weights) from the configuration
  >>> model = PatchTSMixerModel(configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  ```
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code: def __init__(
      self,
      # Time series specific configuration
      context_length: int = 32,
      patch_len: int = 8,
      num_input_channels: int = 1,
      patch_stride: int = 8,
      num_parallel_samples: int = 100,
      # General model configuration
      d_model: int = 8,
      expansion_factor: int = 2,
      num_layers: int = 3,
      dropout: float = 0.2,
      mode: str = "common_channel",
      gated_attn: bool = True,
      norm_mlp: str = "LayerNorm",
      self_attn: bool = False,
      self_attn_heads: int = 1,
      use_positional_encoding: bool = False,
      positional_encoding_type: str = "sincos",
      scaling: Optional[Union[str, bool]] = "std",
      loss: str = "mse",
      init_std: float = 0.02,
      post_init: bool = False,
      norm_eps: float = 1e-5,
      # Pretrain model configuration
      mask_type: str = "random",
      random_mask_ratio: float = 0.5,
      num_forecast_mask_patches: Optional[Union[List[int], int]] = [2],
      mask_value: int = 0,
      masked_loss: bool = True,
      channel_consistent_masking: bool = True,
      unmasked_channel_indices: Optional[List[int]] = None,
      # General head configuration
      head_dropout: float = 0.2,
      distribution_output: str = "student_t",
      # Prediction head configuration
      prediction_length: int = 16,
      prediction_channel_indices: list = None,
      # Classification/Regression configuration
      num_targets: int = 3,
      output_range: list = None,
      head_aggregation: str = "max_pool",
      **kwargs,
  ):
      self.num_input_channels = num_input_channels
      self.context_length = context_length
      self.patch_length = patch_len
      self.patch_stride = patch_stride
      self.d_model = d_model
      self.expansion_factor = expansion_factor
      self.num_layers = num_layers
      self.dropout = dropout
      self.mode = mode
      self.gated_attn = gated_attn
      self.norm_mlp = norm_mlp
      self.scaling = scaling
      self.head_dropout = head_dropout
      self.num_patches = (
          max(context_length, patch_len) - patch_len
      ) // patch_stride + 1
      self.mask_type = mask_type
      self.random_mask_ratio = random_mask_ratio
      self.num_forecast_mask_patches = num_forecast_mask_patches
      self.mask_value = mask_value
      self.channel_consistent_masking = channel_consistent_masking
      self.masked_loss = masked_loss
      self.patch_last = True
      self.use_positional_encoding = use_positional_encoding
      self.positional_encoding_type = positional_encoding_type
      self.prediction_length = prediction_length
      self.prediction_channel_indices = prediction_channel_indices
      self.num_targets = num_targets
      self.output_range = output_range
      self.head_aggregation = head_aggregation
      self.self_attn = self_attn
      self.self_attn_heads = self_attn_heads
      self.init_std = init_std
      self.post_init = post_init
      self.distribution_output = distribution_output
      self.loss = loss
      self.num_parallel_samples = num_parallel_samples
      self.unmasked_channel_indices = unmasked_channel_indices
      self.norm_eps = norm_eps
      super().__init__(**kwargs)
  
imports: from typing import List, Optional, Union
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
label_desc: Use image node_id 1 to create a new PatchTSMixerConfig object from inherited base classes: PretrainedConfig with example: obj = PatchTSMixerConfig(context_length, patch_len, num_input_channels, patch_stride, num_parallel_samples, d_model, expansion_factor, num_layers, dropout, mode, gated_attn, norm_mlp, _attn, _attn_heads, use_positional_encoding, positional_encoding_type, scaling, loss, init_std, post_init, norm_eps, mask_type, random_mask_ratio, num_forecast_mask_patches, mask_value, masked_loss, channel_consistent_masking, unmasked_channel_indices, head_dropout, distribution_output, prediction_length, prediction_channel_indices, num_targets, output_range, head_aggregation)