class_docstr: This is the configuration class to store the configuration of an [`PatchTSTModel`]. It is used to instantiate an
  PatchTST model according to the specified arguments, defining the model architecture.
  [ibm/patchtst](https://huggingface.co/ibm/patchtst) architecture.
  
  Configuration objects inherit from [`PretrainedConfig`] can be used to control the model outputs. Read the
  documentation from [`PretrainedConfig`] for more information.
  
  Args:
      num_input_channels (`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.
      context_length (`int`, *optional*, defaults to 32):
          The context length of the input sequence.
      distribution_output (`str`, *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".
      loss (`str`, *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".
      patch_length (`int`, *optional*, defaults to 1):
          Define the patch length of the patchification process.
      patch_stride (`int`, *optional*, defaults to 1):
          Define the stride of the patchification process.
      num_hidden_layers (`int`, *optional*, defaults to 3):
          Number of hidden layers.
      d_model (`int`, *optional*, defaults to 128):
          Dimensionality of the transformer layers.
      num_attention_heads (`int`, *optional*, defaults to 4):
          Number of attention heads for each attention layer in the Transformer encoder.
      share_embedding (`bool`, *optional*, defaults to `True`):
          Sharing the input embedding across all channels.
      channel_attention (`bool`, *optional*, defaults to `False`):
          Activate channel attention block in the Transformer to allow channels to attend each other.
      ffn_dim (`int`, *optional*, defaults to 512):
          Dimension of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
      norm_type (`str` , *optional*, defaults to `"batchnorm"`):
          Normalization at each Transformer layer. Can be `"batchnorm"` or `"layernorm"`.
      norm_eps (`float`, *optional*, defaults to 1e-05):
          A value added to the denominator for numerical stability of normalization.
      attention_dropout (`float`, *optional*, defaults to 0.0):
          The dropout probability for the attention probabilities.
      dropout (`float`, *optional*, defaults to 0.0):
          The dropout probability for all fully connected layers in the Transformer.
      positional_dropout (`float`, *optional*, defaults to 0.0):
          The dropout probability in the positional embedding layer.
      path_dropout (`float`, *optional*, defaults to 0.0):
          The dropout path in the residual block.
      ff_dropout (`float`, *optional*, defaults to 0.0):
          The dropout probability used between the two layers of the feed-forward networks.
      bias (`bool`, *optional*, defaults to `True`):
          Whether to add bias in the feed-forward networks.
      activation_function (`str`, *optional*, defaults to `"gelu"`):
          The non-linear activation function (string) in the Transformer.`"gelu"` and `"relu"` are supported.
      pre_norm (`bool`, *optional*, defaults to `True`):
          Normalization is applied before self-attention if pre_norm is set to `True`. Otherwise, normalization is
          applied after residual block.
      positional_encoding_type (`str`, *optional*, defaults to `"sincos"`):
          Positional encodings. Options `"random"` and `"sincos"` are supported.
      use_cls_token (`bool`, *optional*, defaults to `False`):
          Whether cls token is used.
      init_std (`float`, *optional*, defaults to 0.02):
          The standard deviation of the truncated normal weight initialization distribution.
      share_projection (`bool`, *optional*, defaults to `True`):
          Sharing the projection layer across different channels in the forecast head.
      scaling (`Union`, *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".
      do_mask_input (`bool`, *optional*):
          Apply masking during the pretraining.
      mask_type (`str`, *optional*, defaults to `"random"`):
          Masking type. Only `"random"` and `"forecast"` are currently supported.
      random_mask_ratio (`float`, *optional*, defaults to 0.5):
          Masking ratio applied to mask the input data during random pretraining.
      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.
      channel_consistent_masking (`bool`, *optional*, defaults to `False`):
          If channel consistent masking is True, all the channels will have the same masking pattern.
      unmasked_channel_indices (`list`, *optional*):
          Indices of channels that are not masked during pretraining. Values in the list are number between 1 and
          `num_input_channels`
      mask_value (`int`, *optional*, defaults to 0):
          Values in the masked patches will be filled by `mask_value`.
      pooling_type (`str`, *optional*, defaults to `"mean"`):
          Pooling of the embedding. `"mean"`, `"max"` and `None` are supported.
      head_dropout (`float`, *optional*, defaults to 0.0):
          The dropout probability for head.
      prediction_length (`int`, *optional*, defaults to 24):
          The prediction horizon that the model will output.
      num_targets (`int`, *optional*, defaults to 1):
          Number of targets for regression and classification tasks. For classification, it is the number of
          classes.
      output_range (`list`, *optional*):
          Output range for regression task. The range of output values can be set to enforce the model to produce
          values within a range.
      num_parallel_samples (`int`, *optional*, defaults to 100):
          The number of samples is generated in parallel for probabilistic prediction.
  
  
  ```python
  >>> from transformers import PatchTSTConfig, PatchTSTModel
  
  >>> # Initializing an PatchTST configuration with 12 time steps for prediction
  >>> configuration = PatchTSTConfig(prediction_length=12)
  
  >>> # Randomly initializing a model (with random weights) from the configuration
  >>> model = PatchTSTModel(configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  ```
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code: def __init__(
      self,
      # time series specific configuration
      num_input_channels: int = 1,
      context_length: int = 32,
      distribution_output: str = "student_t",
      loss: str = "mse",
      # PatchTST arguments
      patch_length: int = 1,
      patch_stride: int = 1,
      # Transformer architecture configuration
      num_hidden_layers: int = 3,
      d_model: int = 128,
      num_attention_heads: int = 4,
      share_embedding: bool = True,
      channel_attention: bool = False,
      ffn_dim: int = 512,
      norm_type: str = "batchnorm",
      norm_eps: float = 1e-05,
      attention_dropout: float = 0.0,
      dropout: float = 0.0,
      positional_dropout: float = 0.0,
      path_dropout: float = 0.0,
      ff_dropout: float = 0.0,
      bias: bool = True,
      activation_function: str = "gelu",
      pre_norm: bool = True,
      positional_encoding_type: str = "sincos",
      use_cls_token: bool = False,
      init_std: float = 0.02,
      share_projection: bool = True,
      scaling: Optional[Union[str, bool]] = "std",
      # mask pretraining
      do_mask_input: Optional[bool] = None,
      mask_type: str = "random",
      random_mask_ratio: float = 0.5,
      num_forecast_mask_patches: Optional[Union[List[int], int]] = [2],
      channel_consistent_masking: Optional[bool] = False,
      unmasked_channel_indices: Optional[List[int]] = None,
      mask_value: int = 0,
      # head
      pooling_type: str = "mean",
      head_dropout: float = 0.0,
      prediction_length: int = 24,
      num_targets: int = 1,
      output_range: Optional[List] = None,
      # distribution head
      num_parallel_samples: int = 100,
      **kwargs,
  ):
      # time series specific configuration
      self.context_length = context_length
      self.num_input_channels = num_input_channels  # n_vars
      self.loss = loss
      self.distribution_output = distribution_output
      self.num_parallel_samples = num_parallel_samples
  
      # Transformer architecture configuration
      self.d_model = d_model
      self.num_attention_heads = num_attention_heads
      self.ffn_dim = ffn_dim
      self.num_hidden_layers = num_hidden_layers
      self.dropout = dropout
      self.attention_dropout = attention_dropout
      self.share_embedding = share_embedding
      self.channel_attention = channel_attention
      self.norm_type = norm_type
      self.norm_eps = norm_eps
      self.positional_dropout = positional_dropout
      self.path_dropout = path_dropout
      self.ff_dropout = ff_dropout
      self.bias = bias
      self.activation_function = activation_function
      self.pre_norm = pre_norm
      self.positional_encoding_type = positional_encoding_type
      self.use_cls_token = use_cls_token
      self.init_std = init_std
      self.scaling = scaling
  
      # PatchTST parameters
      self.patch_length = patch_length
      self.patch_stride = patch_stride
  
      # Mask pretraining
      self.do_mask_input = do_mask_input
      self.mask_type = mask_type
      self.random_mask_ratio = random_mask_ratio  # for random masking
      self.num_forecast_mask_patches = (
          num_forecast_mask_patches  # for forecast masking
      )
      self.channel_consistent_masking = channel_consistent_masking
      self.unmasked_channel_indices = unmasked_channel_indices
      self.mask_value = mask_value
  
      # general head params
      self.pooling_type = pooling_type
      self.head_dropout = head_dropout
  
      # For prediction head
      self.share_projection = share_projection
      self.prediction_length = prediction_length
  
      # For prediction and regression head
      self.num_parallel_samples = num_parallel_samples
  
      # Regression
      self.num_targets = num_targets
      self.output_range = output_range
  
      super().__init__(**kwargs)
  
imports: from typing import List, Optional, Union
  from transformers.configuration_utils import PretrainedConfig
  from transformers.utils import logging
label_desc: Use image node_id 1 to create a new PatchTSTConfig object from inherited base classes: PretrainedConfig with example: obj = PatchTSTConfig(num_input_channels, context_length, distribution_output, loss, patch_length, patch_stride, num_hidden_layers, d_model, num_attention_heads, share_embedding, channel_attention, ffn_dim, norm_type, norm_eps, attention_dropout, dropout, positional_dropout, path_dropout, ff_dropout, bias, activation_function, pre_norm, positional_encoding_type, use_cls_token, init_std, share_projection, scaling, do_mask_input, mask_type, random_mask_ratio, num_forecast_mask_patches, channel_consistent_masking, unmasked_channel_indices, mask_value, pooling_type, head_dropout, prediction_length, num_targets, output_range, num_parallel_samples)