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)zTimesFM model configuration    )strict   )PreTrainedConfig)auto_docstringzgoogle/timesfm-2.0-500m-pytorch)
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ee	d<   dZee	d	<   d
Zee	d<   dZee	d<   dZee	d<   dZee	d<   dZee	d<   dZee	d<   dZee	d<   dZee	d<   dZee   eedf   z  e	d<   dZee	d<   dZeez  e	d<   dZee	d<   d Zee	d!<   d"Zee	d#<   d$Zee	d%<   y&)'TimesFmConfiga(  
    patch_length (`int`, *optional*, defaults to 32):
        The length of one patch in the input sequence.
    context_length (`int`, *optional*, defaults to 512):
        The length of the input context.
    horizon_length (`int`, *optional*, defaults to 128):
        The length of the prediction horizon.
    freq_size (`int`, *optional*, defaults to 3):
        The number of frequency embeddings.
    tolerance (`float`, *optional*, defaults to 1e-06):
        The tolerance for the quantile loss.
    quantiles (`list[float]`, *optional*, defaults to `[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]`):
        The quantiles to predict.
    pad_val (`float`, *optional*, defaults to 1123581321.0):
        The value used to pad the predictions.
    attention_dropout (`float`, *optional*, defaults to 0.0):
        The dropout probability for the attention scores.
    use_positional_embedding (`bool`, *optional*, defaults to `False`):
        Whether to add positional embeddings.
    min_timescale (`int`, *optional*, defaults to 1):
        The start of the geometric positional index. Determines the periodicity of
        the added signal.
    max_timescale (`int`, *optional*, defaults to 10000):
        The end of the geometric positional index. Determines the frequency of the
        added signal.
    timesfmF    patch_lengthi   context_length   horizon_lengthr   	freq_size2   num_hidden_layersi   hidden_sizeintermediate_sizeP   head_dim   num_attention_headsgư>	tolerancerms_norm_eps)	g?g?g333333?g?g      ?g333333?gffffff?g?g?.	quantilesg  @bApad_valg        attention_dropoutuse_positional_embeddingg{Gz?initializer_range   min_timescalei'  max_timescaleN)__name__
__module____qualname____doc__
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