class_docstr: Global min pooling operation for temporal data.
  # Input shape
      3D tensor with shape: `(batch_size, steps, features)`.
  # Output shape
      2D tensor with shape:
      `(batch_size, features)`
class_docstr_tok: ["Global","min","pooling","operation","for","temporal","data",".","#","Input","shape","3D","tensor","with","shape",":","`","(","batch_size",",","steps",",","features",")","`",".","#","Output","shape","2D","tensor","with","shape",":","`","(","batch_size",",","features",")","`"]
code: def compute_output_shape(self, input_shape):
      return input_shape[0], input_shape[2]
  
imports: from tensorflow.keras.models import Model, model_from_json
  from tensorflow.keras.layers import InputSpec, Dense, Conv1D, Layer, Input, concatenate, add
  from tensorflow.keras.metrics import categorical_accuracy, categorical_crossentropy, binary_accuracy, binary_crossentropy
  from tensorflow.keras.utils import to_categorical
  from sklearn.base import ClassifierMixin, TransformerMixin
  from sklearn.utils import check_array, check_X_y
  from sklearn.utils.validation import check_is_fitted
  from sklearn.utils.multiclass import unique_labels
  from tensorflow.keras.regularizers import l2
  from tensorflow.keras.initializers import Initializer
  import tensorflow.keras.backend
  import numpy
  import tensorflow
  import warnings
  from ..utils import to_time_series_dataset, check_dims, ts_size
  from ..bases import BaseModelPackage, TimeSeriesBaseEstimator
  from ..clustering import TimeSeriesKMeans
  from ..preprocessing import TimeSeriesScalerMinMax
label_desc: Use image node_id 2 for calling the GlobalArgminPooling1D obj's underlying member method code with example usage: obj.compute_output_shape(input_shape) and returns: input_shape,  input_shape