class_docstr: Global average pooling operation for temporal data.
  
  Args:
      data_format: string, either `"channels_last"` or `"channels_first"`.
          The ordering of the dimensions in the inputs. `"channels_last"`
          corresponds to inputs with shape `(batch, steps, features)`
          while `"channels_first"` corresponds to inputs with shape
          `(batch, features, steps)`. It defaults to the `image_data_format`
          value found in your Keras config file at `~/.keras/keras.json`.
          If you never set it, then it will be `"channels_last"`.
      keepdims: A boolean, whether to keep the temporal dimension or not.
          If `keepdims` is `False` (default), the rank of the tensor is
          reduced for spatial dimensions. If `keepdims` is `True`, the
          temporal dimension are retained with length 1.
          The behavior is the same as for `tf.reduce_mean` or `np.mean`.
  
  Call arguments:
      inputs: A 3D tensor.
      mask: Binary tensor of shape `(batch_size, steps)` indicating whether
          a given step should be masked (excluded from the average).
  
  Input shape:
  
  - If `data_format='channels_last'`:
      3D tensor with shape:
      `(batch_size, steps, features)`
  - If `data_format='channels_first'`:
      3D tensor with shape:
      `(batch_size, features, steps)`
  
  Output shape:
  
  - If `keepdims=False`:
      2D tensor with shape `(batch_size, features)`.
  - If `keepdims=True`:
      - If `data_format="channels_last"`:
          3D tensor with shape `(batch_size, 1, features)`
      - If `data_format="channels_first"`:
          3D tensor with shape `(batch_size, features, 1)`
  
  Example:
  
  >>> x = np.random.rand(2, 3, 4)
  >>> y = keras.layers.GlobalAveragePooling1D()(x)
  >>> y.shape
  (2, 4)
code: def __init__(self, data_format=None, keepdims=False, **kwargs):
      super().__init__(
          pool_dimensions=1,
          data_format=data_format,
          keepdims=keepdims,
          **kwargs,
      )
      self.supports_masking = True
  
file_path: keras/keras/layers/pooling/global_average_pooling1d.py
imports: from keras import backend
  from keras import ops
  from keras.api_export import keras_export
  from keras.layers.pooling.base_global_pooling import BaseGlobalPooling
label_desc: Use image node_id 1 to create a new GlobalAveragePooling1D object from inherited base classes: BaseGlobalPooling with example: obj = GlobalAveragePooling1D(data_format, keepdims)