class_docstr: Turns positive integers (indexes) into dense vectors of fixed size.
  
  e.g. `[[4], [20]] -> [[0.25, 0.1], [0.6, -0.2]]`
  
  This layer can only be used on positive integer inputs of a fixed range.
  
  Example:
  
  >>> model = keras.Sequential()
  >>> model.add(keras.layers.Embedding(1000, 64, input_length=10))
  >>> # The model will take as input an integer matrix of size (batch,
  >>> # input_length), and the largest integer (i.e. word index) in the input
  >>> # should be no larger than 999 (vocabulary size).
  >>> # Now model.output_shape is (None, 10, 64), where `None` is the batch
  >>> # dimension.
  >>> input_array = np.random.randint(1000, size=(32, 10))
  >>> model.compile('rmsprop', 'mse')
  >>> output_array = model.predict(input_array)
  >>> print(output_array.shape)
  (32, 10, 64)
  
  Args:
      input_dim: Integer. Size of the vocabulary,
          i.e. maximum integer index + 1.
      output_dim: Integer. Dimension of the dense embedding.
      embeddings_initializer: Initializer for the `embeddings`
          matrix (see `keras.initializers`).
      embeddings_regularizer: Regularizer function applied to
          the `embeddings` matrix (see `keras.regularizers`).
      embeddings_constraint: Constraint function applied to
          the `embeddings` matrix (see `keras.constraints`).
      mask_zero: Boolean, whether or not the input value 0 is a special
          "padding" value that should be masked out.
          This is useful when using recurrent layers which
          may take variable length input. If this is `True`,
          then all subsequent layers in the model need
          to support masking or an exception will be raised.
          If mask_zero is set to True, as a consequence,
          index 0 cannot be used in the vocabulary (input_dim should
          equal size of vocabulary + 1).
      lora_rank: Optional integer. If set, the layer's forward pass
          will implement LoRA (Low-Rank Adaptation)
          with the provided rank. LoRA sets the layer's embeddings
          matrix to non-trainable and replaces it with a delta over the
          original matrix, obtained via multiplying two lower-rank
          trainable matrices. This can be useful to reduce the
          computation cost of fine-tuning large embedding layers.
          You can also enable LoRA on an existing
          `Embedding` layer by calling `layer.enable_lora(rank)`.
  
  Input shape:
      2D tensor with shape: `(batch_size, input_length)`.
  
  Output shape:
      3D tensor with shape: `(batch_size, input_length, output_dim)`.
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code: def embeddings(self):
      if self.lora_enabled:
          return self._embeddings + ops.matmul(
              self.lora_embeddings_a, self.lora_embeddings_b
          )
      return self._embeddings
  
file_path: keras/keras/layers/core/embedding.py
imports: import numpy
  from keras import constraints
  from keras import initializers
  from keras import ops
  from keras import regularizers
  from keras.api_export import keras_export
  from keras.layers.layer import Layer
label_desc: Use image node_id 3 for calling the Embedding obj's underlying member method code with example usage: obj.embeddings() and returns: self, unknown