class_docstr: Sparse Random Projection using Paddle operations.
  
  Args:
      eps (float, optional): Minimum distortion rate parameter for calculating
          Johnson-Lindenstrauss minimum dimensions. Defaults to 0.1.
      random_state (Optional[int], optional): Uses the seed to set the random
          state for sample_without_replacement function. Defaults to None.
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docstr: Project the data by using matrix product with the random matrix.
  
  Args:
      embedding (Tensor): Embedding of shape (n_samples, n_features)
          The input data to project into a smaller dimensional space
  
  Returns:
      projected_embedding (Tensor): Sparse matrix of shape
          (n_samples, n_components) Projected array.
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code: def transform(self, embedding: Tensor) -> Tensor:
      """Project the data by using matrix product with the random matrix.
  
      Args:
          embedding (Tensor): Embedding of shape (n_samples, n_features)
              The input data to project into a smaller dimensional space
  
      Returns:
          projected_embedding (Tensor): Sparse matrix of shape
              (n_samples, n_components) Projected array.
      """
      return super().transform(embedding)
  
imports: import warnings
  from typing import Optional
  import paddle
  from paddle import Tensor
  import numpy
  from sklearn.utils.random import sample_without_replacement
label_desc: Use image node_id 3 for calling the SparseRandomProjection obj's underlying member method code with example usage: obj.transform(embedding) and returns: super