class_docstr: Dimensionality reduction using truncated SVD (aka LSA).
  
  This transformer performs linear dimensionality reduction by means of
  truncated singular value decomposition (SVD). Contrary to PCA, this
  estimator does not center the data before computing the singular value
  decomposition. This means it can work with sparse matrices
  efficiently.
  
  In particular, truncated SVD works on term count/tf-idf matrices as
  returned by the vectorizers in :mod:`sklearn.feature_extraction.text`. In
  that context, it is known as latent semantic analysis (LSA).
  
  This estimator supports two algorithms: a fast randomized SVD solver, and
  a "naive" algorithm that uses ARPACK as an eigensolver on `X * X.T` or
  `X.T * X`, whichever is more efficient.
  
  Read more in the :ref:`User Guide <LSA>`.
  
  Parameters
  ----------
  n_components : int, default=2
      Desired dimensionality of output data.
      If algorithm='arpack', must be strictly less than the number of features.
      If algorithm='randomized', must be less than or equal to the number of features.
      The default value is useful for visualisation. For LSA, a value of
      100 is recommended.
  
  algorithm : {'arpack', 'randomized'}, default='randomized'
      SVD solver to use. Either "arpack" for the ARPACK wrapper in SciPy
      (scipy.sparse.linalg.svds), or "randomized" for the randomized
      algorithm due to Halko (2009).
  
  n_iter : int, default=5
      Number of iterations for randomized SVD solver. Not used by ARPACK. The
      default is larger than the default in
      :func:`~sklearn.utils.extmath.randomized_svd` to handle sparse
      matrices that may have large slowly decaying spectrum.
  
  n_oversamples : int, default=10
      Number of oversamples for randomized SVD solver. Not used by ARPACK.
      See :func:`~sklearn.utils.extmath.randomized_svd` for a complete
      description.
  
      .. versionadded:: 1.1
  
  power_iteration_normalizer : {'auto', 'QR', 'LU', 'none'}, default='auto'
      Power iteration normalizer for randomized SVD solver.
      Not used by ARPACK. See :func:`~sklearn.utils.extmath.randomized_svd`
      for more details.
  
      .. versionadded:: 1.1
  
  random_state : int, RandomState instance or None, default=None
      Used during randomized svd. Pass an int for reproducible results across
      multiple function calls.
      See :term:`Glossary <random_state>`.
  
  tol : float, default=0.0
      Tolerance for ARPACK. 0 means machine precision. Ignored by randomized
      SVD solver.
  
  Attributes
  ----------
  components_ : ndarray of shape (n_components, n_features)
      The right singular vectors of the input data.
  
  explained_variance_ : ndarray of shape (n_components,)
      The variance of the training samples transformed by a projection to
      each component.
  
  explained_variance_ratio_ : ndarray of shape (n_components,)
      Percentage of variance explained by each of the selected components.
  
  singular_values_ : ndarray of shape (n_components,)
      The singular values corresponding to each of the selected components.
      The singular values are equal to the 2-norms of the ``n_components``
      variables in the lower-dimensional space.
  
  n_features_in_ : int
      Number of features seen during :term:`fit`.
  
      .. versionadded:: 0.24
  
  feature_names_in_ : ndarray of shape (`n_features_in_`,)
      Names of features seen during :term:`fit`. Defined only when `X`
      has feature names that are all strings.
  
      .. versionadded:: 1.0
  
  See Also
  --------
  DictionaryLearning : Find a dictionary that sparsely encodes data.
  FactorAnalysis : A simple linear generative model with
      Gaussian latent variables.
  IncrementalPCA : Incremental principal components analysis.
  KernelPCA : Kernel Principal component analysis.
  NMF : Non-Negative Matrix Factorization.
  PCA : Principal component analysis.
  
  Notes
  -----
  SVD suffers from a problem called "sign indeterminacy", which means the
  sign of the ``components_`` and the output from transform depend on the
  algorithm and random state. To work around this, fit instances of this
  class to data once, then keep the instance around to do transformations.
  
  References
  ----------
  :arxiv:`Halko, et al. (2009). "Finding structure with randomness:
  Stochastic algorithms for constructing approximate matrix decompositions"
  <0909.4061>`
  
  Examples
  --------
  >>> from sklearn.decomposition import TruncatedSVD
  >>> from scipy.sparse import csr_matrix
  >>> import numpy as np
  >>> np.random.seed(0)
  >>> X_dense = np.random.rand(100, 100)
  >>> X_dense[:, 2 * np.arange(50)] = 0
  >>> X = csr_matrix(X_dense)
  >>> svd = TruncatedSVD(n_components=5, n_iter=7, random_state=42)
  >>> svd.fit(X)
  TruncatedSVD(n_components=5, n_iter=7, random_state=42)
  >>> print(svd.explained_variance_ratio_)
  [0.0157... 0.0512... 0.0499... 0.0479... 0.0453...]
  >>> print(svd.explained_variance_ratio_.sum())
  0.2102...
  >>> print(svd.singular_values_)
  [35.2410...  4.5981...   4.5420...  4.4486...  4.3288...]
docstr: Fit model on training data X.
  
  Parameters
  ----------
  X : {array-like, sparse matrix} of shape (n_samples, n_features)
      Training data.
  
  y : Ignored
      Not used, present here for API consistency by convention.
  
  Returns
  -------
  self : object
      Returns the transformer object.
docstr_tok: ["Fit","model","on","training","data","X.","Parameters","--","--","--","--","--","X",":","{","array-like",",","sparse","matrix","}","of","shape","(","n_samples",",","n_features",")","Training","data",".","y",":","Ignored","Not","used",",","present","here","for","API","consistency","by","convention",".","Returns","--","--","--","-","self",":","object","Returns","the","transformer","object","."]
code: def fit(self, X, y=None):
      """Fit model on training data X.
  
      Parameters
      ----------
      X : {array-like, sparse matrix} of shape (n_samples, n_features)
          Training data.
  
      y : Ignored
          Not used, present here for API consistency by convention.
  
      Returns
      -------
      self : object
          Returns the transformer object.
      """
      self.fit_transform(X)
      return self
  
imports: from numbers import Integral, Real
  import numpy
  import scipy.sparse
  from scipy.sparse.linalg import svds
  from ..base import BaseEstimator, ClassNamePrefixFeaturesOutMixin, TransformerMixin, _fit_context
  from ..utils import check_array, check_random_state
  from ..utils._arpack import _init_arpack_v0
  from ..utils._param_validation import Interval, StrOptions
  from ..utils.extmath import randomized_svd, safe_sparse_dot, svd_flip
  from ..utils.sparsefuncs import mean_variance_axis
  from ..utils.validation import check_is_fitted
label_desc: Use image node_id 2 for calling the TruncatedSVD obj's underlying member method code with example usage: obj.fit(X, y) and returns: self