GH-43352: [Docs][Python] Add all tensor classes documentation

### Rationale for this change

We have added the missing documentation for various tensor classes in PyArrow, specifically `SparseCOOTensor`, `SparseCSRMatrix`, `SparseCSCMatrix`, and `SparseCSFTensor`. This documentation is necessary to provide users with reference information and examples on how to use these classes, as currently, there is no reference documentation or information in the PyArrow user guide regarding these tensor classes.

### What changes are included in this PR?

1. Added docstrings to the `SparseCSRMatrix`, `SparseCSCMatrix`, and `SparseCSFTensor` classes and their methods in the `tensor.pxi` file.
2. Updated the `tables.rst` file to include documentation for `SparseCSFTensor` along with examples.

### Are these changes tested?

No new tests are included in this PR as the changes are purely documentation updates. The existing tests for the tensor classes should cover the functionality.

### Are there any user-facing changes?

Yes, the user-facing change is the addition of documentation for the tensor classes, which will help users understand and utilize these classes more effectively. There are no breaking changes to public APIs.

docs/source/python/api/tables.rst

@@ -59,7 +59,92 @@ Dataframe Interchange Protocol
 Tensors
 -------
 
+PyArrow supports both dense and sparse tensors. Dense tensors store all data values explicitly, while sparse tensors represent only the non-zero elements and their locations, making them efficient for storage and computation.
+
+Dense Tensors
+-------------
+
 .. autosummary::
    :toctree: ../generated/
 
    Tensor
+
+Sparse Tensors
+--------------
+
+PyArrow supports the following sparse tensor formats:
+
+.. autosummary::
+   :toctree: ../generated/
+
+   SparseCOOTensor
+   SparseCSRMatrix
+   SparseCSCMatrix
+   SparseCSFTensor
+
+### SparseCOOTensor
+
+The `SparseCOOTensor` represents a sparse tensor in Coordinate (COO) format, where non-zero elements are stored as tuples of row and column indices.
+
+Example:
+.. code-block:: python
+
+   import pyarrow as pa
+
+   indices = pa.array([[0, 0], [1, 2]])
+   data = pa.array([1, 2])
+   shape = (2, 3)
+
+   tensor = pa.SparseCOOTensor(indices, data, shape)
+   print(tensor.to_dense())
+
+### SparseCSRMatrix
+
+The `SparseCSRMatrix` represents a sparse matrix in Compressed Sparse Row (CSR) format. This format is useful for matrix-vector multiplication.
+
+Example:
+.. code-block:: python
+
+   import pyarrow as pa
+
+   data = pa.array([1, 2, 3])
+   indptr = pa.array([0, 2, 3])
+   indices = pa.array([0, 2, 1])
+   shape = (2, 3)
+
+   sparse_matrix = pa.SparseCSRMatrix.from_numpy(data, indptr, indices, shape)
+   print(sparse_matrix)
+
+### SparseCSCMatrix
+
+The `SparseCSCMatrix` represents a sparse matrix in Compressed Sparse Column (CSC) format, where data is stored by columns.
+
+Example:
+.. code-block:: python
+
+   import pyarrow as pa
+
+   data = pa.array([1, 2, 3])
+   indptr = pa.array([0, 1, 3])
+   indices = pa.array([0, 1, 2])
+   shape = (3, 2)
+
+   sparse_matrix = pa.SparseCSCMatrix.from_numpy(data, indptr, indices, shape)
+   print(sparse_matrix)
+
+### SparseCSFTensor
+
+The `SparseCSFTensor` represents a sparse tensor in Compressed Sparse Fiber (CSF) format, which is a generalization of the CSR format for higher dimensions.
+
+Example:
+.. code-block:: python
+
+   import pyarrow as pa
+
+   data = pa.array([1, 2, 3])
+   indptr = [pa.array([0, 1, 3]), pa.array([0, 2, 3])]
+   indices = [pa.array([0, 1]), pa.array([0, 1, 2])]
+   shape = (2, 3, 2)
+
+   sparse_tensor = pa.SparseCSFTensor.from_numpy(data, indptr, indices, shape)
+   print(sparse_tensor)

python/pyarrow/tensor.pxi

@@ -595,7 +595,25 @@ shape: {0.shape}""".format(self)
 
 cdef class SparseCSRMatrix(_Weakrefable):
     """
-    A sparse CSR matrix.
+    SparseCSRMatrix represents a sparse matrix in Compressed Sparse Row (CSR) format.
+
+    Attributes:
+        indptr : array
+            Index pointer array.
+        indices : array
+            Column indices of the corresponding non-zero values.
+        shape : tuple
+            Shape of the matrix.
+        dim_names : list, optional
+            Names of the dimensions.
+
+    Example:
+        >>> import pyarrow as pa
+        >>> indptr = pa.array([0, 2, 3])
+        >>> indices = pa.array([0, 2, 1])
+        >>> shape = (2, 3)
+        >>> tensor = pa.SparseCSRMatrix(indptr, indices, shape)
+        >>> print(tensor)
     """
 
     def __init__(self):