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+import numpy as np
+
+# Y_DYTPE is the dtype to which the targets y are converted to. This is also
+# dtype for leaf values, gains, and sums of gradients / hessians. The gradients
+# and hessians arrays are stored as floats to avoid using too much memory.
+Y_DTYPE = np.float64
+X_DTYPE = np.float64
+X_BINNED_DTYPE = np.uint8  # hence max_bins == 256
+# dtype for gradients and hessians arrays
+G_H_DTYPE = np.float32
+X_BITSET_INNER_DTYPE = np.uint32
+
+# Note that we use Y_DTYPE=float64 to avoid issues with floating point precision when
+# summing gradients and hessians (both float32). Those are difficult to protect via
+# tools like (Kahan-) Neumaier summation as in CPython, see
+# https://github.com/python/cpython/issues/100425, or pairwise summation as numpy, see
+# https://github.com/numpy/numpy/pull/3685, due to the way histograms are summed
+# (number of additions per bin is not known in advance). See also comment in
+# _subtract_histograms.
+HISTOGRAM_DTYPE = np.dtype([
+    ('sum_gradients', Y_DTYPE),  # sum of sample gradients in bin
+    ('sum_hessians', Y_DTYPE),  # sum of sample hessians in bin
+    ('count', np.uint32),  # number of samples in bin
+])
+
+PREDICTOR_RECORD_DTYPE = np.dtype([
+    ('value', Y_DTYPE),
+    ('count', np.uint32),
+    ('feature_idx', np.intp),
+    ('num_threshold', X_DTYPE),
+    ('missing_go_to_left', np.uint8),
+    ('left', np.uint32),
+    ('right', np.uint32),
+    ('gain', Y_DTYPE),
+    ('depth', np.uint32),
+    ('is_leaf', np.uint8),
+    ('bin_threshold', X_BINNED_DTYPE),
+    ('is_categorical', np.uint8),
+    # The index of the corresponding bitsets in the Predictor's bitset arrays.
+    # Only used if is_categorical is True
+    ('bitset_idx', np.uint32)
+])
+
+ALMOST_INF = 1e300  # see LightGBM AvoidInf()